In AI We Trust

AI-First Mindset: The New Way of Solving Problems

Alex Velinov — Thu, 02 Apr 2026 05:41:46 GMT

TLDR

Most organisations are using AI but thinking exactly the same way they always have. An AI-First Mindset is not about tools. It is about rewiring how you approach every problem, challenge every assumption, and question every workflow that has existed in your business since before ChatGPT was a thing. This article covers what it means, the two questions that change everything, six core principles, seven practical actions you can take this week, and answers to the most common questions leaders ask when they first encounter this idea.

The phrase that is quietly killing your business

There is a phrase you have probably heard in your organisation. Possibly said yourself. It goes something like this:

“That’s how we do it here.”

Sometimes it comes dressed up as process. Sometimes as policy. Sometimes it sounds perfectly reasonable. “We’ve always handled client queries within 24 hours.” Fair enough. Except when AI exists, 24 hours is a choice, not a constraint.

Here is a better question. What would we need to do to answer in one minute?

That is a completely different problem. It needs a completely different solution. And it takes a completely different way of thinking to even ask it in the first place.

This is what an AI-First Mindset actually does. It does not just make you faster at existing tasks. It makes you question whether those tasks should exist at all, and whether the standards you have built your business around still make any sense. It challenges traditional workflows, inherited processes, and the quiet assumption that because something has always worked a certain way, it should keep working that way.

It also forces a more fundamental rethink for service businesses. Growth has historically meant headcount. More clients meant more people. An AI-First Mindset challenges that relationship directly. It asks whether productivity and capacity can grow without the team growing at the same rate. That is a different kind of business model, and it starts with a different kind of thinking.

What is an AI-First Mindset?

Here is a clean definition from strategist Tim Hillegonds:

“AI First is an organizational mindset infused with curiosity, proactivity, and experimentation, aimed at embracing AI’s potential to drive innovation, elevate thinking, enhance efficiency, and unlock new opportunities for growth.”

Here is how I think about it in practice.

An AI-First Mindset is your operating system. The tools, ChatGPT, Copilot, Claude, whatever you are using, are just apps running on top of it. You can install every app in existence. If the operating system is broken, nothing works properly.

Most organisations are spending thousands on apps and nothing on the operating system.

There is a number that makes this real. According to McKinsey’s 2025 research, 88% of organisations now use AI in at least one business function. Only 7% have meaningfully scaled it across the enterprise. That gap between 88 and 7 is not a technology problem. It is a thinking problem.

Two questions that change everything

Before you start any task, any meeting, any project, any piece of work, ask yourself:

“Can I get AI to do this for me?”

If the answer is no, ask:

“Can I get AI to help me with this?”

That is it. Two questions. They sound simple. They are not.

The moment those questions become automatic, the moment you ask them before you open a blank document, before you pick up the phone, before you call a team meeting, you have crossed the line. That is the AI-First Mindset in practice.

Most people never ask them. They open the document and start typing. They use AI as an afterthought, not a starting point. The mindset shift is in the order of operations. Ask first. Then act.

6 core principles

There are six principles that underpin an AI-First Mindset. They split into two groups and the distinction matters.

The Mindset: the cultural side

Curiosity and experimentation. This is the prerequisite. You cannot build an AI-first culture with people who are not willing to try things, break things, and learn from both. Curiosity is not a personality trait you either have or lack. It is a practice. It needs time, permission, and safety to develop.

Human-AI collaboration. Humans stay in the loop. AI handles volume. Humans handle judgment, nuance, and trust. These are not the same job and they should not be treated as interchangeable. The goal is not to remove humans. It is to elevate what humans do.

Start with the problem, not the technology. Do not ask what can we do with AI. Ask what problem are we trying to solve, then bring AI to it. This is the most counterintuitive principle and the most important one. Technology-first thinking produces solutions looking for problems. Problem-first thinking produces results.

The Discipline: the structural side

Leadership models it visibly. Not in policy documents. In daily behaviour. What leaders do, teams copy. What leaders ignore, teams deprioritise. If you use AI privately but present polished outputs publicly, your team learns nothing useful.

Continuous learning. Not a one-time workshop in January. An ongoing commitment to building capability month by month. The tools are changing fast. The people using them need to keep pace.

Ethical governance. Bias prevention, transparency, and privacy built in from the start. Not bolted on after something goes wrong. This is not a compliance box to tick. It is a foundation that makes everything else sustainable.

7 actions to embrace an AI-First Mindset

These are practical. You can start this week.

01. Go first. Visibly.

Show your team how you use AI. Share your prompts. Talk about what worked and what did not. Research from Atlassian found that a single leader demonstration nearly doubled AI usage on teams. One demo. Doubled adoption. That is the highest-leverage thing a leader can do and it costs nothing except a bit of vulnerability.

02. Create dedicated space for experimentation.

You cannot build an AI-first culture in the gaps between meetings. Block time. Name the sessions. Protect them from being cancelled for “real work.” Because this is the real work. BCG’s research is clear: 30 to 50% efficiency gains only come when workflows are actively reengineered. That requires dedicated time with permission to fail.

03. Invest in training like it is infrastructure.

A 30-minute lunch and learn is not an AI strategy. The human factor, how much a company invests in its people’s AI capability, consistently outweighs every structural factor when it comes to AI maturity. Walmart committed nearly a billion dollars to AI workforce skills. Amazon trained 31 million learners. You do not need those numbers. But you do need to treat training as seriously as you treat your tech stack.

04. Build an AI Champions Network.

Every organisation already has people who are experimenting quietly. The person who automated their reporting. The marketer who built a workflow that saves the team four hours a week. They exist. Find them. Give them visibility and status. Let peer-to-peer influence do the heavy lifting. Top-down mandates create compliance. Champions create culture. Goldman Sachs runs internal Shark Tank-style AI competitions. Shopify publishes an internal leaderboard. Both work on the same principle: make the enthusiasts famous internally.

05. Make the wins visible and repeatable.

A dedicated Slack channel for AI wins. A monthly 20-minute show-and-tell. A shared prompt library anyone can add to. These cost nothing and compound quickly. Nothing accelerates adoption faster than watching a colleague solve a problem you also have. Strategy creates permission. Use cases create momentum.

06. Build psychological safety.

Here is a stat that should make every leader pause. In a 2025 survey of 1,600 knowledge workers, 41% of Millennial and Gen Z employees admitted to actively sabotaging their company’s AI strategy. Not resisting. Sabotaging. These are digital natives who were never given a reason to trust the change.

Fear of obsolescence, fear of irrelevance, and the anxiety of not knowing what your role looks like in two years are real. When leaders respond to those fears with training schedules and tool licences, the fear does not go away. It goes underground. And underground, it becomes resistance.

Name the fear. Have the conversation. You cannot mandate your way to an AI-first culture.

07. Tie AI competency to performance reviews.

Shopify, Microsoft, and Accenture all did this. When AI is in the review, it stops being optional. The review is the structural anchor. Culture is what makes it real. One important note: do not do this step first. Do it after the other six. In an environment of poor training and unaddressed fear, tying AI to reviews produces compliance theatre. People find ways to look like they are using AI without changing anything. The structure only works when the culture is ready for it.

Common Questions

What are the best first steps for creating an encouraging AI environment for your team?

Start with yourself. Go first before you ask anyone else to. Then pick one problem your team genuinely finds painful, something that eats time, kills morale, or slows things down, and solve it publicly with AI. Show the before and after. Make the win visible. One concrete example does more than ten strategy documents. People do not change behaviour because of announcements. They change behaviour because they see someone they trust doing something that looks useful.

How do you start adopting AI in your business without overwhelming the team?

Narrow the scope. Do not launch a company-wide AI programme on day one. Pick one team, one workflow, one use case. Get a win. Share it. Let the momentum build naturally. The biggest mistake leaders make is trying to transform everything at once. You end up with confused people and no clear progress. Start small, make it real, then expand. One good use case, well executed and openly shared, is worth more than a hundred slides about AI strategy.

Is there a way to measure whether an AI-First Mindset is actually working?

Yes. A useful benchmark: if your organisation is not seeing at least two to four hours of AI-driven productivity per worker per week, you have not reached meaningful adoption yet. Start tracking time saved, use cases developed, and new capabilities unlocked. Not how many people completed the training module. Completion rates measure activity. Productivity metrics measure impact. One thing worth tracking early is whether people are coming to you with AI use cases rather than waiting to be told what to use AI for. That shift in direction is one of the clearest signs the mindset is taking hold.

Final words

The phrase “that’s how we do it” built most successful businesses. It codified what worked and protected it from being changed for no good reason.

But that same phrase is now the thing standing between most organisations and a step change in what they are capable of.

An AI-First Mindset does not ask you to abandon what works. It asks you to question whether it still does. Whether the standards you set before AI existed are still the right standards now. Whether the workflows your team follows every day are genuinely the best way to do things, or just the way they have always been done.

The client query that used to take a day. Could it take a minute?

The team you needed to grow in order to grow the business. What if that relationship no longer has to hold?

These are different questions. They need a different way of thinking to answer them.

That is what an AI-First Mindset is for.

25 AI Prompt Hacks for Work That Will Make You More Productive

Alex Velinov — Thu, 26 Mar 2026 06:45:41 GMT

TLDR

Most people are getting mediocre results from AI, not because the tools are bad, but because the prompts are vague. This is a ranked list of 25 prompting techniques that genuinely move the needle on day-to-day productivity, from prompt chaining and mega-prompts at the top to accountability coaching and gap analysis further down. Each one comes with a ready-to-use example prompt. No fluff, no “just be specific” platitudes. These are the techniques that separate people who use AI from people who get real leverage from it.

Prompting is How we Communicate with AI

Here’s something that gets overlooked in almost every conversation about AI: prompting isn’t a technical skill. It’s a communication skill.

Think about it. The same things that make you effective in a meeting - being clear about what you want, giving enough context, specifying who you’re talking to, asking good questions before jumping to solutions are exactly what make you effective with AI. Every principle of good human-to-human communication applies to human-to-machine communication. Clarity. Structure. Context. Audience awareness. Specificity. The model doesn’t reward you for being vague, just like your colleagues don’t.

And yet... most of the “prompting advice” out there boils down to “be more specific” or “add context.” Which is a bit like telling someone to “communicate better” in a presentation skills workshop. Technically true. Practically useless.

The reason most people get mediocre results from AI isn’t technology. It’s the same reason most emails get ignored, most briefs get misinterpreted, and most meetings end without clarity - the input wasn’t good enough. The difference is that AI will never push back and say “I don’t understand what you’re asking for.” It’ll just... give you its best guess. And its best guess based on a vague prompt is a vague answer.

So here’s something more useful than “be specific”: 25 prompting techniques, ranked by impact, each with a concrete example you can steal, adapt, and use today. These work across ChatGPT, Claude, Gemini - whatever you’re using. The principles are universal because they’re not really about AI at all. They’re about communication. The AI part is just the medium.

25 Hacks

1. The Prompt Chain

Impact: Transformative

This is the single biggest unlock for most people. Instead of cramming everything into one enormous prompt and hoping for the best, you break the work into a sequence, where each output feeds the next.

Think of it like cooking. You wouldn’t throw every ingredient into a pot simultaneously and expect a Michelin-star meal. You prep, you layer, you build. Same thing here.

Most people overload a single prompt and get mediocre results. Chaining gets you compound quality.

Example (Step 1 of 3):

“Analyse this raw customer feedback data and identify the top 5 recurring themes. For each theme, note frequency and sentiment. Output as a numbered list.”

Then follow with:

“Take theme #1 from your analysis and draft a detailed action plan with owner, timeline, and success metric.”

Then:

“Convert that action plan into a one-page executive summary suitable for a board slide.”

Three prompts. Three minutes. Output that would have taken an afternoon.

2. The Mega-Prompt

Impact: Transformative

The opposite of chaining — and equally powerful in the right context. Here, you front-load everything into one structured request: role, context, task, constraints, format, examples. All of it.

This works best when you want a single comprehensive output and you already know exactly what “good” looks like. It’s also ideal for building reusable templates. Write the mega-prompt once, swap out the variables, and run it every week.

Example:

“You are a senior content strategist at a B2B SaaS company. I need a 90-day content calendar for our blog. Context: We sell project management software to mid-market teams. Our three content pillars are remote work productivity, agile methodology, and team collaboration. Constraints: 3 posts per week, mix of how-to guides, thought leadership, and case study roundups. Format: Table with columns for Week, Title, Pillar, Target Keyword, CTA, and Distribution Channel. Tone: Authoritative but approachable. Avoid jargon.”

One prompt. One shot. A deliverable you can actually hand to someone.

3. Role Assignment with Specificity

Impact: High

“Act as a marketing expert” does almost nothing. The research actually backs this up - vague personas barely shift output quality. But detailed, domain-specific ones? Different story entirely.

The trick is specificity. Don’t just assign a job title. Assign years of experience, industry context, methodology, and crucially, the audience the persona is speaking to. The more precise the identity, the more the model narrows its enormous probability space into something genuinely useful.

Example:

“You are a CFO with 15 years of experience in private equity-backed SaaS companies. You specialise in unit economics and have presented to institutional LPs. Review the following P&L and identify the three metrics a prospective investor would scrutinise first. Explain your reasoning as if briefing your CEO before a board meeting.”

The difference between “act as a finance expert” and the above is the difference between a Wikipedia summary and actual advice.

4. Chain-of-Thought Reasoning

Impact: High

Five extra words. Massive difference in output quality.

Adding “think step by step” — or any instruction that forces the model to show its reasoning before giving an answer — dramatically improves accuracy on anything involving logic, analysis, or multi-step decisions. This is one of the most well-researched techniques in prompt engineering, and it works across every major model.

Example:

“A client has a marketing budget of £50,000 per quarter. They want to allocate across paid search, paid social, and content marketing to maximise qualified leads for a B2B event services company. Think step by step: first analyse the typical cost-per-lead for each channel in this industry, then recommend an allocation with rationale, then identify the biggest risk in your recommendation.”

You’re not just getting an answer anymore. You’re getting the reasoning, which means you can spot where the logic is weak and course-correct.

5. The “Before You Answer” Constraint

Impact: High

This one hack eliminates the most common prompting failure: insufficient context.

Instead of accepting whatever the AI generates on the first pass, you instruct it to ask you questions before responding. It flips the dynamic from vending machine to consultant. And it surfaces context you didn’t even realise you were withholding.

Example:

“I need help writing a proposal for a new client. Before you draft anything, ask me up to 5 questions that will help you write a proposal that sounds like it came from someone who deeply understands their business.”

Suddenly you’re in a dialogue, not a monologue. The output quality jumps because the input quality jumped first.

6. Output Format Specification

Impact: High

This is absurdly simple and absurdly underused. Just... tell the AI what shape the answer should take. Table. Email. Slack message. Executive summary. Numbered list. JSON. Whatever.

Most people describe what they want but forget to describe how they want it delivered. The format specification alone can turn a rambling paragraph into something you paste directly into your workflow.

Example:

“Compare the pros and cons of Notion, Monday.com, and Asana for a 30-person marketing agency. Format your answer as a comparison table with rows for: Pricing, Ease of onboarding, Client collaboration features, Reporting, Integrations, and Best suited for. Add a one-sentence verdict at the bottom.”

Same information. Completely different usability.

7. Few-Shot Prompting

Impact: High

Showing is better than telling. Always has been and that applies to AI too.

Providing one or two examples of what “good” looks like is far more effective than describing it in abstract terms. This is especially powerful for writing tasks where tone, style, and structure matter. You’re essentially saying “do more like this” instead of hoping the model interprets your adjectives correctly.

Example:

“Write me LinkedIn post hooks for our new AI event activation product. Match the style of these examples:
Example 1: ‘We gave 500 conference attendees a camera, an AI model, and 30 seconds. What happened next changed how we think about live events.’
Example 2: ‘Your attendees are already bored of photo booths. Here’s what comes next.’
Now write 5 more hooks in this style for a product that turns selfies into AI-animated dancing videos at live events.”

You went from “write something catchy” to “write something that sounds like us.” Night and day.

8. The Iterative Refinement Loop

Impact: High

Here’s the uncomfortable truth: the first output is almost never the best output. But most people accept it and move on.

Power users treat the first response as a draft — a starting point, not a deliverable. They refine through follow-up prompts, just like an editor would mark up a first draft. This mimics the creative process that produces good work in any medium: write, review, revise, repeat.

Example (after receiving a first draft):

“Good start. Now revise with these changes: (1) Make the opening paragraph more direct — cut the throat-clearing. (2) Add a specific data point to support the second argument. (3) Tighten the conclusion to a single sentence call to action. (4) Reduce overall length by 30%.”

The prompt took 20 seconds to write. The improvement in output quality? Significant.

9. Audience Specification

Impact: High

The same topic explained to a C-suite executive, a junior developer, and a non-technical client requires completely different language, depth, and framing. But if you don’t tell the AI who the reader is… it defaults to writing for no one in particular. Which is exactly what it sounds like.

Example:

“Explain what retrieval-augmented generation (RAG) is and why it matters for enterprise AI. Write this for a non-technical marketing director who needs to brief their CEO. Avoid acronyms. Use a business analogy to make the concept click. Keep it under 200 words.”

Same topic. Radically different output. All because you told it to who’s reading.

10. The Reverse Prompt

Impact: High

When you don’t know how to frame a complex task, which, let’s be honest, happens more often than anyone admits, ask the AI to write the prompt for you.

This is a meta-hack. It teaches you better prompting while immediately improving your results. And it’s surprisingly effective because the model understands its own processing patterns better than you do.

Example:

“I want to use AI to help me prepare for a difficult salary negotiation with my manager. What would be the ideal prompt I should give you to get the most useful coaching? Write the prompt for me, then I’ll paste it back to you.”

Think of it as asking the chef what to order. They know the kitchen better than you do.

11. Constraint Injection

Impact: Medium-High

This one’s counterintuitive: more constraints usually produce better results. Word limits, forbidden words, required inclusions, structural rules. They all force the model to work harder and be more deliberate about its choices.

It’s the creative brief principle. “Write me something” is paralysing. “Write me 100 words, no jargon, ending with a question” is liberating.

Example:

“Write a cold outreach email to the VP of Marketing at a mid-size events company. Constraints: Maximum 100 words. No more than 3 sentences per paragraph. Must include one specific observation about their company. Must end with a single, low-friction call to action. Do not use the words ‘synergy,’ ‘leverage,’ or ‘circle back.’”

The constraints don’t limit the output. They focus on it.

12. The “Critique Then Improve” Loop

Impact: Medium-High

Ask the AI to critique its own work, then revise based on its own feedback. Two steps, one conversation, and you get something markedly better than the first pass.

This simulates having a second pair of eyes on your work, except the second pair of eyes is available instantly, at no cost, at 11pm on a Sunday.

Example (after receiving an output):

“Now critique your response as if you were a senior editor. Identify the three weakest points, any unsupported claims, and any sections where the reader might lose interest. Then produce a revised version that addresses every issue you flagged.”

It’s remarkable how good AI is at spotting flaws in its own work when you simply... ask it to look.

13. The RTFD Framework

Impact: Medium-High

If you remember nothing else from this article, remember RTFD: Role, Task, Format, Details. It’s a reliable scaffolding for any business prompt and ensures you never forget the key ingredients.

Not every prompt needs to be a work of art. Sometimes you just need a formula that works consistently.

Example:

“Role: You are a senior HR business partner. Task: Draft an internal announcement about our company’s new flexible working policy. Format: Email, under 250 words, with a subject line. Details: The policy starts 1 May. Employees can work from home up to 3 days per week. It applies to all UK-based staff. Tone should be warm but clear on expectations.”

Four components. Reliable output. Every time.

14. Conditional Logic Prompting

Impact: Medium-High

Give the AI decision rules to follow, and its output adapts based on what it receives. This is where prompting starts to feel less like chatting and more like programming — in the best way.

Especially useful for repeatable processes like lead qualification, support triage, or content categorisation.

Example:

“I’m going to paste customer support tickets one at a time. For each ticket, classify it as Billing, Technical, or Feature Request. If Billing: draft a 2-sentence response with a link to our payment FAQ. If Technical: ask one clarifying question before suggesting a fix. If Feature Request: log it in a table with columns for Date, Feature Description, and Customer Tier.”

You’ve just built a basic AI workflow. No code, no automation platform, just a well-structured prompt.

15. The “Explain My Options” Prompt

Impact: Medium

Instead of asking AI to make a decision for you — which it shouldn’t, and you probably shouldn’t let it. Ask it to lay out your options with tradeoffs. You keep the judgment. It does the analysis.

Example:

“I need to choose a CRM for a 30-person agency. My shortlist is HubSpot, Salesforce, and Pipedrive. For each, give me: (1) The strongest argument for choosing it, (2) The biggest risk or downside, (3) The type of company it’s best suited for, (4) Estimated total cost for 30 users per year. Then tell me which questions I should be asking myself to make this decision.”

Notice the last line. You’re not asking it to choose. You’re asking it to help you choose better.

16. Persona Flipping

Impact: Medium

Ask the AI to answer the same question from two or more opposing viewpoints. Excellent for stress-testing strategies, preparing for pushback, or finding the holes in your own thinking.

It’s essentially a debate simulation, except both debaters are available on demand and neither gets offended.

Example:

“I want to pitch my CEO on investing £100,000 in AI tooling for the agency this year. First, write the strongest case FOR the investment as if you are the VP of Technology. Then write the strongest case AGAINST it as if you are the CFO who is sceptical of unproven ROI. Then suggest how the VP should address the CFO’s objections.”

Three perspectives. One prompt. Much better preparation than rehearsing your pitch alone in the shower.

17. The “Teach Me Like I Am” Prompt

Impact: Medium

Specify your current knowledge level and the AI calibrates its explanation accordingly. This eliminates the too-basic or too-advanced problem that makes most AI explanations feel either patronising or bewildering.

Example:

“I understand basic SEO - keywords, meta descriptions, internal linking, but I’ve never worked with structured data or schema markup. Explain schema markup to me as a natural next step from what I already know. Include one practical example I could implement on a WordPress site today.”

You’re not starting from zero. You’re not pretending to be an expert. You’re telling the AI exactly where you are so it meets you there.

18. Summarise Then Extract

Impact: Medium

When dealing with long documents, meeting transcripts, or data dumps, a two-phase approach beats a one-shot request every time. First: summarise. Then: extract specific action items or insights.

This prevents the AI from drowning in detail and gives you both the forest and the trees.

Example:

“Here is the transcript of our 60-minute strategy meeting. First, provide a 5-sentence summary of the key discussion points. Then extract: (1) All decisions that were made, (2) All action items with the person responsible and deadline, (3) Any unresolved questions that need follow-up.”

Sixty minutes of conversation, distilled in under a minute. That’s the promise of AI productivity — when the prompt is right.

19. Template Generation

Impact: Medium

Instead of writing one-off prompts, ask the AI to create reusable templates with placeholder fields you can fill in repeatedly. This turns a single prompting session into a long-term productivity system.

One good template, reused 50 times? That’s 50 tasks where you didn’t have to think about the prompt at all.

Example:

“Create a reusable prompt template I can use every Friday to generate a weekly client report. The template should have placeholder fields I can fill in for: project name, key accomplishments this week, blockers, next week’s priorities, and budget status. The output should be a professional email I can send directly to the client.”

Build the template once. Use it forever. That’s leverage.

20. The Rubber Duck Prompt

Impact: Medium

Named after the old programming trick of explaining your code to a rubber duck to find bugs — except this duck talks back and asks good questions.

Use the AI as a sounding board for half-formed ideas. Describe your messy, unfinished thinking and ask it to help you organise, challenge, or build on it. This replaces the “I just need to talk this through with someone” moment, which usually requires finding a willing colleague and an empty meeting room.

Example:

“I’m thinking about pivoting our agency’s positioning from ‘full-service digital marketing’ to ‘AI-powered event marketing.’ I haven’t fully thought this through yet. Here’s what’s in my head: [paste your rough notes]. Help me organise these thoughts into a coherent argument. Challenge any assumptions that seem weak. Then suggest the three things I should validate before making this decision.”

Half-baked idea in. Structured thinking out.

21. The Pre-Mortem

Impact: Medium

Before launching anything, ask the AI to imagine it’s already failed and then work backwards to identify why. This is a well-known strategic exercise, and AI is exceptionally good at it because it has no optimism bias.

Example:

“We’re about to launch a new AI chatbot product for live events. Imagine it’s 6 months from now and the launch has failed. Write a post-mortem analysis identifying the 5 most likely reasons it didn’t succeed. For each reason, suggest one preventive action we could take now.”

Your team will thank you for catching the blind spots before they became expensive lessons.

22. The Tone Transplant

Impact: Medium

Describing a tone in the abstract — “professional but friendly” — is remarkably imprecise. Instead, paste a sample of the writing style you want to match and ask the AI to analyse it before writing in that voice.

Example:

“Here’s a sample of our founder’s writing style from a recent LinkedIn post: [paste sample]. Analyse the key characteristics of this writing: sentence length, vocabulary level, use of stories, rhetorical devices, and overall tone. Then write a new 300-word LinkedIn post about AI at live events in exactly this style.”

You’ve gone from “sound like us” which means nothing to an AI to “here’s precisely what ‘us’ sounds like.”

23. Data Structuring

Impact: Medium

Paste unstructured data, messy notes, or raw exports and ask the AI to clean and restructure them. This is one of those tasks that takes humans an unreasonable amount of time and takes AI... seconds.

Example:

“Below is a raw export of 50 leads from our event last week. The data is messy - inconsistent formatting, missing fields, names in various cases. Clean this data into a structured table with columns for: Full Name (title case), Email, Company, Job Title, and Lead Score (assign Hot/Warm/Cold based on job title seniority). Flag any rows with missing critical data.”

Hours of spreadsheet work. One prompt. Done.

24. The “What Am I Missing” Prompt

Impact: Medium

After completing your own work, paste it in and ask the AI to find the gaps. This is a fast, free second opinion on any plan, document, or strategy — and it catches things your brain glosses over because it already knows what it intended to write.

Example:

“Here’s my marketing plan for Q3. Review it and tell me: (1) What important elements am I missing? (2) What assumptions am I making that I should test? (3) What would a competitor do to undercut this plan? (4) What’s the single biggest risk I haven’t accounted for?”

Not “is this good?” — that’s a useless question. “What did I miss?” — that’s the one that improves your work.

25. The Accountability Prompt

Impact: Situational but Powerful

Use the AI to create structure and accountability around your goals. This turns a chat interface into a lightweight coaching and project management tool, especially useful for solo operators or anyone who doesn’t have a team looking over their shoulder.

Example:

“I want to publish 12 LinkedIn posts this month to build my personal brand around AI in events marketing. Act as my content accountability coach. First, create a 4-week posting schedule (3 per week) with suggested topic angles. Then, every time I return to this conversation, ask me which posts I’ve completed, give me feedback on what I share, and suggest improvements for the next batch.”

It’s not going to replace a real coach. But it’s available at 6am and it never cancels on you.

Final Words

Here’s the thing about all 25 of these techniques: not a single one requires any technical skill. No coding. No API access. No special tools. Just a better understanding of how to communicate with intention — which, if you think about it, is the same skill that makes people effective in every other professional context.

We’ve spent decades refining how we communicate with other humans. How to write a clear brief. How to give feedback that actually lands. How to run a meeting that ends with decisions instead of confusion. How to ask questions that surface the real problem instead of the surface-level symptom. Prompting is just that — applied to a different kind of collaborator.

The gap between “AI is useless” and “AI is transformative” is almost always a communication gap. The people getting extraordinary results aren’t using a different tool. They’re using the same tool with more intentional input. They’re doing what good communicators have always done: being clear about what they want, providing the context someone needs to help them, and iterating instead of hoping the first attempt is perfect.

And if this list feels overwhelming, don’t try to master all 25 at once. Pick three. The Prompt Chain (#1), the “Before You Answer” Constraint (#5), and the Critique Then Improve Loop (#12) would be a strong starting trio. Get those into your daily workflow. The rest will follow naturally.

The AI doesn’t need to get smarter. The prompts do. And improving your prompts is really just improving how you communicate, which unlike the models themselves, is entirely in your hands.

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Scotland's AI Strategy 2026–2031: 50 Questions Answered

Alex Velinov — Wed, 25 Mar 2026 07:06:54 GMT

In March 2026, the Scottish Government published its most ambitious technology policy document to date — a five-year national AI strategy that maps out how Scotland plans to become a globally competitive AI nation while keeping ethics, sustainability, and inclusivity at its core.

The strategy covers everything from a potential £23 billion annual GDP boost by 2035 to renewable-powered data centres, a new national AI programme called “AI Scotland,” and sector-specific plans spanning healthcare, financial services, semiconductors, and creative industries. It was shaped by input from over 100 experts, business leaders, academics, and third-sector organisations.

Whether you’re a business leader weighing up AI investment, a policymaker tracking regulatory signals, a researcher looking for funding and collaboration cues, or simply someone who wants to understand how AI will shape Scotland’s economy and public services over the next five years — this FAQ breaks down the full 58-page document into 50 clear, direct answers.

TL;DR

Scotland’s AI Strategy 2026–2031 is built around an eight-layer “AI Stack” framework and sets out ten priority actions to be delivered by March 2027. The headline numbers: AI could add £23 billion in annual GDP by 2035, yet only 30.7% of Scottish businesses currently use AI. The strategy launches “AI Scotland” as a national coordination programme, backs major data centre and compute infrastructure investment — including a £15 billion AI Pathfinder project in North Ayrshire and over £8 billion in the Lanarkshire AI Growth Zone — establishes a Future Jobs Panel and AI Leadership Academy for workforce readiness, and pushes for UK-level regulation aligned with the EU AI Act. Six priority sectors are targeted — healthcare, advanced manufacturing, financial services, renewable energy, space tech, and creative industries. Sustainability is a major theme, with Scotland positioning its record-breaking 38.4 TWh of renewable electricity generation as the backbone of a “Scottish Green Compute” proposition. Public trust will be addressed through a nationwide engagement programme, and non-digital routes to public services will remain available.

General Overview

1. What is Scotland’s AI Strategy 2026–2031?

Scotland’s AI Strategy 2026–2031 is a national policy document published by the Scottish Government in March 2026. Its purpose is to harness the potential of artificial intelligence to drive responsible and inclusive economic growth across Scotland and make a positive difference at every level of society. The strategy covers a five-year period and is structured around an “AI Stack” model with eight interconnected layers.

2. Who published Scotland’s AI Strategy?

The strategy was published by the Scottish Government. The foreword was jointly written by Kate Forbes MSP (Deputy First Minister and Cabinet Secretary for Economy and Gaelic) and Richard Lochhead MSP (Minister for Business and Employment).

3. What is the main purpose of the strategy?

The stated purpose is: to harness the potential of AI to drive responsible and inclusive growth across Scotland’s economy and make a positive difference at every level of society. It demands an ethical approach to AI development and application, with a focus on improving lives, transforming productivity, stimulating economic opportunity, and improving the quality and efficiency of public services.

4. What time period does the strategy cover?

The strategy runs from 2026 to 2031. It will be delivered in three phases: Phase 1 actions are outlined in this document, Phase 2 updates will be published in 2027, and Phase 3 updates in 2029.

5. How was the strategy developed?

The strategy was developed through widespread engagement across Scotland, including consultations with businesses, Industry Leadership Groups, universities and colleges, innovation centres, and enterprise and skills agencies. Over 100 experts, business leaders, academics, and third-sector organisations participated in consultation workshops. The work was guided by the AI Sub-group of the Scottish Technology Council.

The AI Stack Framework

6. What is the “AI Stack”?

The AI Stack is the organising framework at the heart of the strategy. It describes eight non-hierarchical layers that represent the full AI ecosystem and must work together to deliver social and economic good. The layers are: Users, Adoption and Skills, Companies and Products, Innovation Research and Development, Data Centres and Infrastructure, Semiconductors, Data, and Regulation.

7. Are the layers of the AI Stack ranked by importance?

No. The position of each layer does not reflect its value or importance. All layers have interdependencies and interact with all other layers. Data and Regulation are shown as “encircling” layers to illustrate that they sit around, as well as within, all other layers.

8. How does the AI Stack relate to the strategy’s outcomes?

Each layer of the AI Stack has its own case for change, specific actions, and defined outcomes to be achieved by 2031. If all actions and outcomes are delivered across every layer, the strategy’s overall purpose will be achieved.

Key Actions and Delivery

9. What are the ten key actions to be completed by March 2027?

The ten priority actions are:

(1) Position AI Scotland as the national flagship programme,

(2) Appoint AI Industry Champions with an Expert Advisory Board,

(3) Launch a nationwide engagement programme for public trust,

(4) Implement a framework for safe AI use in health and social care,

(5) Roll out a revitalised national AI adoption programme for SMEs including an AI Leadership Academy,

(6) Establish a Future Jobs Panel,

(7) Pilot an AI Scale-up Accelerator,

(8) Launch an innovation programme applying AI to public services,

(9) Promote Scotland as a green data centre hub and maximise the Lanarkshire AI Growth Zone,

(10) Launch a data matchmaking pilot for public-sector datasets.

10. What is AI Scotland?

AI Scotland is a new national transformation programme led by the Scottish Government and a consortium of partners, including The Data Lab, ScotlandIS, and Enterprise agencies. It will coordinate and amplify Scotland’s collective efforts to implement the strategy’s actions, working across business, academia, and the public sector.

11. What organisational form will AI Scotland take long-term?

In the first year, an Expert Advisory Board will advise on the development of a comprehensive business case for AI Scotland’s long-term organisational model. Potential models under consideration include a cluster management organisation (CMO) or a non-profit company (NPC).

12. What is the role of the Expert Advisory Board?

The Expert Advisory Board will evaluate AI Scotland’s activities, provide strategic advice, and guide the development of future programmes and interventions. Membership will include AI champions from key sectors and regions, alongside business leaders and technical experts.

Economic Impact

13. What is the estimated economic value of AI for Scotland?

GC Insight has estimated that, with the right investment and leadership, AI could generate more than an additional £23 billion in annual GDP by 2035, with a potential cumulative additional GDP of £140.75 billion over the period 2025 to 2035.

14. How many AI-focused companies are there in Scotland?

Independent assessments indicate that Scotland is home to an estimated 296 AI-focused companies, spanning emerging start-ups, scale-ups, research institutions, innovation centres, and specialist technical consultancies.

15. What is the current level of AI adoption among Scottish businesses?

As of March 2025, 61.9% of Scottish SMEs reported they are not using AI technologies, according to the Business Insights and Conditions Survey (BICS). Only 30.7% of businesses across Scotland currently use AI.

16. What is the Lanarkshire AI Growth Zone?

Scotland’s first AI Growth Zone in North Lanarkshire is backed by over £8 billion of private investment and designed in partnership with DataVita and CoreWeave. It is set to deliver more than 3,400 new jobs, including 800 high-value AI and digital infrastructure roles, and will include a community fund to support local programmes over the next 15 years.

Skills and Workforce

17. How many AI job postings were there in Scotland recently?

Between July 2024 and June 2025, there were 5,700 job postings requiring at least one AI skill across Scotland.

18. What is the Future Jobs Panel?

The Future Jobs Panel will be established to assess AI’s workforce impact and guide national skills planning. It will help ensure Scotland’s workforce can navigate the opportunities and changes AI brings.

19. What is the AI Leadership Academy?

A new AI Leadership Academy will be piloted for leaders of Scottish SMEs as part of the revitalised national AI adoption programme. It aims to build leadership capacity to understand and respond to the potential of AI and the need for change.

20. How does the strategy address skills shortages?

The strategy addresses skills shortages through expanded modular AI literacy training, a standardised AI readiness tool for SMEs and public bodies, a Future Jobs Panel, the AI Leadership Academy, and alignment of skills investment with strategic national and regional needs. Fair Work principles are embedded throughout.

21. What is the SME AI Adoption Programme?

The SME AI Adoption Programme was a £1 million initiative launched in the Programme for Government 2025–26, delivered in collaboration with Scotland’s enterprise agencies and The Data Lab. More than 500 SMEs engaged with it, over 80 firms identified AI use cases, 120+ senior leaders participated in leadership development, and 160+ companies received hands-on assistance.

22. How does the strategy incorporate Fair Work principles?

The strategy embeds Scotland’s Fair Work principles throughout, ensuring that AI adoption involves workers, promotes upskilling, and supports improved job quality and security. All workforce changes driven by AI must align with these principles.

Research and Innovation

23. What is Scotland’s standing in AI research?

Scotland is recognised as a global leader in AI research. Five Scottish universities were placed in the UK’s top 30 for AI research output in 2025. The country hosts ARCHER2 (the UK’s national supercomputer) and will host the new £750 million UK National Supercomputing Centre at the University of Edinburgh.

24. Who are Scotland’s AI pioneers?

The strategy highlights five notable AI pioneers with Scottish connections: Donald Michie (founded Europe’s first AI research group at Edinburgh in 1963), Geoffrey Hinton (completed his PhD at Edinburgh, won the 2024 Nobel Prize in Physics), Joanna Bryson (Edinburgh graduate, co-authored the UK’s first national AI ethics framework), John Giannandrea (Strathclyde graduate, led AI at Google then Apple), and Amanda Askell (Dundee graduate, co-authored the GPT-3 paper and helped create Constitutional AI at Anthropic).

25. What is the National Robotarium?

Based at Heriot-Watt University, the National Robotarium drives breakthroughs in medical robotics, offshore robotics, and autonomous systems. It has incubated 14 robotics companies in its first few years, operating in a sector projected to grow to £218 billion globally by 2030.

26. How will research be better commercialised?

The strategy commits to piloting a new approach to university commercialisation through a “Venture Creator” model, bringing together all the essential elements of commercialisation. It will also progress initiatives from the Scottish Spin-out Report and establish a national cluster scheme.

27. What is the planned national cluster scheme?

The Scottish Government will establish a national cluster scheme, aligning AI as a critical enabling technology across all clusters, and develop financial support and guidance to enable clusters to emerge, grow, and compete internationally.

Infrastructure and Energy

28. What renewable energy advantages does Scotland have for AI?

Scotland produced 38.4 TWh of renewable electricity in 2024 — its highest annual total ever — representing a 13.2% increase on the prior year. It has 26.4 GW of new renewable capacity in planning or consented pipelines, one of the largest in Europe relative to population. Scotland also has established onshore and offshore wind sectors and first-mover advantage in floating offshore wind.

29. What major AI infrastructure investments are planned?

Key investments include: a £15 billion AI Pathfinder project in North Ayrshire creating a large-scale AI industrial park with up to 6,400 GPUs; a £2.5 billion CoreWeave and DataVita renewable-powered AI compute campus in Lanarkshire; and a Lenovo AI Research and Development Hub in Edinburgh.

30. What is Scotland’s approach to sustainable data centres?

Scotland is developing a “Scottish Green Compute” proposition — AI powered by clean energy. The strategy includes publishing guidance on what constitutes a “green” data centre, exploring “Green AI-Ready” planning zones, mobilising heat offtake opportunities linked to district heat networks, and promoting water-resilient design through Scottish Water advisories.

31. What is distributed compute and why does it matter?

Distributed compute spreads processing across multiple geographically dispersed sites rather than concentrating it in hyperscale data centres. Scotland’s national fibre backbone investment has enabled this potential. Distributed compute offers quicker deployment, benefits around AI inference capabilities, and heat reuse opportunities.

32. How are grid connection challenges being addressed?

While electricity grid policy is reserved to the UK Government, the Scottish Government is engaging with the National Energy Systems Operator (NESO) and the UK Government’s Connections Accelerator Service to enable projects to be connected as soon as feasible through NESO’s Grid Connection Reform Process.

Data

33. What is the strategy’s approach to public sector data?

The strategy recognises that Scotland’s public sector data is often fragmented and difficult to access. It commits to launching a data matchmaking pilot, identifying barriers to data access for AI, launching an AI innovation programme for public services, and delivering a more coordinated public sector approach through a joint leadership group with Local Government.

34. What is the data matchmaking pilot?

The data matchmaking pilot will enable organisations to access trusted public-sector datasets to support data-driven innovation. It is one of the ten priority actions to be completed before March 2027.

35. What are the data outcomes sought by 2031?

By 2031, the strategy seeks: collective leadership creating a mature public sector data ecosystem; secure, well-maintained data assets guided by transparency and safety principles; AI-ready public sector data deployed responsibly; and secure, anonymised datasets available across organisational boundaries for research and innovation.

Regulation

36. What is Scotland’s regulatory approach to AI?

Scotland’s approach is guided by the OECD’s five values-based principles for the responsible stewardship of trustworthy AI. The strategy advocates for UK-level regulation that places these principles on a statutory footing and enables alignment with the EU AI Act to maintain access to European markets.

37. How does the strategy relate to the EU AI Act?

The Scottish Government intends to advocate for UK-level AI regulation that addresses the need for Scottish businesses to access European markets, investment, and opportunities. The strategy notes that the EU AI Act imposes statutory obligations on providers of general-purpose AI models and that alignment would benefit Scottish companies operating in European markets.

38. What is meant by “sandboxes” in the context of AI regulation?

Sandboxes are controlled environments in which changes to regulations can be tested. The strategy references the Financial Conduct Authority’s existing regulatory sandbox and the UK Government’s proposed “AI Growth Lab” as models that Scotland may learn from and innovate upon.

39. Will there be Scotland-specific AI regulation?

The Scottish Government will review and report on the scope and requirement to regulate in devolved areas where additional sector-specific safeguards may be needed. A report on the scope and requirement for AI regulation in Scotland will be published before 2027.

Sectors and Applications

40. Which sectors does the strategy identify as priorities for AI?

The strategy identifies six priority sectors: Healthcare and Life Sciences, Advanced Manufacturing and Robotics, Financial Services and FinTech, Renewable Energy and Climate Science, Space and Satellite Technology, and Creative Industries.

41. How is AI being used in Scottish healthcare?

Examples include: the NeurEYE project (using retinal scans to detect dementia risk); NHS Grampian’s GEMINI project (using AI to detect 12% more breast cancers); the AI-TRiPS clinical trial (predicting life-threatening complications in emergency trauma); and SPARRAv4 (predicting emergency hospital admissions by analysing 4.8 million health records).

42. What is Scotland doing with AI in financial services?

Scotland’s financial services sector uses AI for fraud detection, customer support, compliance, and investment analysis. Key supporting organisations include FinTech Scotland, the Financial Regulation Innovation Lab, the Finance and Health Lab, and the Smart Data Foundry.

43. How does the strategy address AI in creative industries?

The strategy acknowledges that AI creates new opportunities for digital creativity while also posing risks to intellectual property and creative work. It emphasises protecting creators’ rights, supporting ethical practice, and ensuring people benefit from ethical and trustworthy AI.

Semiconductors

44. What is Scotland’s semiconductor capability?

Scotland has a Critical Technologies supercluster covering photonics, quantum, semiconductors, and connectivity and sensing. The semiconductor cluster includes over 50 companies across the full supply chain, multiple open-access industrial facilities, and turnover exceeding £1.2 billion annually. Specialisms include image sensors, AI architectures, advanced packaging, and automotive applications.

45. How does the strategy support semiconductor growth?

The strategy will engage Scotland’s Critical Technologies Supercluster Advisory Board, enable growth of high-potential AI hardware spin-outs, and align workforce planning with advanced technology industry needs. Scotland aims to differentiate globally by leading in low-energy, semiconductor-enabled data centre and edge-AI technologies.

Risks and Ethics

46. What risks does the strategy identify?

The strategy identifies seven key risk areas: privacy and data protection, workforce impacts, environmental impacts of AI energy and water consumption, renewable energy capacity pressures, gender inequality being reproduced or amplified by AI systems, sovereign infrastructure concerns, and sector-specific disruptions to business models and skills demand.

47. How does the strategy address environmental concerns?

The strategy addresses environmental impacts through promoting renewable-powered compute, water-secure data centre development, energy-aware planning, heat reuse from data centres, and guidance on what constitutes a “green” data centre. Environmental considerations will guide AI infrastructure planning.

48. How does the strategy ensure ethical AI?

The strategy is guided by OECD AI principles and Scotland’s commitment to Fair Work. It establishes an independent Expert Advisory Board, implements trusted frameworks for AI in health and social care, advocates for principles-based regulation, and commits to transparency through mechanisms like the Scottish AI Register.

Public Trust and Engagement

49. How will public trust in AI be built?

The strategy commits to launching a nationwide engagement programme to listen to concerns and develop solutions that ensure public trust and confidence. It will also increase visibility of AI use in the public sector, strengthen AI literacy through open access learning materials, and ensure AI-enabled public services are transparent, fair, and accountable.

50. Will non-digital routes to public services still exist?

Yes. While AI offers clear benefits such as reducing administrative burdens and improving access to information, the strategy explicitly states that non-digital routes to accessing public services will still exist. AI-enabled services are intended to supplement, not replace, existing service channels.

Source: Scotland’s AI Strategy 2026–2031, published by the Scottish Government, March 2026. ISBN: 978-1-80775-004-6.

Scotland’s AI Strategy 2026–2031 Explained

Alex Velinov — Mon, 23 Mar 2026 09:54:54 GMT

TL;DR

AI could add £23 billion a year to Scotland’s GDP. That’s the headline figure behind a new five-year national strategy, launched in March 2026, that sets out how Scotland will use artificial intelligence to grow its economy, improve public services, and create new jobs. It’s built around an eight-layer “AI Stack” framework, backed by billions in private investment, and comes with 10 concrete actions to be delivered by March 2027.

The Launch

On Friday 20 March 2026, the Scottish Government officially launched its AI Strategy 2026 - 2031 at the Edinburgh Futures Institute, Edinburgh. The event, brought together policy makers, business leaders, academics, and tech professionals for what was billed as a landmark moment for Scotland’s digital future.

The strategy was introduced by Deputy First Minister Kate Forbes MSP and Minister for Business and Employment Richard Lochhead MSP. The details were presented by Colin Cook, Director of Economic Development at the Scottish Government. The strategy was developed with input from over 100 experts, business leaders, academics, and third-sector organisations, guided by the AI Sub-group of the Scottish Technology Council.

Delivery will be driven by AI Scotland, a new national transformation programme led by the Scottish Government in partnership with The Data Lab, Scottish Enterprise, Highlands and Islands Enterprise, and South of Scotland Enterprise. Working across business, academia, and the public sector, AI Scotland will coordinate the country’s collective efforts. An early priority is the launch of a refreshed AI Adoption Programme aimed at SMEs, building on a £1 million pilot from 2025.

The strategy’s core purpose is straightforward: use AI to drive responsible and inclusive economic growth while making a positive difference at every level of Scottish society. It’s not just a vision document. It comes with defined outcomes, a phased action plan, and an independent Expert Advisory Board to ensure accountability. The plan will be delivered in three phases: Phase 1 actions are already in motion, Phase 2 updates will follow in 2027, and Phase 3 in 2029.

But what does “responsible AI” actually mean here? The strategy is guided by the OECD’s five principles for trustworthy AI and Scotland’s commitment to Fair Work. In practice, that means every programme under the AI Scotland banner must promote ethical deployment, address barriers to adoption, support high-potential companies to scale, and improve access to quality data, all while maintaining public trust.

How will we know if it’s working? The government has defined clear outcomes for each of the eight layers of the strategy, with regular progress reports planned throughout the five-year lifecycle. An Expert Advisory Board made up of AI champions, business leaders, and technical experts will evaluate activities and guide future priorities.

10 Key Stats From the Strategy

£23 billion estimated additional annual GDP that AI could generate for Scotland by 2035. For context, that’s roughly comparable to the entire output of Scotland’s financial services sector.
£140.75 billion potential cumulative GDP boost between 2025 and 2035. That would represent a transformative shift in the size of Scotland’s economy over a single decade.
296 AI-focused companies currently operating in Scotland, spanning start-ups, scale-ups, research institutions, and consultancies.
61.9% proportion of Scottish SMEs not yet using AI technologies. This is the adoption gap the strategy is designed to close.
5,700 AI-related job postings across Scotland between July 2024 and June 2025, signalling strong and growing employer demand.
£8 billion+ private investment backing Scotland’s first AI Growth Zone in North Lanarkshire, one of the largest AI infrastructure commitments in Europe.
3,400+ new jobs the Lanarkshire AI Growth Zone is expected to create, including 800 high-value AI and digital infrastructure roles.
38.4 TWh renewable electricity produced in Scotland in 2024, a record high and a core part of the country’s pitch to global data centre investors.
50+ companies in Scotland’s semiconductor cluster, with annual turnover exceeding £1.2 billion across the full supply chain.

Quick Reference: 10 Actions at a Glance

These are the ten priority actions the Scottish Government has committed to delivering before March 2027. Each one is explored in more detail within the AI Stack sections below.

Position AI Scotland as the national flagship delivery programme
Appoint AI Industry Champions with an independent Expert Advisory Board
Launch a nationwide public engagement programme to build trust
Implement a trusted framework for AI in health and social care
Roll out a revitalised SME AI adoption programme with a new AI Leadership Academy
Establish a Future Jobs Panel for workforce planning
Pilot an AI Scale-up Accelerator for high-growth companies
Launch an AI innovation programme for public services
Promote Scotland as a green data centre hub and maximise the Lanarkshire AI Growth Zone
Launch a data matchmaking pilot for public-sector datasets

Phase 2 updates follow in 2027 and Phase 3 in 2029, ensuring the plan adapts as technology evolves.

The AI Stack: Scotland’s Framework Explained

At the heart of the strategy sits the “AI Stack”, a model of eight interconnected layers that represent everything needed for a healthy AI ecosystem. These layers aren’t ranked by importance; they all depend on each other. Data and Regulation are shown as “encircling” layers, sitting around and within all other layers.

Layer 1: Users

This is about everyday people: citizens who interact with AI through public services, healthcare, and digital platforms. The goal by 2031 is that people across Scotland understand where AI is being used in services that affect them, feel confident engaging with it, and trust that it’s being used fairly. Non-digital access to services will still exist. AI supplements; it doesn’t replace.

Key actions by 2027:

Establish an independent Expert Advisory Board
Launch a nationwide engagement programme to build public trust
Implement a trusted framework for safe AI in health and social care
Promote open-access AI literacy materials
Increase visibility of AI use across the public sector
Work with public bodies to strengthen ethical and inclusive approaches to AI governance

Q&A

Will AI replace how I access public services? No. You’ll still be able to access services the way you do now. AI is being introduced to speed things up and reduce paperwork, not to remove the human option.

How will I know when AI is involved in decisions about me? The government says it will make it clearer when and how AI is being used in public services, including what safeguards are in place. The aim is full transparency.

What about people who aren’t tech-savvy? Free, open-access learning materials are planned, alongside a nationwide engagement programme designed to reach people of all ages, backgrounds, and locations.

Layer 2: Adoption and Skills

Nearly two-thirds of Scottish SMEs aren’t using AI yet. This layer tackles that head-on with practical support: a revitalised national adoption programme, a new AI Leadership Academy for business leaders, a standardised AI readiness tool, and expanded modular training focused on real use cases. A Future Jobs Panel will map out how AI reshapes the workforce and guide national skills planning.

Key actions by 2027:

Roll out the revitalised national AI adoption programme
Pilot the AI Leadership Academy for SME leaders
Introduce a standardised AI readiness tool for SMEs and public bodies
Expand short, modular AI literacy training on practical use cases and ethics
Establish the Future Jobs Panel to assess AI’s workforce impact

Q&A

I run a small business. What support is available for me? Quite a lot, actually. A refreshed national programme will offer hands-on guidance tailored to SMEs. There’s a new Leadership Academy to help business owners get to grips with AI, a readiness tool to show you where you stand, and short training courses on practical use cases.

Will AI take my job? The strategy is built around Fair Work principles. Rather than replacing people, the focus is on helping workers develop new skills so they can benefit from the changes AI brings. A new Future Jobs Panel will track the impact and guide national planning.

What is the AI readiness tool? Think of it as a health check for your organisation. It’s a standardised assessment that helps SMEs, social enterprises, and public bodies understand how prepared they are for AI and what to focus on next.

Layer 3: Companies and Products

Scotland is home to around 296 AI companies, from early start-ups to globally recognised scale-ups like Edinburgh-based Wordsmith AI, which reached a $100 million valuation just 18 months after launch. This layer aims to build a pipeline of high-growth AI firms capable of reaching billion-pound valuations, supported by better access to compute power, talent, and international investment.

Key actions by 2027:

Pilot an AI Scale-up Accelerator connecting high-growth companies with experienced entrepreneurs and investors
Deliver a national stakeholder event for Scotland’s AI ecosystem
Undertake a national assessment of AI company needs and scaling barriers
Increase accessibility of compute power for Scottish AI companies
Strengthen international investor engagement via Techscaler and public agencies

Q&A

Can Scottish AI companies compete on the global stage? That’s the ambition. With 296 AI companies already here and a growing track record of fast-scaling start-ups, the ecosystem has real momentum. The strategy backs this with a Scale-up Accelerator, international investor engagement, and better access to computing infrastructure.

What about access to computing power? This is a big barrier for smaller firms. The government plans to work with existing asset owners to make compute capacity more accessible and affordable for Scottish AI companies.

Why does Scotland need its own AI companies? Control. Home-grown AI companies help protect sensitive data in areas like health and public services. They also keep skilled workers in Scotland and support the wider ecosystem of universities, investors, and start-ups.

Layer 4: Innovation, Research and Development

Scotland’s universities are world-class in AI research, but too little of that brilliance is making it into commercial products. This layer focuses on closing the gap between lab and market through a new Venture Creator model for university commercialisation, a national cluster scheme, and stronger pathways from research to spin-outs. AI is already delivering remarkable results in Scottish healthcare: the NeurEYE project is using nearly 1 million retinal scans to detect dementia risk, and NHS Grampian’s GEMINI project used AI to detect 12% more breast cancers than standard screening.

Key actions by 2027:

Launch an AI innovation programme for public services
Establish a national cluster scheme with AI as a critical enabling technology
Develop financial support for clusters to compete internationally
Pilot the Venture Creator model for university commercialisation
Strengthen the research commercialisation pipeline via the Scottish Spin-out Report

Q&A

What’s a “Venture Creator”? It’s a new model for turning university research into businesses. Instead of leaving academics to figure out commercialisation alone, it brings together funding, mentoring, and business expertise under one roof.

Is Scottish AI research any good? More than good. Scotland’s universities perform strongly on the international stage in AI and related disciplines. The problem isn’t the quality of research; it’s that not enough of it is being turned into products, services, and companies.

What real-world impact is AI already having in Scotland? Several projects stand out. The NeurEYE project is using retinal scans to spot early signs of dementia. NHS Grampian’s GEMINI project found 12% more breast cancers than standard practice. SPARRAv4 analyses over 4.8 million health records to predict emergency hospital admissions. And the AI-TRiPS clinical trial is one of the world’s first randomised evaluations of AI in emergency trauma care.

Layer 5: Data Centres and Infrastructure

AI needs massive computing power, and that needs to be hosted somewhere. Scotland is positioning itself as a green data centre hub, leveraging its record-breaking renewable energy output and water infrastructure. Key investments include a £15 billion AI industrial park in North Ayrshire with up to 6,400 GPUs, and a £2.5 billion CoreWeave and DataVita renewable-powered compute campus in Lanarkshire.

Key actions by 2027:

Ensure effective delivery of the Lanarkshire AI Growth Zone
Explore a dedicated AI Accelerator linked to the Growth Zone
Progress the pipeline of data centre investment opportunities
Identify and mobilise heat offtake opportunities on data centre sites linked to district heat networks
Publish guidance on what constitutes a “green” data centre

Q&A

Why Scotland for data centres? Scotland produced a record 38.4 TWh of renewable electricity in 2024, with 26.4 GW of new capacity in the pipeline. That’s one of the largest renewable pipelines in Europe relative to population. For global investors, AI powered by clean energy in a politically stable country is a strong proposition.

What about the environmental impact? It’s being taken seriously. Scottish Water is advising on sustainable water use, including wastewater reuse and closed-loop cooling systems. The government will also publish guidance on what qualifies as a “green” data centre, and is exploring ways to reuse heat from data centre sites to warm nearby homes through district heating networks.

What is distributed compute and why does it matter? Instead of concentrating all processing in a handful of giant data centres, distributed compute spreads it across smaller sites around the country. Scotland’s national fibre backbone makes this possible, and it offers practical benefits: faster deployment, lower latency for AI tasks, and opportunities to reuse waste heat locally.

Layer 6: Semiconductors

Every AI system runs on chips, and the global race to secure semiconductor supply chains is intensifying. Scotland already has skin in this game: a Critical Technologies supercluster with over 50 companies, annual turnover exceeding £1.2 billion, and specialisms in image sensors, AI architectures, and photonics. The ambition is to differentiate globally by leading in low-energy, semiconductor-enabled data centre and edge-AI technologies.

Key actions by 2027:

Engage the Critical Technologies Supercluster Advisory Board to identify strengths and coordinate R&D
Enable growth of high-potential AI hardware spin-outs
Align future workforce planning with the needs of advanced technology industries

Q&A

How big is Scotland’s semiconductor industry? Bigger than many people realise. Over 50 companies span the full supply chain, from research through to advanced packaging, with combined annual turnover above £1.2 billion and multiple open-access industrial facilities.

What makes Scotland different in this space? Niche strengths. Scotland isn’t trying to compete with Taiwan on volume manufacturing. Instead, it’s focusing on photonics, chip design, power electronics, and advanced packaging, areas where focused innovation can deliver outsized returns and sovereign capability.

Layer 7: Data

Good AI needs good data, and right now Scotland’s public sector data is too fragmented and too hard to access. This layer tackles that with a data matchmaking pilot to open up trusted public-sector datasets for innovation, a programme to identify what’s blocking access, and a joint leadership group with Local Government to drive a more coordinated approach.

Key actions by 2027:

Launch the data matchmaking pilot for public-sector datasets
Identify barriers and enablers affecting public sector data access for AI
Launch an AI innovation programme for public services
Establish a joint data leadership group with Local Government

Q&A

What is the data matchmaking pilot? It’s a new programme that will connect organisations with trusted public-sector datasets they can use to develop AI-driven solutions. Think of it as a facilitated introduction between the people who hold the data and the people who can put it to good use.

Will my personal data be at risk? No. Data shared through these programmes will be anonymised and governed by strict security frameworks. The strategy is built on principles of transparency, safety, and public benefit.

Why does data matter so much for AI? Because AI learns from data. If public sector data stays locked in silos, Scotland can’t build the AI tools that could improve NHS services, make government more efficient, or drive new research. Unlocking that data safely is one of the biggest practical challenges the strategy tries to solve.

Layer 8: Regulation

Rules matter. Without clear regulation, businesses don’t know what’s allowed, investors hesitate, and the public loses trust. Scotland’s approach is guided by OECD principles and advocates for UK-level legislation that aligns with the EU AI Act, which is crucial for Scottish businesses that trade with European markets. The government will also review whether additional Scotland-specific safeguards are needed in devolved areas.

Key actions by 2027:

Advocate for UK AI regulation aligned with OECD principles and the EU AI Act
Review the scope for regulation in devolved areas where sector-specific safeguards may be needed
Publish a report on the requirements for AI regulation in Scotland

Q&A

Will regulation slow down innovation? The strategy argues the opposite. Clear rules give businesses and investors the confidence to commit. And aligning with international standards, particularly the EU AI Act, keeps European markets open for Scottish companies rather than creating barriers.

Is there going to be Scotland-specific AI regulation? Possibly. The government will review whether devolved areas need additional safeguards and will publish a report before 2027.

What are regulatory “sandboxes”? They’re controlled environments where new regulations can be tested before being rolled out widely. The strategy references the Financial Conduct Authority’s existing sandbox and the UK Government’s proposed “AI Growth Lab” as models Scotland could learn from.

Final Words

There is a lot to like about this strategy. It’s well-structured, specific, and refreshingly honest about the gaps Scotland needs to close, from the 62% of SMEs not yet using AI to the fragmented state of public sector data. The eight-layer AI Stack is a genuinely useful framework, and the commitment to publishing phased updates in 2027 and 2029 means the strategy has a built-in mechanism for course correction.

The delivery partnerships are credible. Having The Data Lab, Scottish Enterprise, Highlands and Islands Enterprise, and South of Scotland Enterprise working alongside the government gives AI Scotland a practical reach that a policy document alone could never achieve. The ten priority actions are concrete, time-bound, and measurable, which is more than many national strategies can claim.

Of course, strategies are only as good as their execution. The real test will come in the next 12 to 18 months. Will the AI Leadership Academy attract SME leaders who wouldn’t otherwise engage with AI? Will the data matchmaking pilot unlock genuinely useful datasets, or get bogged down in governance? Will the Lanarkshire Growth Zone deliver the promised 3,400 jobs, and will those jobs benefit local communities as well as global tech firms? These are the questions that will determine whether this strategy becomes a turning point or a shelf decoration.

For now, though, the ambition and the architecture are in place. Whether you’re a business owner weighing your first AI investment, a citizen wondering what this means for your GP practice, or a researcher looking to turn your work into a company, this strategy is speaking directly to you. It deserves your attention.

Resources

Google Lyria: What It Is, How It Works, and Complete Prompting Guide

Alex Velinov — Thu, 19 Feb 2026 14:35:53 GMT

TL;DR

Google DeepMind launched Lyria 3 mid‑February 2026 (announced February 17, 2026) - its most advanced AI music generation model. Available inside the Gemini app for anyone 18+, it generates 30-second tracks complete with vocals, lyrics, and album art from a simple text prompt or even a photo. It supports eight languages, spans virtually every music genre, and is rolling out globally. This guide breaks down what Lyria is, how to prompt it effectively (from beginner to advanced), gives you 10 prompts to try right now, and offers a downloadable prompting guide for deeper exploration. Whether you’re a business leader evaluating AI creative tools, a content creator looking for faster production, or simply curious - this is your starting point.

What Is Google Lyria?

Lyria is Google DeepMind’s family of AI music generation models. Think of it as the musical equivalent of what DALL-E and Midjourney did for images, or what Veo did for video, except the output is fully-produced, high-fidelity audio.

The name “Lyria” covers three distinct products, each serving a different use case:

Lyria 3 is the flagship consumer model, now embedded directly in the Gemini app. Describe an idea - “a comical R&B slow jam about a sock finding their match” and within seconds, Gemini produces a 30-second track with vocals, auto-generated lyrics, and AI-created cover art. No musical knowledge required.

Lyria 2 is the developer-facing API available through Google Cloud’s Vertex AI. It generates instrumental-only tracks and offers structured parameters like negative prompts (to exclude unwanted elements) and seed values (for reproducible outputs). This is the tool for production pipelines.

Lyria RealTime is the most experimental offering - a streaming API that generates continuous music in real time via WebSocket connections. You can steer it live, blending genres and adjusting tempo on the fly. Think of it as jamming with an AI musician.

For most people reading this, Lyria 3 in the Gemini app is the one that matters. It’s free, it’s accessible, and it’s remarkably capable. That’s where this guide focuses.

How Lyria 3 Works

Unlike earlier AI music tools that stitched together pre-made loops, Lyria 3 generates complete musical arrangements from scratch. The model handles melody, harmony, rhythm, timbre, and vocals simultaneously -creating multi-layered compositions at 48kHz stereo quality (that’s higher than CD quality).

Here’s what happens when you type a prompt:

You describe your track using natural language - genre, mood, instruments, tempo, vocal style, lyric theme, or any combination. You can also upload a photo and ask Lyria to compose something that matches its visual mood.

Lyria interprets your intent across multiple musical dimensions. It understands genre conventions, instrument relationships, emotional contexts, and how music should progress and evolve over time.

The model generates a full arrangement - not just a melody or a beat, but a complete production with multiple instruments, dynamics, and vocal performance.

Output is delivered as a 30-second track with lyrics and AI-generated cover art (created by Google’s Gemini’s built‑in image model). You can download it or share it via link.

Every track is embedded with SynthID, Google’s imperceptible audio watermark. It’s inaudible to humans but detectable by software, even after compression, speed changes, or re-recording through speakers. You can upload any audio file to Gemini and ask whether it was generated by Google AI.

Lyria 3 currently supports vocals and lyrics in English, German, Spanish, French, Hindi, Japanese, Korean, and Portuguese, with more languages coming.

How to Prompt Lyria 3

The quality of your output is directly proportional to the quality of your prompt. Lyria understands natural language, so there’s no special syntax to learn - but knowing what musical elements the model responds to makes a significant difference.

Every effective Lyria prompt is built from a combination of these building blocks:

Genre & Style

The musical category and, crucially, the era. “Early 90s hip-hop” produces dramatically different results than just “hip-hop.” You can also blend genres: “K-pop with a Motown edge” works surprisingly well. Lyria recognises a vast range of genres - from Afrobeat, Bossa Nova, and Celtic Folk to Drum & Bass, Synthpop, Vaporwave, and dozens more.

Mood & Emotion

The feeling the music should evoke. This is often the most impactful single element in your prompt. Think beyond simple words like “happy” or “sad” - Lyria responds well to nuanced descriptors like ethereal ambience, triumphant, bittersweet, nostalgic, brooding, or dreamy.

Instrumentation

Specific instruments you want to hear. The more descriptive, the better: “warm acoustic guitar with fingerpicked style” outperforms “guitar” significantly. Lyria recognises over 70 instruments by name - everything from Rhodes Piano and Buchla Synths to Koto, Tabla, Hang Drum, and TR-909 Drum Machine.

Tempo & Rhythm

The pace of the track. You can be explicit (”120 BPM”) or descriptive (”slow ballad,” “uptempo dance groove”). Tempo dramatically shapes the energy of the output.

Vocals

Gender, range (soprano, baritone), vocal quality (breathy, gravelly, soulful, powerful), and language. You can provide custom lyrics by prefixing them with “Lyrics:” or describe a theme and let Lyria write the words.

Dynamics & Arrangement

How the music evolves. “Starts with a lone piano, builds with strings at the midpoint, full ensemble in the chorus” gives the track narrative arc.

Production Quality

The overall sonic character: clean and polished, warm and vintage, raw and lo-fi, spacious and reverb-heavy.

You don’t need every element in every prompt. Lyria intelligently fills gaps based on genre conventions. But the more specific you are about what matters to you, the more control you have.

Want the full reference lists?

The guide includes complete tables of 70+ supported genres, 80+ confirmed instruments, mood descriptors, vocal options, and production keywords - all verified against official Google documentation.

Download Lyria Prompting Guide

Prompting Templates: From Easy to Pro

Beginner - One-Line Prompts

If you’re just getting started, keep it simple. Describe a vibe and let Lyria handle the details.

Template:

A [genre] song about [topic]

Examples:

A rock song about summer adventures

An upbeat birthday tune with a fun pop feel

A chill lo-fi beat for studying

These work. The output won’t be precisely tailored, but it’s a fast way to explore what Lyria can do.

Intermediate -Layered Prompts

Add mood, instruments, and tempo for more targeted results.

Template:

A [mood] [genre/era] track with [vocal style]. Instruments: [2–4 instruments]. [Tempo description]. [Theme or lyric direction].

Examples:

A dreamy indie pop track with a breathy female vocal. Instruments: soft synth pads, acoustic guitar, gentle beat. Slow tempo, nostalgic mood, spacious reverb.

An energetic 90s hip-hop track with boom-bap drums, funky slap bass, turntable scratches, and brass stabs. Male vocals with a confident, playful delivery. A song about Friday nights.

This level of detail gives you meaningful control without requiring music production expertise.

Advanced - Structured Prompts

For maximum creative control, specify multiple dimensions including dynamics, custom lyrics, and production character.

Template:

Create a [genre A] meets [genre B] track. Tempo: [BPM]. Instruments: [3–6 specific instruments with adjectives]. [Vocal gender] vocals with a [vocal quality] tone. Lyrics: [your lines with (backing vocals)]. The track should [arrangement/dynamics description]. [Production/atmosphere description].

Example:

Create a track that merges 1970s funk with modern electronic synthwave. Tempo: 115 BPM. Instruments: funky slap bass, vintage Rhodes piano, analog synth pads, crisp electronic drums. Male vocals with a smooth, soulful tone. Lyrics: We’re riding through the neon night (night), every beat a pulse of light (light). The track should build from a sparse, groovy intro into a full, energetic chorus with layered harmonies. Warm vintage production with analog warmth.

Image Prompts

Upload a photo - holiday snaps, your pet, a piece of art, a sunset and Lyria analyses the subjects, setting, colours, and mood to compose a matching track.

Template:

[Upload image] Create a [genre] track that captures the [mood/feeling] of this image. [Instrument and vocal preferences].

Example:

[Upload photo] Create a downtempo ambient chill out track that captures the tranquility of this image. Use sounds form the nature and handpan drum, instrumental

10 Prompts You Can Try in Gemini Today

Copy and paste any of these directly into the Gemini app:

1. The Personalised Birthday Song Create a joyful, upbeat birthday pop song with catchy melody, hand claps, and cheerful brass. Male vocals, fun and goofy. Lyrics should be celebratory and humorous about getting older but still feeling young.

2. The Focus Soundtrack A calm lo-fi hip hop beat with warm dusty piano, soft boom-bap drums, and a lazy saxophone melody. Slow tempo, relaxed and cozy. No vocals.

3. The Podcast Intro A confident, modern electronic track with a punchy beat, bright synth hook, and crisp production. 10 seconds of build into a catchy drop. Fast, energetic, professional.

4. The Nostalgic Throwback An early 2000s pop-punk track with driving electric guitar, fast drums, and anthemic energy. Male vocals, raw and passionate. A song about missing your hometown.

5. The Cinematic Moment A cinematic orchestral piece with soaring strings, powerful brass, and thunderous percussion building to an epic crescendo. Heroic, triumphant, and grand. Film score quality.

6. The Cross-Cultural Fusion A track combining Japanese koto with modern electronic production, tabla percussion, and ambient synth textures. Ethereal, meditative mood with unexpected rhythmic complexity.

7. The Workout Banger High-energy EDM track with massive bass drops, pulsing synths, aggressive drums, and a huge build-up. 130 BPM, relentless energy. No vocals.

8. The Bedtime Lullaby A gentle, soothing acoustic lullaby with soft fingerpicked guitar, warm cello, and a tender female vocal. Very slow tempo, peaceful and dreamy. Lyrics about stars and sweet dreams.

9. The Afrobeat Party High-energy Afrobeat track with driving percussion, funky guitar rhythms, bright brass hits, and a groovy bass line. Male and female duet vocals with playful lyrics about dancing all night. 110 BPM.

10. The Genre Experiment A track that fuses baroque classical harpsichord with drum and bass electronic production. Fast tempo, complex rhythms, dramatic dynamics. Start with solo harpsichord, then drop the bass.

Download the Full Prompting Guide

For a deeper dive - including the complete reference tables of 70+ supported genres, 80+ confirmed instruments, mood descriptors, vocal options, advanced techniques, API-specific prompting, and Lyria RealTime configuration - download our comprehensive Lyria Prompting Guide:

Download Lyria Complete Prompting Guide

Frequently Asked Questions

Is Lyria 3 free to use? Yes. Lyria 3 is available to all Gemini users aged 18+ globally. Google AI Plus, Pro, and Ultra subscribers get higher generation limits, but the base experience is free.

Can I use Lyria-generated music commercially? Usage rights are governed by Google’s Terms of Service for the Gemini app. All generated tracks carry a SynthID watermark. For specific commercial licensing questions, consult Google’s official terms.

Can Lyria copy a specific artist’s style? Not directly. If you include an artist’s name in a prompt, Lyria treats it as broad creative inspiration - generating something with a similar mood or style, not an imitation. Filters are in place to check outputs against existing copyrighted content.

What’s the maximum track length? Currently 30 seconds per generation in Lyria 3 (Gemini app) and about 32.8 seconds per clip in Lyria 2 (Vertex AI API). Lyria RealTime streams continuously but sessions are capped to approximate 10 minutes.

Does Lyria only generate music in English? No. Lyria 3 (Gemini app) supports vocals and lyrics in eight languages: English, German, Spanish, French, Hindi, Japanese, Korean, and Portuguese. More languages are planned. Lyria 2 (Vertex AI API) currently accepts prompts in US English and generates instrumental‑only music.

Can I upload my own lyrics? Yes. Prefix your lyrics with Lyrics: in the prompt. Use parentheses for backing vocals or echoes, for example: Lyrics: We rise up (rise up) into the light (the light).

Is there an API for developers? Yes - Lyria 2 is available via Google Cloud’s Vertex AI, and Lyria RealTime is accessible through the Gemini API. Both require API keys and are geared toward developer and production workflows.

Final Words: Where AI Music Is Heading

What makes Lyria 3 significant isn’t just the technology - it’s the timing. We’re entering a period where AI-generated audio, whether music, voice, or soundscapes, will become one of the dominant modes of content consumption. Audio is effortless to digest. You can consume it on the go, during a commute, while cooking. Unlike text or video, it doesn’t compete for your visual attention. As AI makes audio creation instantaneous and near-costless, the sheer volume of AI-generated audio content will explode.

For music specifically, I expect a pattern of boom followed by selective correction. The initial excitement is justified - from a pure business perspective, AI music generation is a clear win. It streamlines creative production, compresses timelines, and dramatically reduces costs. For content creators, advertisers, game developers, and anyone who needs original music at scale, tools like Lyria fundamentally change the economics. Background music for videos, podcast intros, social content, in-store ambience, hold music - the commercial use cases are enormous and the ROI is immediate.

But here’s the nuance that most commentary misses: the very quality of AI music creates its own challenge. Right now, everything sounds equally polished. There’s a normalisation effect - the output is consistently good, but consistently predictable. There are no happy accidents, no rough edges, no moments where a musician’s imperfection becomes the thing that makes a track unforgettable. That absence of surprise will eventually become noticeable.

This is why I believe we’ll see the emergence of distinct audience segments. There will be a new, legitimate category of AI-generated music with its own dedicated audience - people who appreciate the standard, sounding good music with the accessibility, and the personalisation that AI enables. There will also be a counter-movement: a revival of appreciation for 100% human-created music, similar to how vinyl and analogue recording saw a resurgence in the streaming era. Both audiences will coexist, and both markets will thrive.

The smartest artists are already ahead of this curve. Musicians like will.i.am and Dr. Dre have integrated AI into their creative workflows - not as a replacement for human creativity, but as an amplifier of it. These are artists with proven track records of success, and their embrace of AI isn’t a concession. It’s a competitive advantage. They use AI to generate ideas faster, explore sonic territories they might not have considered, and handle the mechanical aspects of production so they can focus on the creative decisions that actually matter. The result is music that has both the efficiency of AI and the soul of human intent.

That, ultimately, is the most likely future: not AI versus human music, but AI as a creative instrument - one that the most adaptable artists and businesses will wield to produce work that neither could achieve alone.

Lyria 3 is one of the earliest mainstream tools that makes this future tangible for everyone. Whether you use it for a birthday song, a brand soundtrack, or the starting point of your next creative project - the barrier to musical creation just dropped to zero.

The question isn’t whether AI music will be part of your world. It’s whether you’ll be the one creating it, or just consuming it.

The Content Orchestrator - Mastering The Art Of Dual-Audience Strategy

Alex Velinov — Fri, 06 Feb 2026 06:50:57 GMT

TL;DR

The content landscape is splitting in two. AI agents now influence $15 trillion in B2B purchases and consume 51% of web traffic. They need structured, machine-optimized content. Simultaneously, humans are fleeing AI-generated sameness, craving authentic, voice-driven content. The solution isn’t choosing between audiences - it’s serving both with completely different strategies. Enter the Content Orchestrator: a hybrid role combining technical literacy to manage AI-generated machine content with craft expertise to create irreplaceable human content. Companies mastering this duality now will dominate the next decade. Those stuck optimizing for a single audience risk becoming invisible to both.

The Thing Nobody’s Saying Out Loud Yet

Here’s the thing nobody’s saying out loud yet.

We’re optimizing content for the wrong audience. Or rather... we’re optimizing for only one of our audiences. And it’s about to cost us.

Right now, as you read this, AI agents are crawling the web, reading product descriptions, parsing documentation, evaluating vendors, and making purchasing recommendations, often without a human ever seeing your carefully crafted landing page. By 2028, Gartner projects these agents will influence $15 trillion in B2B purchases. That’s not a typo. Fifteen. Trillion.

But here’s what hit me recently: we’re feeding these agents content designed for humans. And humans? They’re increasingly hungry for content that feels... well, human. Authentic. Real. Not another AI-generated listicle that sounds like it was written by a committee of algorithms.

The future isn’t about choosing between human readers and machine readers. It’s about serving both. Simultaneously. With completely different content strategies.

Welcome to the dualistic content era. Where your content team needs to become something entirely new.

Key Stats

On the Machine Side

74.2% of newly created English‑language webpages contained some AI‑generated content in April 2025 (Ahrefs analysis of 900,000 pages).
Only 25.8% of those pages were purely human‑written; the rest mixed human and AI text in varying proportions (Ahrefs).
Zero‑click searches on Google increased from about 56% in May 2024 to around 69% in May 2025 after AI Overviews rolled out (Similarweb).
Across all queries, the median zero‑click share sits near 60%, but for searches where AI Overviews appear, zero‑click rates can climb into the 80%+ range (Similarweb / Semrush synthesis).

On the Commerce Side

Gartner projects that by 2028, 90% of B2B buying will be intermediated by AI agents, representing over $15 trillion in B2B spend.
Early research indicates that more than 60% of consumers already use AI in some form to support shopping and purchase decisions (University of Virginia School of Business, via Similarweb).

On the Content Creation Side

74.2% of new webpages, and 86.5% of top‑ranking pages, now contain at least some AI‑generated content (Ahrefs).
Surveys of businesses report that roughly 80–90% now use AI tools to help create SEO and marketing content, with blog posts the most common format (Tenet / Ahrefs synthesis).

The pattern is unmistakable. Machines are writing the content. Machines are reading the content. And increasingly, machines are making the decisions.

So... what are we creating content for, exactly?

The Paradox

Here’s where it gets interesting.

While AI agents devour the web looking for structured, parseable, optimized content—humans are running in the opposite direction. They’re retreating to Discord servers, private communities, Substacks, podcasts. Places where they can verify there’s an actual human on the other end.

Think about your own behavior. When you see yet another blog post that reads like it was extruded through ChatGPT’s “make it sound professional” filter... you bounce. You know that feeling. The uncanny valley of content. Technically correct. Perfectly optimized. Completely soulless.

But when you find something with a real voice? With specificity, quirks, genuine insight? You save it. Share it. Remember it.

The machines want structured data and semantic markup. Humans want authenticity and voice.

And somehow, we’re supposed to serve both.

What’s Actually Changing (And Where)

This split is already reshaping every content format:

Video

Fracturing fastest. AI-generated explainer videos, product demos, and tutorials for machine consumption and indexing. But authentic, personality-driven video content for humans—think of how we trust creators who show their face, their workspace, their genuine reactions. That 95% of viewers can’t distinguish AI video from real footage? That’s exactly why verified human content becomes more valuable.

Written Content

Going two directions. Machine-optimized: structured FAQs, technical specifications, comparison matrices, API documentation - all formatted for agents to parse and act on. Human-optimized: essays with voice, narrative case studies, opinionated takes, slow-burn thought leadership. The stuff you actually want to read.

Audio and Podcasts

20,000 AI tracks uploaded daily means the signal-to-noise ratio is collapsing. Which makes verified human voices (with all their imperfections, tangents, and personality) more valuable, not less.

The content that wins with machines looks nothing like the content that wins with humans. And trying to split the difference? That’s how you lose both audiences.

Feeding the Machine (Without Boring the Humans)

So what do the machines actually want?

They’re not mysterious. They’re just... literal. Structured. Hungry for clean data.

The Infrastructure Emerging:

llms.txt - Think of it as robots.txt’s smarter cousin. A markdown file that tells AI agents exactly what your site offers and where to find it. Over 600 sites (Anthropic, Stripe, Cloudflare, Perplexity) have implemented it.

Model Context Protocol (MCP) - The universal adapter for AI agents to connect with your data sources. Adopted by OpenAI, Google DeepMind, Microsoft. It’s becoming the USB-C of AI agent communication.

C2PA - Content authenticity standard. Cryptographic proof of origin and edit history. Required by EU AI Act starting August 2026.

Business-to-Agent (B2A) - The emerging discipline of optimizing your business for AI agent discovery and transaction. Because agents don’t browse; they evaluate.

The Practical Translation

Machines want:

Structured data they can parse (JSON, schema markup, XML feeds)
Clear, factual answers to questions
Verifiable metrics and specifications
Real-time availability and pricing
APIs they can query directly

It’s not sexy. It’s infrastructure. But it’s the difference between being recommended by an AI agent... or being invisible to it.

The Architecture of Dual-Audience Content

Here’s what this actually looks like in practice.

You’re not maintaining one content operation anymore. You’re maintaining two parallel streams:

Stream One: Machine-Optimized Content

Product specifications with complete technical details
Structured FAQs in machine-readable formats
Comparison matrices with objective attributes
API documentation for agent access
Real-time data feeds (inventory, pricing, availability)
Schema markup on every page

This content is dense, factual, comprehensive. It’s designed to be parsed, indexed, and acted upon by agents making purchasing decisions. It answers the question: “Can this AI agent confidently recommend us?”

Stream Two: Human-Optimized Content

Essays with voice and perspective
Narrative case studies with context and story
Behind-the-scenes insight into decisions and trade-offs
Video content with real people and genuine reactions
Thought leadership that takes a stance
Long-form exploration of ideas

This content has fingerprints on it. It’s designed to be remembered, shared, and to build actual relationships. It answers the question: “Do humans trust us enough to care?”

Both matter. Both require craft. Both need resources.

But (and this is critical) - they require different skills.

Enter the Content Orchestrator

Traditional content creators were writers, designers, videographers. The skillset was creative, editorial, and visual.

The emerging role - the Content Orchestrator—is something different. Something hybrid.

They Need to Be Technically Literate Enough To:

Configure AI agents to generate machine-optimized content at scale
Implement structured data and schema markup
Understand APIs and data feeds
Monitor AI visibility and citation share
Optimize for generative engines, not just search engines

But Also Craftspeople Enough To:

Write with genuine voice and insight
Create content that feels authentically human
Understand what makes people trust, share, remember
Develop editorial judgment about what should be human vs. machine
Preserve brand voice and authenticity at scale

It’s not “AI will replace content creators.” It’s “content creators must become orchestrators of both machine efficiency and human connection.”

This is an opportunity, not a threat. The people who master this dual skillset? They’ll own the next decade of content strategy.

Because here’s what nobody else is saying: as AI-generated content floods every channel, the verified human touch becomes the scarcest resource. Premium brands will compete on authenticity. On having real people with real expertise creating content that AI can’t replicate.

And simultaneously, they’ll compete on being the most discoverable, most structured, most agent-friendly option in their category.

Two games. One strategy. Completely different execution.

New Formats Will Emerge

Here’s the part I’m most curious about.

When content splits into these two streams, what new forms emerge?

We’ve seen this pattern before. When radio faced TV, it didn’t die - it found its voice in talk, music, and intimacy. When newspapers faced the internet, long-form investigative journalism became more valuable, not less.

So what’s the “verified human content” equivalent in 2027? What formats and genres evolve specifically because they’re not replicable by AI?

I don’t know. Nobody does. But I’d bet on things like:

Slow content: Deep, researched, time-intensive pieces that agents can’t generate
Process transparency: Showing your work, your thinking, your humanity
Collaborative creation: Humans creating with humans in ways that feel genuine
Imperfection as signal: The rough edges that prove it’s real

The formats that win with humans might look nothing like what we’re creating today. And that’s... kind of exciting, actually.

Your Next Move

The shift to dual-audience content isn’t theoretical anymore. It’s happening now. And the gap between early adopters and laggards is widening daily.

Start With Awareness

Understand that your content strategy now has two distinct audiences with fundamentally different needs. The $15 trillion question isn’t whether to adapt - it’s how fast you can move.

Then Ask Yourself:

Which of our content should be optimized for machines? (Probably more than you think - product specs, FAQs, technical documentation, pricing, availability)
Which should be unmistakably, verifiably human? (Probably less, but higher quality - thought leadership, case studies, brand storytelling, behind-the-scenes insight)
Do we have people who can orchestrate both? (Or are we trying to force one skillset to do two completely different jobs?)
Are we measuring the right things? (AI visibility and citation share matter now, not just pageviews and time-on-site)

The Opportunity Is Real

Companies figuring this out now, while their competitors are still optimizing for a single audience will have a compounding advantage. Because the dual-content skillset is rare. The Content Orchestrators who can manage AI systems and create compelling human content. They’re not being trained in traditional programs. They’re inventing themselves right now.

That’s your opportunity. Build the capability before it becomes standard. Hire or develop the hybrid talent before everyone else realizes they need it. Implement the technical infrastructure (llms.txt, B2A optimization, structured data) while it’s still a differentiator, not table stakes.

What Comes Next

The content landscape is split. On one side: highly structured, machine-optimized content that AI agents can confidently parse and act on. On the other: deeply human, authentic content that builds trust and relationships in ways algorithms can’t replicate.

The organizations that thrive won’t be the ones with the most content. They’ll be the ones with the right content for each audience. The ones who understand that serving machines and serving humans require fundamentally different approaches and who have the talent and systems to execute both brilliantly.

The Question That Matters

So here’s what I want to know:

If the future is dual-stream content, one optimized for machine evaluation, one crafted for human connection. Which audience are you currently neglecting? And what’s it costing you?

Because one thing’s certain: the organizations are still trying to create “one-size-fits-both” content? They’re about to discover they’ve been invisible to both audiences all along.

AI and Sustainability: What Tech Giants Are Doing and What You Can Control

Alex Velinov — Fri, 30 Jan 2026 06:50:55 GMT

Recently I found myself in several conversations about AI and environmental impact. One question kept coming up:

“How can we use AI when we have environmental concerns?”

It’s a fair question that deserves a thoughtful answer. But as I dug into the data, I discovered something interesting: AI companies are actually leading the tech industry in sustainability commitments, transparency, and renewable energy adoption. They’re doing more than the social media platforms we’ve used for over a decade and yet AI bears most of the environmental scrutiny.

Perhaps more importantly, focusing on what tech companies do misses the bigger opportunity. Each of us controls decisions that have far greater environmental impact than our AI usage: how we travel, what we eat, how long we keep our devices, and how we power our homes.

This article examines what AI companies are really doing, how they compare to other tech sectors, and outlines practical steps that deliver measurable impact. Because if we’re serious about sustainability, we need to focus our energy where it matters most.

TL;DR

AI companies are leading tech sustainability efforts: firms like Google, Microsoft, Meta, and Nvidia already match 100% of their electricity use with renewables, while many social platforms still trail on similar commitments. Google reduced energy per AI query by 33x in one year while Meta and Microsoft invest billions in clean infrastructure. Social media platforms collectively generate 262 million tonnes of CO₂ annually with less transparency and accountability. The smartphone manufacturing supply chain produces massive emissions that dwarf individual AI usage—yet we upgrade devices every 1-2 years without hesitation. The opportunity lies in focusing on high-impact choices: reducing car travel, eating more plant-based foods, extending device lifecycles, and using AI for genuine productivity. Small shifts in these areas deliver exponentially more impact than limiting AI usage.

Understanding the Full Picture

Here’s what the data reveals: AI has become tech’s environmental focal point while other significant sources of digital emissions operate with less scrutiny.

Social media platforms collectively generate an estimated 262 million tonnes of CO₂ annually, nearly equivalent to Malaysia’s entire carbon footprint. TikTok alone produces 50 million tonnes, comparable to Greece’s national emissions. The average person spends 147 minutes daily on social media, generating tens of kilograms of CO₂ annually from a single high‑use social platform such as Instagram.

Consider the contrast: a typical AI text query uses on the order of a few tenths of a watt-hour of electricity and a fraction of a gram of CO₂, depending on the model and energy mix. You’d need hundreds to thousands of AI queries to rival the emissions from hours of high‑intensity scrolling and streaming. When AI generates code, analyzes data, or automates tasks (delivering genuine productivity), the emissions per unit of value created are remarkably efficient.

The renewable energy picture tells an even more interesting story. X sources just 10% of its energy from renewables. TikTok operates only one renewable-powered data center among many facilities. Meanwhile, Google, Meta, and Nvidia have achieved 100% renewable energy matching. Microsoft has contracted 34 gigawatts of clean energy across 24 countries.

This isn’t about assigning blame. It’s about recognizing that AI companies are responding to their computational demands with measurable commitments and investments that set new standards for the tech industry.

The Device Lifecycle Opportunity

There’s another part of this story worth exploring: the hardware we all use.

Manufacturing a single smartphone generates 85-95 kilograms of CO₂. Laptops produce 200-300 kilograms. Here’s the critical insight: 70-80% of a device’s lifetime emissions come from manufacturing, not operation.

This presents a tremendous opportunity. Extending your phone’s life from 2 to 4 years avoids manufacturing a replacement device, saving 85-95 kg of CO₂. That’s equivalent to running thousands of productive AI queries.

The most sustainable device is the one you already own. This single decision delivers more environmental benefit than most other digital choices combined.

What AI Companies Are Doing Right

AI companies face higher computational demands than traditional platforms, but they’re responding with transparency and investment that’s setting new benchmarks.

Google achieved 100% renewable energy matching globally in 2017, years before the current AI expansion. The company now pursues 24/7 carbon-free energy, meaning every kilowatt-hour consumed is matched with carbon-free generation on the same grid at the same time. A single Gemini query consumes 0.24 watt-hours and emits 0.03 grams of CO₂. Google’s data centers operate with industry‑leading efficiency, using about 84% less overhead energy (cooling and other support systems combined) than a typical data center, with AI‑optimized cooling cutting cooling energy by around 40%

Meta maintains 100% renewable energy matching since 2020 and has contracted around 12 gigawatts of clean energy, making it one of the world’s largest corporate buyers of renewables. Their data centers use 80% less water and 32% less energy than industry averages. The company has restored 1.6 billion gallons of water through 40+ environmental projects.

Nvidia achieved 100% renewable electricity in 2025 and continues to deliver major efficiency improvements in each new GPU architecture, dramatically increasing performance per watt for AI workloads. Microsoft operates a $1 billion Climate Innovation Fund and has contracted 34 gigawatts of carbon-free energy while pioneering innovations like mass timber data centers that reduce carbon footprint by 65%.

The transparency matters. Google, Meta, and Microsoft publish comprehensive annual sustainability reports with detailed emissions data across all scopes. They disclose renewable energy procurement, water usage, waste diversion, and supplier engagement metrics. This level of accountability enables stakeholders to track progress and hold companies responsible.

Some AI companies need improvement here. OpenAI and Anthropic should provide more detailed public sustainability data. But the sector overall demonstrates greater transparency than many established tech platforms.

Social platforms operating independently show different patterns. Many rely on parent company disclosures or publish limited independent reporting. The accountability standards vary significantly across the industry.

The takeaway: AI companies are making substantial investments in renewable energy, efficiency improvements, and transparent reporting. They’re setting the pace for tech industry sustainability.

Where Your Impact Actually Lives

Here’s the empowering part: the decisions you control deliver far more environmental impact than your AI usage.

You can’t dictate how tech companies build infrastructure. But you absolutely control your transportation choices, diet, home energy use, and device lifecycle decisions. These areas represent 80%+ of most individuals’ carbon footprints.

The following ten actions are ranked by actual environmental impact. The first items deliver exponentially more benefit than the last. Focus your energy on the big wins. That’s where meaningful change happens.

1. Reduce Car Travel

Transportation typically represents 25-30% of an individual’s carbon footprint. Driving less, using public transport, walking, or cycling delivers transformative impact. For business leaders: remote work policies and transit benefits matter enormously. This single category outweighs all your digital activity combined.

2. Shift Toward Plant-Based Eating

Food production accounts for roughly one-quarter of global emissions, with meat and dairy contributing disproportionately. Moving toward more plant-based meals (even without perfect adherence) delivers substantial reductions. This area rivals transportation in impact potential.

3. Improve Home Energy Efficiency

Home heating and cooling represent the second-largest portion of individual footprints. Better insulation, efficient appliances, smart thermostats, and heat pumps create lasting reductions. If you own your home, rooftop solar has never been more accessible. These improvements compound over years.

4. Cut Unnecessary Flights

A single transatlantic flight generates roughly the same emissions as an entire year of typical car driving. Choosing trains for medium distances, combining trips, and questioning whether every conference requires in-person attendance saves tremendous carbon. One avoided flight per year delivers more benefit than months of other optimizations.

5. Extend Device Lifecycles

This is where tech’s real environmental opportunity lives. Manufacturing accounts for 70-80% of device emissions. Keeping your phone for 4-5 years instead of 2 avoids manufacturing a replacement, saving 85-95 kg of CO₂. The same applies to laptops (200-300 kg per device) and other electronics. Repairing instead of replacing amplifies this benefit. This single shift saves more carbon than limiting AI usage could ever achieve.

6. Use AI Purposefully

Deploy AI when it delivers genuine value: code generation that saves hours, data analysis that informs decisions, research synthesis that accelerates projects, automation that improves efficiency. A productive AI session that eliminates manual work or enables better decisions justifies its energy cost many times over. The key is intentionality, using AI as a powerful tool rather than passive entertainment.

7. Optimize Streaming and Social Media

Streaming and social media represent moderate individual impact but massive aggregate footprint. Simple optimizations help: avoid background streaming you’re not watching, choose audio over video when visuals aren’t needed, lower resolution when HD isn’t necessary. Most impactfully: be intentional about time spent. Purposeful engagement beats mindless scrolling.

8. Practice Digital Efficiency

Delete old emails and large attachments. Turn off auto-play video. Disable unnecessary cloud backups. Send links instead of huge attachments. Use dark mode on OLED screens (21-35% energy reduction). These habits model the kind of thoughtful digital consumption that, when scaled across millions of users, creates meaningful change.

9. Support Sustainable Providers

When you have choices, prefer providers demonstrating strong environmental performance. Companies publishing detailed emissions data over those providing none. Platforms investing in renewable energy over those showing minimal commitment. Your purchasing decisions create market signals that reward leadership and accountability.

10. Use Your Voice and Influence

Share what you’re doing with colleagues, friends, and family. Encourage your organization to adopt greener digital practices: efficient hosting, thoughtful data management, better procurement. Support policies that accelerate public transport, improve home energy efficiency, and enable grid decarbonization. Individual actions inspire collective change and normalize sustainable choices.

Moving Forward with Clarity

The conversation about AI and sustainability deserves nuance and honesty about what actually drives environmental impact.

AI companies are demonstrating substantial leadership through renewable energy commitments, transparent reporting, and efficiency innovations. The decisions we control individually (transportation, diet, home energy, device lifecycles) deliver 10x to 100x more environmental impact than our AI usage. A single avoided flight saves more carbon than a year of AI queries. Keeping a phone one extra year saves more than months of limiting digital activity.

When we focus our energy on high-impact choices, we create meaningful change. When we extend this thinking to our organizations (influencing procurement, travel policies, and operational decisions), the impact multiplies.

The path forward isn’t about choosing between technology and environment. It’s about making thoughtful decisions in areas that actually matter, supporting companies demonstrating leadership, and using powerful tools like AI to drive productivity and innovation.

Focus on what you can control. Start with the big wins. Then optimize digital habits with clear understanding of what delivers impact.

Because when you take action on what actually matters, you’re not just reducing your footprint. You’re modeling the kind of intentional, high-impact thinking that inspires others and drives collective change.

That’s how individual actions become movements. That’s how we build a more sustainable future while embracing the tools that help us work smarter, solve harder problems, and create more value with less waste.

LLM Advertising: The Marketing Channel Shift That Will Define The Next Decade

Alex Velinov — Thu, 22 Jan 2026 06:50:53 GMT

LLM Advertising: The Marketing Channel Shift That Will Define The Next Decade

TL;DR

OpenAI launches ChatGPT ads Q1 2026 for free and low-tier users in the US, marking the first major Western AI platform to monetize through advertising
Google Gemini firmly rejects ads for now, citing user trust as competitive advantage while OpenAI faces revenue pressure
Conversational AI ads deliver 73% higher click-through rates than traditional search, but pose brand safety risks and attribution challenges
Market opportunity is massive: AI-driven ad spending projected to surge from $1.1B (2025) to $26B by 2029
Strategic imperative: Allocate 5-15% of digital budgets to test ChatGPT ads in Q1-Q2 2026 while building AI Optimization capabilities for organic visibility

The Conversational Advertising Era Begins

We’re witnessing a fundamental shift in how people discover products and services. Traditional search typing keywords into Google is giving way to conversational queries with AI assistants. Instead of searching “best hotels Barcelona,” users now ask ChatGPT: “What’s the best family-friendly hotel in Barcelona with a pool and near the beach?”

This transformation creates a new advertising frontier. On January 16, 2026, OpenAI announced it will introduce ads in ChatGPT, becoming the first major AI platform to monetize its users base through advertising. The move marks an inflection point: conversational AI is no longer just a technology experiment, it’s becoming a commercial advertising channel that will reshape digital marketing as profoundly as Google Ads did two decades ago.

For business leaders and marketers, the question isn’t whether LLM advertising will matter, but how quickly to adapt strategy, budget, and creative approaches for this high-intent, context-driven channel.

Status by Platform

OpenAI ChatGPT: First Mover on Ads

OpenAI is moving aggressively into advertising, with testing beginning in “coming weeks” and full rollout throughout Q1 2026. The decision reflects financial reality: despite 800 million weekly users, only fewer than 5% pay for subscriptions. With $1.4 trillion committed to AI infrastructure over eight years and a target of $20 billion annual revenue, advertising became essential for sustainability.

Who sees ads: Free tier users and ChatGPT Go subscribers ($8/month) in the US only. Premium tiers Plus ($20/month), Pro ($200/month), Business, and Enterprise remain ad-free.

Ad format: Ads appear at the bottom of chatbot responses in clearly labeled “sponsored” boxes, separate from answer content. Targeting is context-based, matching conversation topics without using historical user profiles or selling personal data. Users can opt out of personalized ads, and no ads will appear on sensitive topics like health, mental health, or politics. Users under 18 won’t see ads.

Advertiser access: The program launches February 2026 with less than $1 million minimum commitment per advertiser. Initial focus on travel, retail, and technology verticals using impression-based pricing. There’s no self-serve platform yet. OpenAI is building this for future release.

Privacy commitments: OpenAI pledges not to sell user data to advertisers and keeps conversations private. Advertisers receive only aggregate performance data (impressions, clicks), with no individual user information like age, location, or interests.

OPENAI CHATGPT: BY THE NUMBERS
800 million weekly users, but only 5% pay for subscriptions
$1.4 trillion infrastructure commitment over 8 years
Q1 2026 ad launch timeline (testing begins “in coming weeks”)
Strategic shift: CEO Sam Altman previously called ads a “last resort” revenue pressure changed the calculus
We plan to test ads at the bottom of answers in ChatGPT when there’s a relevant sponsored product or service based on your current conversation.” - OpenAI’s official blog post, January 2026

Google Gemini: The Anti-Ad Stance

Google is taking the opposite approach. At the World Economic Forum in Davos (January 2026), DeepMind CEO Demis Hassabis declared: “We don’t have any plans to do ads at the moment. We’re focusing on the core experience and the core technology of being a better assistant.”

This wasn’t Google’s first time rejecting Gemini ads. In December 2025, Dan Taylor (VP of Global Advertising) denied reports claiming a 2026 ad rollout: “There are no ads in the Gemini app and there are no current plans to change that.”

Alternative monetization: Instead of putting ads inside Gemini chat, Google is testing promotional content in “AI Overview” within Google Search and “AI Mode” for deep conversations. This preserves Gemini’s identity as a “personal AI assistant” rather than a commercial discovery tool.

Competitive positioning: Hassabis expressed surprise that OpenAI moved “so early” on ads, suggesting it reflects “revenue pressure” rather than long-term product strategy. He emphasized the fundamental difference between search (user intent-driven, ad-friendly) and assistants (operating on user’s behalf, trust-critical).

User base: Google has 650 million monthly active users on the Gemini app and over 2 billion monthly active users for the AI Overview feature in Search.

Long-term outlook: Google leaves the door open for future ads but prioritizes user experience and technological maturity first. With mature search advertising generating over $300 billion annually, the company has more financial flexibility than competitors facing immediate monetization pressure.

GOOGLE GEMINI: THE TRUST PLAY
650 million monthly active Gemini users; 2+ billion using AI Overview in Search
$300+ billion annual search ad revenue gives Google flexibility to wait
Zero current plans for Gemini ads - focusing on “core experience” instead
Strategic bet: User trust as competitive moat against ad-heavy rivals
“We have no urgent pressure to make hasty decisions like that.” - Demis Hassabis on OpenAI’s ad timing

Anthropic Claude: Enterprise-First, No Consumer Ads

Anthropic has made zero public statements about advertising and shows no internal signals of pursuing this model. With 300,000+ business customers and a $183 billion valuation (as of September 2025), the company focuses entirely on enterprise subscriptions and API services—competing on “responsible AI” and trust rather than consumer monetization.

DeepSeek: Hedge Fund Backed, API-Only Revenue

DeepSeek operates with a unique business model: wholly funded by High-Flyer, a Chinese quantitative hedge fund. This backing eliminates monetization pressure entirely. The company generates revenue through API services and enterprise solutions, with no advertising component. Despite surpassing ChatGPT as the #1 downloaded free app on US iOS in late January 2025, DeepSeek remains unlikely to introduce ads.

Grok (xAI): Ads Already Live

Grok took the most aggressive approach—ads are already embedded in chatbot responses since August 2025. Unlike ChatGPT’s separate labeled boxes, Grok integrates ads directly into responses. The strategic rationale: xAI reported a $1.46 billion net loss in Q3 2025 and burned through $7.8 billion in nine months. However, brand safety concerns around deepfake controversies and inappropriate content raise serious questions for advertisers.

Practical Recommendations

Immediate Actions (Q1 2026)

Secure ChatGPT pilot access (US Only): Contact OpenAI’s advertiser program launching February 2026. Prepare a budget under $1 million for initial testing. Prioritize if you operate in travel, retail, or technology - the pilot’s focus verticals.

Audit AI-mentioned brand presence: Use ChatGPT, Gemini, and Claude to query your product category. Document how your brand appears in organic responses. Identify competitors mentioned more frequently to establish baseline “AI share of voice.”

Develop conversational ad creative: Adapt messaging for context-based targeting rather than demographic profiles. Emphasize solutions to specific problems, not broad brand awareness. Test whether helpful recommendations (product discovery) outperform promotional offers (discounts).

Mid-Term Strategy (2026-2027)

Diversify beyond Google Ads: Allocate 5-15% of search budget to conversational AI testing. Track ROAS separately - expect higher click-through rates (73% improvement, according to Microsoft Advertising research on Copilot, conversational AI ads deliver) but different conversion dynamics. Prepare for platform fragmentation as multiple LLMs potentially introduce ads.

Build AI Optimization (AIO) capabilities: Invest in optimizing how your brand surfaces in LLM organic responses. Create structured data feeds consumable by AI models - product specs, FAQs, customer reviews. Monitor “prompt share of voice” to understand which competitors dominate AI recommendations in your category.

Prepare for attribution challenges: Conversational AI ads will fragment customer journey tracking. Implement incrementality testing to isolate LLM ad impact separate from other channels. Accept that aggregate metrics will provide less granular attribution than current digital platforms.

Final Thoughts: The Inevitable Evolution

Advertising has always followed attention. When people migrated from newspapers to television, ads followed. When the internet emerged, digital advertising was born. When mobile became dominant, app-based ads exploded. Now, as billions of users shift from keyword search to conversational AI, advertising will inevitably follow.

OpenAI’s move to introduce ChatGPT ads isn’t surprising. it’s a natural evolution of platform monetization. Google’s current resistance to Gemini ads reflects a luxury of existing revenue, not a permanent philosophy. As LLMs become the primary interface for information discovery, advertising will integrate into these experiences because that’s where commercial intent lives.

The opportunity is substantial: AI-driven ad spending will grow from $1.1 billion in 2025 to $26 billion by 2029. Conversational AI ads already deliver 73% higher click-through rates than traditional search. Users asking “What’s the best project management software for a 50-person team?” are signaling high purchase intent - the exact moment brands want to reach them.

For business leaders and marketers, the strategic question isn’t whether to participate, but how quickly to build capabilities. Early movers who secure ChatGPT pilot access, develop conversational creative, and master AI Optimization will gain competitive advantage. Those who wait risk becoming invisible in the channels where their customers increasingly discover products.

The platforms offering ad-free experiences today - Google Gemini, Anthropic Claude may be building trust as a competitive moat. But trust also requires sustainability. If free users don’t generate revenue, these platforms may eventually face the same pressures OpenAI does now.

As you plan your 2026 marketing strategy, consider this:

In a world where customers ask AI for recommendations instead of searching Google, how will your brand earn the right to be suggested?

The answer will define which companies thrive in the conversational advertising era—and which become footnotes in the history of marketing’s next great transformation.

AI vs Human Error: Where Machines Win, Where They Fail, and Why Hybrid Beats Both

Alex Velinov — Fri, 09 Jan 2026 06:30:36 GMT

TL;DR

“Error rate” isn’t one universal number. Depending on the domain, it can mean missed cancers, mistranscribed words, misrouted tickets, false fraud alerts, escaped defects, buggy code, or crashes per mile.
When the task, data, and metric are directly comparable, AI can match or, in some settings, exceed individual human performance on narrow, well-defined perceptual or classification tasks (this is domain- and setup-dependent).
The practical question isn’t “Who wins?” It’s how each fails. AI tends toward systematic failures under data shift or edge cases. Humans tend toward inconsistent misses under fatigue and workload.
The most useful operating model is complementarity: AI reduces high-volume, repetitive error; humans reduce context and judgment error. The best outcomes come from designing human+AI workflows on purpose.

Introduction

Every leadership team is being asked some version of the same question: Is AI more accurate than people? The honest answer is: it depends because “accuracy” changes meaning across domains, and comparisons only hold when the task and evaluation setup align.

This article compares AI vs human error rates and error profiles across nine domains: medical imaging/diagnosis, transcription (ASR), translation (MT), content moderation, fraud detection, manufacturing QA, customer support triage, software bugs, and driving perception. For each domain, you’ll see (1) a short insight paragraph on the typical error profile, followed by (2) brief examples of where AI failed, where humans failed, and where a hybrid workflow helped (when available). Throughout, any statements about “AI vs human” performance should be read as conditional on the specific task, dataset, and metric.

At-a-glance: what each tends to reduce

Domain-by-domain: insights and case briefs

1) Medical imaging and diagnosis

AI can be non-inferior to expert double reading in screening-style imaging tasks. In practice, pairing AI with clinicians can reduce missed findings while keeping false positives manageable.

The pattern that matters is error shape. Radiologists may miss subtle lesions due to fatigue or “satisfaction-of-search.” AI may miss cases that fall outside learned patterns or that sit near decision thresholds. Safety depends on how uncertainty is handled, and whether the workflow reliably triggers a second look when something is borderline.

Case Study - AI failed
In a Swedish mammography screening trial, the AI system occasionally assigned low suspicion to a subtle finding that a human reader flagged as concerning. The human’s concern triggered follow-up that identified a cancer the AI would likely have missed under that threshold setting. This is a classic systematic miss: borderline cases can slip through when the system is tuned for higher specificity. A practical mitigation is independent second reading (or structured review of uncertain negatives) so weak signals still get a human “second look.”

Case Study - Human failed
In a pathology example, an AI system highlighted a small focus of intestinal metaplasia that the original pathologist—and multiple independent human reviewers missed on initial review. The finding was confirmed only after the AI prompt triggered re-examination. This maps to a human vulnerability: subtle, low-salience patterns can be overlooked, especially under time pressure. Using AI as a second reader is a direct mitigation for small or rare signals.

Case Study - Hybrid approach
A hybrid failure mode can occur through automation bias: an AI chest X‑ray tool did not flag an early pneumothorax, and a less experienced clinician deferred to the “no acute findings” output despite noticing a faint sign. A senior reviewer later identified the issue. Here, an AI omission became a human miss because the workflow treated AI as authority. Mitigation is UI and training that preserve independent judgment and require escalation when a clinician has uncertainty.

2) Transcription (ASR)

Transcription has clear metrics, especially WER, but performance is highly condition-dependent. On clean audio, modern ASR can approach human-level WER. Under accents, elderly speech, and domain shift, error rates can become extreme far worse than typical human transcription.

Humans often make smaller, more random errors, but can use context to preserve meaning. The most reliable operational pattern is hybrid: ASR drafts, humans correct.

Case Study - AI failed
In a medical dialogue example (older patients, accented speech), ASR error rates were reported in a range where meaning can be distorted illustratively, one model’s WER was ~84% and a commercial system’s was ~59% in that setting. This isn’t a minor degradation; it’s a failure of reliability under real-world speech conditions. The failure mode is systematic mismatch: accents, elderly speech, and clinical vocabulary outside training data. Mitigation is treating ASR as draft-only and requiring human correction in high-stakes contexts.

Case Study - Human failed
A court transcription vignette describes a high-impact human error: “I do have an alibi” was transcribed as “I don’t have an alibi.” The mistake changed meaning and was only corrected after audio re-check. This shows that human transcription can fail catastrophically, not just cosmetically, especially under workload. Mitigation is verification on critical passages and routine audio review for ambiguity.

Case Study - Hybrid approach
In hybrid workflows, ASR produces a first draft and human editors correct it. Reported outcomes include around 5× faster turnaround, with final error rates <1% after human editing. The key point is not “AI is accurate,” but “AI makes the work editable,” letting human attention focus on meaning-critical corrections. Hybrid here is a reliability strategy, not just a productivity hack.

3) Translation (MT)

Translation quality is hard to collapse into a single number. MT systems often reach roughly 70–85% accuracy on straightforward content, versus ~95–100% for professional humans in similar settings. In one evaluated setting, GPT‑4 can be comparable to junior human translators in accuracy, while still lagging senior experts in stylistic finesse.

The error profile difference is predictable and dangerous if ignored. AI can be fluent yet wrong in meaning. Humans can introduce interpretive bias or inconsistency. The safest operating model is machine draft plus human post-editing, with quality gates for high-stakes use.

Case Study - AI failed
In a medical translation near-miss, an AI mistranslated dosage guidance (e.g., “once daily”), creating a potentially dangerous instruction. The error was caught before harm, but it illustrates a critical point: fluency is not safety. The failure mode is semantic precision small wording shifts can carry high consequence. Mitigation is mandatory verification for medical/legal translation and structured review processes.

Case Study - Human failed
Human translators can introduce interpretive shifts, especially in culturally sensitive or nuanced text—because humans optimize for tone and intent, not only literal meaning. In comparative evaluations, this can appear as omissions or meaning drift relative to a stricter reference. This isn’t “humans are worse.” It’s that humans are not neutral instruments. Mitigation is explicit translation standards (what the work is optimizing for) and independent review on sensitive content.

Case Study - Hybrid approach
A post-editing pitfall shows how hybrid workflows can fail if humans relax vigilance when MT output looks polished. A subtle meaning error in an otherwise fluent machine draft slipped through because the human treated the task like proofreading, not verification. This is automation bias in linguistic form. Mitigation is quality gates and reviewer training that assumes “looks good” can still be wrong.

4) Content moderation

Moderation is often “not directly comparable” because ground truth depends on policy and context. Still, the recurring pattern is consistent: AI operates at scale and catches obvious violations, but struggles with nuance. Humans handle nuance better, but show fatigue, inconsistency, and limited recall at volume.

The precision/recall trade-off is the center of gravity. Aggressive automation catches more harmful content but risks wrongful takedowns. Many platforms shift toward targeting clear-cut severe violations while routing edge cases through appeals or specialized review.

Case Study - AI failed
An artistic nude photograph was automatically removed despite policy allowing certain contextual nudity. It was later restored after appeal, illustrating an AI false positive where context matters. The error type is over-blocking with delayed correction. Mitigation is stronger context handling plus efficient escalation and appeals pathways.

Case Study - Human failed
Human inconsistency and inter-annotator disagreement show up on borderline cases: similar content can receive different decisions depending on reviewer judgment and workload. This produces uneven enforcement both missed violations and inconsistent removals. The failure mode is variability under ambiguity at scale. Mitigation includes structured guidance, calibration, and AI assist signals that standardize triage.

Case Study - Hybrid approach
When human review capacity is reduced, automation errors persist longer. During a period when appeals were constrained, far fewer mistaken removals were reversed compared to periods with normal appeals volume. The takeaway is simple: hybrid outcomes depend on the system, not just the model. Mitigation is tiered review so AI handles obvious violations and humans handle the contested, context-heavy cases.

5) Fraud detection

Fraud is a “needle in a haystack” domain: fraud is rare, and the cost of missing it is high. Traditional rule-based systems can generate ~95% false positives, overwhelming investigators and frustrating customers.

ML systems can materially improve precision and detection speed—especially by linking patterns humans miss. The operational frontier is workflow: AI prioritizes and correlates; humans adjudicate edge cases and manage customer impact.

Case Study - AI failed
An ML fraud model flagged many legitimate peer-to-peer payments (including rent), creating customer friction and mistrust. The error type is false positives triggered by pattern similarity (round amounts, “rent” memos) during heightened scam conditions. Mitigation included model tuning and shifting from auto-blocking to tiered verification for medium-risk cases. The lesson is that fraud accuracy and user experience are inseparable.

Case Study - Human failed
A human investigator approved small refund requests that looked plausible individually, missing they were part of a coordinated fraud ring. Humans tend to see queues; fraud often hides in networks. The failure mode is cross-case linkage miss under fragmentation. Mitigation is AI correlation across accounts, devices, and behavior patterns.

Case Study - Hybrid approach
In hybrid setups, AI flags and prioritizes likely fraud, and human analysts make final decisions—reducing alert volume while also reducing missed fraud. In several examples, AI surfaced non-obvious connections that changed the investigation outcome, but humans controlled the action. Hybrid here is both accuracy and harm-reduction: fewer false blocks, faster intervention when risk is real. The system works when thresholds map to downstream actions (block vs review vs notify).

6) Manufacturing QA (visual inspection / defect detection)

Manufacturing QA typically measures error through miss/escape rates and false rejects (scrap/rework), plus throughput impact. Human visual inspection error rates are reported around ~10–20%, driven by fatigue and subjectivity. Modern deep-learning vision systems can claim ~99% defect detection accuracy in controlled contexts.

A critical distinction: legacy rule-based AOI (high false alarms and misses) is not the same as modern AI vision. The best-performing pattern is a team-up model: AI scans 100%, humans verify flags and handle novel anomalies.

Case Study - AI failed
An electronics manufacturer’s older rule-based AOI system flagged ~15% of boards as defective, but only ~5% were truly defective after re-check, meaning ~10% were false positives that created bottlenecks. This is a failure of rigid pattern rules and poor context discrimination (noise vs real defects). Upgrading to a modern AI vision model reduced false rejects dramatically (to ~2%) while improving throughput. The key is not “automation,” but the difference between brittle rules and learned variation tolerance.

Case Study - Human failed
In one case, a small crack defect escaped because a human inspector missed a rare, subtle flaw in a high-volume setting. When AI was trained on images of the cracked parts, it detected the signature reliably, enabling 100% inspection with humans verifying and handling rework. The lesson is classic: rare, small-signal defects are exactly where humans are vulnerable. Mitigation is training AI on that signature and structuring verification rather than relying on vigilance.

Case Study - Hybrid approach
A composite materials plant implemented AI scanning for tiny weave defects but initially faced too many false defect flags when running AI alone. Switching to a hybrid approach - AI marking potential defects and a human confirming/overriding, achieved near-zero misses with minimal false rejects. AI finds everything; humans filter noise and keep operations practical. Hybrid works when humans retain ownership of the final call and feedback loops improve the model.

7) Customer support triage (routing / classification / priority)

Support triage errors show up as misroutes, misprioritization, and delays. Automated classifiers can materially improve routing accuracy and reduce misroutes, while also reducing assignment time from manual handling to near-instant tagging.

Humans add nuance—intent, urgency, and customer context—but under surges they become bottlenecks. A robust pattern is AI suggesting categories/priorities (ideally with confidence), and humans overriding on ambiguity.

Case Study - AI failed
A telecom deployed an AI ticket classifier that initially misrouted many outage reports as billing issues because the model overweighted keywords like “credit” and “refund.” In one outage incident, 50+ customers waited hours because tickets sat with the wrong team. The failure mode is brittle text classification without situational context and without a feedback loop. Mitigation included retraining on outage examples and adding a rule to auto-tag spikes of similar tickets as potential outages, plus a clear path for manual rerouting.

Case Study - Human failed
In one bank, manual triage created average delays of several hours before emails were categorized and forwarded. During surges, urgent fraud reports sat in general queues too long an error of omission/delay rather than incorrect labeling. The failure mode is 24/7 scale limits and inconsistent prioritization under workload. Mitigation was implementing real-time AI tagging so urgent cases are escalated within minutes.

Case Study - Hybrid approach
In hybrid support setups, an AI assistant handles common, repetitive queries and collects the right details up front, then escalates exceptions to a human agent. The AI’s role is to reduce delay and standardize intake; the human’s role is to apply judgment on edge cases, goodwill decisions, and emotionally sensitive situations. The benefit is not only faster response, but better allocation of human attention to the cases where it changes the outcome. Hybrid delivers speed and judgment when escalation rules are clear.

8) Software bugs (detection, review misses, assistance impacts)

Software has multiple “error surfaces”: bugs introduced, bugs missed in review, and false positives that prompt unnecessary changes. A key pattern is the precision/recall trade-off: static tools can have higher recall but lower precision (creating warning fatigue), while humans catch fewer issues but are more precise when they do.

There is evidence that AI coding assistance can increase bugs and vulnerabilities when used without guardrails speed rises, scrutiny falls. The practical takeaway is that software quality is a socio-technical problem: tools shift error distribution unless the workflow compensates.

Case Study - AI failed
In AI-assisted code generation and security tasks, AI suggestions produced insecure or buggy code at a meaningfully higher rate than baseline human work in that setting. The risk is “plausible code”: it compiles, looks standard, and still carries subtle vulnerabilities. The failure mode is learned repetition of seen-before patterns, including insecure idioms. Mitigation is strict human review, targeted security training data for models, and stronger automated testing.

Case Study - Human failed
Human code review inevitably misses defects, with illustrative ranges where review might catch only a fraction of bugs depending on time and expertise. This becomes especially risky under deadline pressure: missing a safety check or edge-case path can slip into production. The failure mode is bounded attention humans don’t exhaustively scan everything. Mitigation is process (release discipline, checklists) and tool support that surfaces high-risk areas.

Case Study - Hybrid approach
An emerging hybrid direction is straightforward: AI helps filter and prioritize static analysis warnings and can generate targeted tests, while humans apply judgment to a reduced, higher-signal set. The goal is to combine AI thoroughness with human context, improving signal-to-noise and reducing warning fatigue. Hybrid succeeds when “AI output” triggers more verification, not less. Without that shift, automation bias can turn tool errors into shipped defects.

9) Driving perception (ADAS/AV perception + safety proxy metrics)

Driving “error rate” is not a single perception metric. It is safety outcomes (crashes, injuries) and proxies (disengagements, interventions) within specific operating domains. Within constrained operational design domains (ODDs), some autonomous fleets report lower injury crash rates than general human driving averages in comparable areas - an important claim that remains contingent on operating domain and measurement method.

At the same time, AVs can fail in edge cases that humans handle routinely. And “safe fallback” behavior can still be a mission failure even if it avoids a crash. Comparability depends on ODD: environment matters as much as model capability.

Case Study - AI failed
“Phantom braking” describes a perception/decision failure where a system brakes hard despite no hazard, creating risk for following traffic. This is not a minor classification error; it’s an action-level failure with safety implications. The failure mode is conservative logic triggered by misperception or uncertainty. Mitigation includes model/sensor improvements and safer takeover pathways.

Case Study - Human failed
A core human driving error profile is that distraction, impairment, and aggression drive many high-severity crashes that automated systems, by design, avoid. Human drivers don’t “fail safe.” They improvise through confusion, sometimes successfully, sometimes with catastrophic results. The “error rate” in driving is ultimately measured in crashes and injuries, and humans produce a steady background rate of severe failures. Mitigation is assistance systems that reduce common human failure roots, while keeping the driver attentive where responsibility remains.

Case Study - Hybrid approach
A widely cited 2018 fatal crash case illustrates a combined failure: the automated system misclassified a pedestrian at night and did not brake in time, while the safety driver was distracted and did not intervene. This is a hybrid breakdown - AI perception failure plus human monitoring failure amplified by over-trust and weak engagement design. The mitigation theme is explicit: stronger driver monitoring, clearer responsibility boundaries, and interfaces that prevent complacency in partial-automation regimes.

Patterns and themes across domains

Error trade-offs (precision vs recall) are universal

Systems are rarely “just accurate.” They are tuned. Higher recall catches more true issues but increases false alarms; higher precision reduces false alarms but risks misses. Screening, moderation, and fraud make this visible: different stakeholders choose different equilibria depending on harm. Hybrid workflows can combine strengths AI casts a wide net, humans confirm—but only if review capacity and escalation design are real.

Humans excel at context, AI at scale

Humans interpret intent, nuance, and atypical situations. AI excels at high-volume, consistent scanning: every image, every ticket, every transaction, every frame. The risk is treating scale as understanding. The opportunity is using scale to feed human context rather than replacing it. Across domains, “AI as triage and second reader” is the pattern that best aligns with those strengths.

Human inconsistency vs AI consistency

Humans drift with fatigue, workload, and individual interpretation. AI delivers repeatable thresholds. That consistency is a capability, but it can be dangerous when the system is consistently wrong in a systematic blind spot. The practical implication is monitoring for systematic errors and maintaining feedback loops that let humans correct the model over time.

Over-reliance and automation bias

A recurring failure shows up in imaging, post-editing, and partial automation: humans lower vigilance when AI output looks confident or polished. Automation bias turns AI omissions into human misses. Mitigation is workflow design: show uncertainty, require confirmation on critical items, and train teams to treat AI as input—not authority.

Data shifts and adversarial behavior

AI error rates degrade when inputs shift: new dialects, new fraud tactics, new product designs, novel road situations. Humans are generally more adaptable to novelty, while AI often needs retraining or robust design to handle distribution shift. In adversarial settings like fraud and moderation, attackers adapt to what models detect. Hybrid systems can be more resilient because humans can recognize “this feels off” even when it doesn’t match known patterns.

Error impact and mitigation matter as much as the rate

A 1% error can be trivial in one domain and catastrophic in another. False positives in fraud harm trust. False negatives in medicine risk lives. False positives in moderation silence creators. False negatives in driving can be fatal. Designing the mitigation layer, escalation, verification, uncertainty thresholds, safe fallbacks is often the real differentiator.

“Not directly comparable” scenarios

“AI vs human” comparisons only hold cleanly when tasks, metrics, and ground truth align. Many settings don’t, either because ground truth is partly normative (moderation) or because operating conditions vary dramatically (audio quality, driving environments). In those cases, raw error-rate comparisons mislead. Error profiles - what each tends to miss, what each tends to overcall, and how those patterns shift under stress—are the more actionable lens.

Final words

AI can reduce certain types of human error, especially random and large-scale errors, while introducing new error modes (systematic misses, edge-case brittleness, and automation-driven human lapses). Where tasks, data, and metrics are directly comparable, there are cases where AI can match—or, in some settings, exceed—individual humans on narrow perceptual and classification tasks. Those results should be interpreted within the bounds of the evaluation setup and not generalized beyond it without caution.

The decisive factor is system design. Translating benchmark improvements into real-world outcomes requires thresholds, monitoring, escalation paths, and training that anticipates failure—not just success. The best results come from harnessing complementarity: let AI do what it’s good at (high-volume, high-consistency detection and triage) and let humans do what they’re good at (judgment, verification, handling novelty, and values-based trade-offs). The future isn’t “AI replaces people.” It’s organizations building better socio-technical systems—where humans stay meaningfully in the loop, and the combined system is designed to be safer, more resilient, and more accountable than either could be alone.

Reverse Prompt Engineering Framework (RPEF) - Deliver Consistent Results with LLMs

Alex Velinov — Sat, 27 Dec 2025 10:27:17 GMT

TL;DR
Getting one good answer from an LLM is easy. Getting consistently good answers is hard. Reverse Prompt Engineering is a practical way to turn successful outputs into reusable systems, letting LLMs handle structure and repetition while humans focus on judgment and context.

Why Consistency With LLMs Is Still Hard

Many professionals using tools like ChatGPT, Gemini or Claude on daily basis. The real challenge is no longer adoption, it is reliability.

Most teams have experienced the same pattern. An LLM produces a great result once, but the next attempt requires rewrites, prompt tweaking, and manual correction. Over time, the effort shifts from doing the work to managing the prompts.

High-quality output still depends heavily on human input. Refinement, iteration, and careful phrasing remain essential. This is where many people feel stuck. They do not want to become prompt engineers, yet they need consistent results.

Based on hands-on testing in real workflows, there is a more effective way to approach this problem.

Instead of starting with prompts, start with outcomes.

That shift in thinking is the foundation of the Reverse Prompt Engineering Framework (RPEF).

What Is Reverse Prompt Engineering?

Reverse Prompt Engineering is a method that works backwards from proven results.

Rather than asking how to write the perfect prompt, it asks a different question:

How can an LLM recreate outputs like this again when given similar inputs?

By using real inputs and real outputs, the LLM can infer structure, expectations, and decision logic on its own. The result is a reusable meta-prompt that captures how the task should be done.

This turns one successful example into a repeatable system.

The Reverse Prompt Engineering Framework (RPEF)

The framework consists of two clear stages. Together, they transform one-off success into consistent performance.

Stage One: Generate a Reusable Meta-Prompt

Every knowledge task follows the same basic flow. You start with inputs, you perform some form of reasoning or synthesis, and you produce an output you are confident sharing.

Stage One assumes you already have a strong example of completed work.

You take the original inputs and the final output and ask the LLM to reverse-engineer the prompt that could reliably produce similar results. The LLM analyses what went in, what came out, and how decisions were implicitly made.

The result is a reusable meta-prompt.

A meta-prompt is not content. It is instruction. It encodes structure, tone, constraints, quality thresholds, and decision rules that would otherwise live only in a human’s head.

Use following prompt to generate Meta Prompt

You are an expert Prompt Engineer.
Your task is to **reverse-engineer a reusable prompt** using:
- Raw input data (provided below)
- A desired output (also provided below)
---
## Instructions
1. Analyze the relationship between the input and the output.
2. Infer tone, structure, logic, and formatting patterns.
3. Identify the essential information needed to recreate the output.
4. Ask clarifying questions if any data is missing or unclear.
5. Proceed only when you are **90% confident** in your understanding.
6. Write a **copy-paste-ready prompt** that can generate similar output from similar input.
---
## Provided by the User
**INPUT DATA (raw):**
{Paste raw input here}
---
**OUTPUT (desired result):**
{Paste example output here}
---
## Your Response
Return a reusable prompt that:
- Replicates the tone, style, and structure of the output.
- Works with new, similar inputs.
- Includes placeholders for user input.

Once created, this meta-prompt becomes an asset. It can be reused whenever the same type of task appears again.

Stage Two: Reuse the Meta-Prompt to Generate Outputs

Stage Two is where the actual job is done.

Instead of starting from a blank page, you start with the reusable meta-prompt and new inputs. The LLM may ask for clarification or additional context, which you provide. It then generates a first-pass output aligned with the original quality standard.

At this point, the human role changes. You are no longer drafting from scratch. You are reviewing, adjusting, and applying context.

This separation matters. LLMs excel at structure and repetition. Humans excel at judgment and nuance.

Real-World Example: Press Releases

This framework has been tested in live production work.

In one case, I worked with a team from PR agency, responsible for press releases used real client inputs and the final press releases sent to media outlets. These were provided to an LLM to generate a reusable meta-prompt for press release creation. This end up with a Meta Prompt with a length of around 1000 words

Once the meta-prompt was created, the same inputs were reused. The LLM generated a new press release using only the meta-prompt.

The output was reviewed by the professionals who normally write these releases. They rated the result 7 out of 10.

That score is important. It was not perfect, but it was good enough to act as a strong foundation. With human editing and contextual awareness, the team could efficiently turn it into a 10 out of 10 final release.

The value was not replacement. It was speed, consistency, and reduced cognitive load.

A Few Examples and Ideas

The same approach applies to many repeatable knowledge tasks.

Internal strategy summaries can use research notes and meeting transcripts as inputs, producing consistent executive-ready outputs.

Marketing or campaign briefs can follow the same structure every time, regardless of who prepares them.

Educational or explainer content can maintain tone, depth, and clarity across multiple pieces without constant prompt tweaking.

In each case, the principle remains the same. One strong example becomes the blueprint for many.

Final Thoughts: A Smarter Way to Work With LLMs

LLMs are becoming more capable, but the real shift is happening in how we use them.

Reverse Prompt Engineering shows that consistency does not require prompt engineering expertise. It requires strategic thinking and a willingness to work backwards from success.

This approach allows LLMs to handle the heavy lifting of structure and repetition, while humans stay focused on judgment, creativity, and context.

If you try this framework, test it in your own work. Notice where it saves time and where it still needs human input.

Most importantly, share what you learned.

5 Principles to Be AI‑Bulletproof

Alex Velinov — Fri, 12 Dec 2025 09:10:12 GMT

Artificial intelligence has quietly crossed an important threshold. It is no longer experimental, optional, or confined to technical teams. It is becoming a foundational layer of how work gets done.

For professionals across industries, this shift often triggers a familiar concern: What does this mean for my career? The answer is neither panic nor blind optimism. Becoming “AI-bulletproof” is not about competing with machines, but about learning how to work alongside them in ways that amplify human value.

The following five principles are not technical prescriptions. They are practical, experience-led ways of thinking and working that help individuals remain resilient, relevant, and adaptable as AI becomes part of everyday professional life.

1. Embrace an AI-First Mindset

An AI-first mindset begins with a simple change in how problems are approached.

In the past, problem-solving relied almost entirely on individual expertise and manual effort. Today, a more effective starting point is to ask where AI might support the process. Large problems can be broken into smaller components, and many of those components—research, drafting, summarization, pattern recognition—can be handled or accelerated by AI.

This does not diminish human intelligence. On the contrary, it creates space for higher-value thinking. Judgment, creativity, and decision-making become the focus, while AI handles parts of the work that are repetitive or time-consuming.

An AI-first mindset is not about automation for its own sake. It is about designing smarter ways to think and work.

2. Redesign Workflows

Adding AI tools to outdated workflows rarely delivers meaningful impact. Real gains come from redesigning how workflows from start to finish.

Every workflow is a combination of steps—some sequential, others parallel. To become AI-bulletproof, those steps must be deconstructed and redesigned. The question is not whether AI can be used, but where and how it adds the most value.

When workflows are rebuilt with AI in mind, the result is often faster execution, improved consistency, and higher-quality outcomes. In many cases, the redesigned workflow looks fundamentally different from the original, not just incrementally better.

3. Dedicate Time to Learn

AI evolves quickly, and the sheer volume of information can feel overwhelming. Trying to keep up with everything is neither realistic nor necessary.

What matters more is consistency. Setting aside a small, regular amount of time—whether 30 minutes or an hour per week—creates a sustainable learning rhythm. This time can be used to test tools, explore use cases, and think critically about how AI fits into one’s own work and life.

Over time, this deliberate practice builds fluency. Learning stops feeling reactive and becomes part of a long-term professional habit.

4. Experiment and Fail

AI is powerful, but it is not magic. Expecting perfect results from the outset leads to disappointment.

Progress comes from experimentation, and experimentation includes failure. Getting hands-on with AI means trying approaches that do not work as expected, learning from the experience, and refining how tools are used.

Failure in this context is not a setback. It is feedback. Each experiment reveals something about the limits, strengths, and behaviors of the technology. Those insights compound quickly for people willing to engage directly rather than observe from a distance.

5. Convert from AI User to AI Builder

Using AI as instructed is a starting point, not an endpoint.

An AI user follows predefined paths. An AI builder begins to shape tools around real problems. This might involve creating simple custom workflows, adapting prompts to personal needs, or assembling small AI-driven systems that improve efficiency and decision-making.

Becoming a builder does not require deep technical expertise or engineering skills. It requires a mindset shift—from consuming tools to creating solutions with them.

Builders are difficult to replace because they understand how to adapt AI to context. As organizations increasingly rely on AI, this ability becomes a lasting source of professional value.

Final Words

Becoming AI-bulletproof is not about resisting change or trying to outpace machines. It is about understanding how the nature of work is evolving—and choosing to evolve with it.

An AI-first mindset, redesigned workflows, consistent learning, a willingness to fail, and the shift from user to builder are not technical strategies. They are human ones. They reflect curiosity, adaptability, and ownership over how we work and grow.

AI will continue to advance, regardless of individual hesitation or enthusiasm. The real question is how each of us decides to engage with it. Those who lean in thoughtfully, experiment openly, and build with intention will not just remain relevant—they will help shape what comes next.

The future is not something to wait for. It is something to participate in.

AEO, GEO and LLMO: 22 FAQ for AI-Driven Discovery

Alex Velinov — Fri, 05 Dec 2025 06:42:28 GMT

TL;DR — What This Guide Covers

This FAQ explains the fundamentals of AEO, GEO, and LLMO. What they are, why they matter, and how to optimize your content for AI-powered discovery. You’ll learn:

The core differences between AEO, GEO, and LLMO, including how they target featured snippets, AI-generated responses, and model-level understanding.
How to structure content so AI can extract and cite it as the definitive answer.
The exact formats that work best for answer engines and LLMs, like FAQs, how-tos, comparison tables, and list structures.
Key tactics for accuracy, personalization, and conversational queries.
Why schema markup matters — including the most important structured data types.
How to measure success using new metrics like citation frequency, authority, and share of voice in AI responses.
Team skills required to execute AI search optimization effectively.

In short: this guide teaches you how to be selected, cited, and trusted by AI, not just ranked by search.

Fundamentals & Definitions

What is Answer Engine Optimization (AEO) and how does it differ from traditional SEO?

Answer Engine Optimization (AEO) structures content so AI-powered answer engines can directly extract and present it as definitive responses rather than just listing links. While traditional SEO focuses on ranking web pages to drive clicks through keywords, backlinks, and click-through rates, AEO prioritizes becoming the single answer in zero-click environments like featured snippets, voice assistants, and AI Overviews.

Key differences:

SEO = optimize for rankings and clicks
AEO = optimize for direct answer extraction and zero-click satisfaction
AEO requires immediate, concise answers (first 2-3 sentences), clear hierarchical headings, FAQ/HowTo schema markup, and conversational language that voice assistants can read naturally

What is Generative Engine Optimization (GEO) and why is it important?

Generative Engine Optimization (GEO) is the strategic process of adapting digital content to improve visibility within AI-generated responses from platforms like ChatGPT, Gemini, and Perplexity. GEO ensures your brand is cited, mentioned, and recommended when AI systems synthesize information from multiple sources to answer complex queries.

Importance: As AI-driven search becomes the primary discovery method, GEO is critical for maintaining brand authority and capturing user attention in an environment where traditional rankings matter less than being referenced by trusted AI systems. Success is measured by citation frequency, attribution accuracy, and share of voice in AI responses rather than search result positions.

What is Large Language Model Optimization (LLMO) and why is it becoming essential?

Large Language Model Optimization (LLMO) tailors content, brand presence, and technical infrastructure to appear in AI-generated responses from large language models. LLMO focuses on making content retrievable, quotable, and referenceable by training AI systems to recognize your brand as an authoritative source through semantic clarity, entity recognition, and structured data.

Essential because: With zero-click searches now accounting for the majority of US queries, LLMO is critical for future-proofing visibility as users increasingly rely on conversational AI rather than traditional search results. LLMO ensures your content is represented accurately in AI-generated responses and maintains competitive position as AI becomes the primary interface for information retrieval.

What are the core differences between AEO, GEO, and LLMO?

AEO — Targets featured snippets, voice search, and direct answer boxes to become the direct answer to specific queries, measured by position zero, answer box placement, and voice assistant selection.
GEO — Targets AI-generated responses (ChatGPT, Gemini, Perplexity) to be cited as a source within AI-generated narratives, measured by citation frequency, share of voice, and mention quality.
LLMO — Targets underlying language model mechanisms to influence how models fundamentally understand and process your content, measured by model-level knowledge representation, entity recognition, and training data inclusion.

Scope progression: AEO targets answer extraction → GEO targets citation in generated responses → LLMO targets model-level influence through semantic relationships and vector embeddings.

How does AEO specifically target zero-click and voice search results?

AEO targets zero-click searches by providing immediate, concise answers within the first 2-3 sentences of content, using question-based headers and structured data that AI engines can extract without requiring users to click through.

Voice search optimization:

Use conversational language patterns and natural speech queries
Implement FAQ schema and “How-To” guides with step-by-step instructions
Create long-tail question phrases that voice assistants like Siri and Alexa can easily read aloud
Ensure mobile responsiveness and fast-loading pages
Use Speakable schema for news content

How do “citations” and “mentions” replace “rankings” as the success metric in GEO?

In generative AI environments, traditional page rankings become irrelevant because AI models synthesize information from multiple sources and present cohesive responses rather than lists of links. Success is now measured by:

Citation frequency: How often your brand/content appears in AI-generated answers
Attribution accuracy: Whether AI properly credits your brand
Share of voice: Your brand’s presence versus competitors in AI responses
Positioning: Being the first cited source represents the new “Position 1”

These metrics measure brand authority and visibility within AI answers, reflecting the shift from traffic-driven to awareness-driven optimization.

Content Strategy & Optimization

How do I structure my content to appear in AI-generated answers?

Structure content with immediate direct answers in the first 2-3 sentences, followed by comprehensive supporting details. Use clear hierarchical headings (H1 → H2 → H3) that mirror natural language queries.

Implementation:

Place direct answers near the top using concise paragraphs of 40-60 words
Use the inverted pyramid style where key information comes first
Implement question-and-answer formats, bulleted lists for easy extraction, summary boxes, and table formats for comparative data
Include clear topic sentences, use semantic HTML markup, and ensure each section can stand alone as a complete answer

Pro tip: Content with hierarchical headings gets cited nearly 3× more often than unstructured content.

What content formats are most effective for answer engines and generative AI?

Most effective formats:

FAQ pages and Q&A sections: Mirror how users ask questions directly
How-to guides with step-by-step instructions: AI can easily extract sequential information
Comparison tables: Excellent for summarizing differences or pros/cons
Listicles and bullet-point lists: 80% of articles cited by ChatGPT contain lists vs only ~28% of top Google results.
Definition paragraphs: Concise definitions (e.g., “X is defined as...”) can be quoted directly for “What is X?” queries
Long-form comprehensive guides: Demonstrate topical authority
Multi-modal content: Videos with transcripts, infographics with descriptive alt text, audio with transcripts

What specific writing styles or patterns do LLMs best understand?

LLMs best understand conversational, natural language that uses complete sentences and mirrors how people actually ask questions.

Best practices:

Use question starters like “Who,” “What,” “Where,” “When,” “Why,” and “How”
Keep paragraphs short and factual (2-3 sentences) that state facts clearly and early
Use simple, direct language; avoid jargon without explanation
Include clear entity definitions and consistent terminology
Use active voice and explicit logical connections with transition words
Format key terms with bold or italics to signal importance
Cite authoritative sources within your text to build trust
Write with high “perplexity” (uniqueness) but low “burstiness” (consistency) for machine comprehension

How can brands optimize content for conversational queries in AI chat agents?

Create content that anticipates follow-up questions and provides comprehensive, contextually rich answers that address user intent beyond the initial query.

Optimization tactics:

Conduct thorough question research using People Also Ask, Reddit threads, Quora, and forums
Incorporate natural-language questions verbatim into your content as subheadings
Adopt a conversational tone using active voice and second person (”you”)
Include practical examples that demonstrate application of concepts
Structure content to flow logically through related topics
Address long-tail, multi-part questions that include specific details
Build topical authority through interconnected content clusters demonstrating expertise across related subjects

Should I optimize existing content or create new content for AEO/GEO/LLMO?

Do both. Audit and optimize high-performing existing content first to maximize immediate gains, then create new content to fill gaps in topic coverage and target emerging queries.

Optimization approach:

Existing content: Add concise answer sections, FAQ blocks, schema markup, entity clarification, and updated information
New content: Address conversational queries not currently covered, demonstrate unique expertise, and build topical authority
Prioritize cornerstone content and high-traffic pages for optimization while simultaneously developing new content for visibility gaps

How do personalization and user intent affect AI-driven visibility?

AI models increasingly tailor responses based on user context, conversation history, and inferred intent, making it essential to create content that serves multiple intent types (informational, navigational, transactional).

Impact on strategy:

Cover multiple intent angles in your content (e.g., “for beginners... for advanced users...”)
Use phrasing that aligns with intent categories (”how to” for informational, “best X” for commercial investigation)
Ensure content is comprehensive enough to match various user contexts while maintaining clear topical focus
Build breadth of content (basic to advanced topics) so AI can select the piece that best fits the user’s level
Align content precisely with user intent to increase selection likelihood

How do I ensure content accuracy when LLMs summarize or paraphrase it?

Write with extreme clarity using unambiguous language, clearly delineated facts, and explicit statements that leave no room for misinterpretation.

Accuracy safeguards:

Be explicit and clear; avoid ambiguous metaphors or complex jargon
Provide context for statements to make them self-contained
Include clear disclaimers and date stamps for time-sensitive information
Use consistent terminology and proper attribution for claims
Structure key information to resist distortion through paraphrasing
Regularly update content to ensure accuracy and use fact-check markup where appropriate
Limit complex, compounded sentences; break up long sentences with multiple clauses

What skills or team roles are required to execute LLMO effectively?

Successful LLMO requires cross-functional collaboration between specialized roles:

Core team composition:

Content strategists who understand AI behavior and semantic relationships
Technical SEO specialists familiar with structured data, schema implementation, and entity markup
Subject matter experts who create authoritative, information-rich content
Data analysts who track AI citations, mentions, and performance metrics
Developers who implement technical optimizations and ensure crawlability
Brand strategists who monitor share of voice and ensure consistent authority-building

Key requirement: Seamless collaboration between content, SEO, engineering, and product teams for comprehensive implementation.

Technical Implementation

Which answer engines and AI platforms should brands prioritize?

Prioritize the “big four” platforms that command the largest user bases:

Google AI Overviews and SGE - reaches all Google users
ChatGPT - dominant conversational AI with search capabilities
Microsoft Copilot - integrated into Office ecosystem
Perplexity AI - growing research-focused platform

Selection criteria: Choose based on your target audience’s usage patterns. Monitor emerging platforms like Claude and Gemini, but focus resources on platforms that currently drive the majority of AI-driven discovery. Industry-specific platforms may also warrant attention (e.g., AI coding assistants for technical brands).

What role does schema markup and structured data play in AEO/LLMO success?

Schema markup is critical for AEO/LLMO success as it provides explicit, machine-readable context that helps AI systems accurately understand and categorize your content’s meaning, relationships, and key information.

Impact:

Acts as a “definitive translator,” explicitly telling the AI “this text is a price” or “this person authored this article,” removing ambiguity
Dramatically increases likelihood of content being featured in rich results, answer boxes, and AI citations
Enables AI to quickly identify entities, facts, relationships, and content types without interpreting unstructured text
Reduces ambiguity and increases extraction accuracy compared to sites without structured data.

What structured data types are critical for AI-driven search understanding?

Most critical schema types:

FAQPage and HowTo - ideal for direct answer extraction
Article with author and organization markup - establishes authority
Product, Review, Event - essential for commercial queries
Organization and Person - establishes entity relationships and authority
BreadcrumbList and SiteNavigationElement - helps AI understand site architecture
Speakable - specifically targets voice search
ClaimReview - for fact-checking content
Dataset - for structured information
sameAs references - establish entity identity across the web
Specialized schemas like MedicalCondition or Recipe - provide domain-specific clarity

Measurement, ROI & Competitive Strategy

How do AI Overviews impact organic search traffic?

AI Overviews significantly reduce click-through rates to websites by providing comprehensive answers directly in search results, potentially decreasing traditional organic traffic by 20-60% for informational queries. However, the impact varies:

Informational queries: Experience the steepest traffic declines
Transactional queries: Maintain better click-through rates as users seek to complete purchases
Brand benefit: Websites cited in AI Overviews can experience credibility boosts, increased branded searches, and higher-quality traffic from users seeking deeper information.

How can GEO influence website traffic and conversions?

GEO typically reduces direct website traffic from information-seeking queries since users receive answers without clicking through, but it significantly increases brand awareness, authority, and indirect traffic.

Conversion impact:

When AI models cite your brand, you gain credibility that leads to branded searches, social mentions, and higher conversion rates
Users who do click through arrive with greater intent and are further along in their decision journey
Focus shifts from traffic volume to traffic quality, with better-qualified leads
Brand mentions in AI answers build trust throughout the buyer’s journey, influencing purchase decisions even without immediate clicks.

Should GEO and SEO use separate content strategies?

No. Develop an integrated approach where content simultaneously serves traditional SEO and GEO objectives through comprehensive, authoritative coverage with flexible formatting.

Unified strategy benefits:

Core principles (quality, relevance, E-E-A-T, user value) apply to both
Prevents conflicting optimization efforts while maximizing visibility across all discovery channels
Tactical implementations may vary: schema markup, content structure, and optimization priorities can be adapted for each paradigm
Most effective approach optimizes content for both paradigms simultaneously.

How important is topical authority for LLMO visibility?

Topical authority is critically important for LLMO because AI models favor content from sources that demonstrate consistent expertise, comprehensive coverage, and credibility within specific subject areas.

Building authority:

Create extensive, interlinked content clusters on related subjects
Signal to AI models that your site is a definitive source worthy of citation
Depth and breadth of coverage on focused topics outperforms scattered content across unrelated areas
Without clear topical authority, AI models may not recognize your brand as a trusted source, reducing citation likelihood even for individual high-quality pages.

How should companies align SEO, GEO, and LLMO into one strategy?

Create a unified content excellence framework where high-quality, authoritative content serves as the foundation for all three optimization types, with tactical variations in formatting and technical implementation.

Integration approach:

Develop integrated workflows where content teams simultaneously optimize for traditional search visibility, AI citation potential, and long-term model training influence
Establish shared KPIs around brand authority, content comprehensiveness, and multi-channel visibility rather than siloed metrics
Ensure technical teams implement solutions that serve all optimization objectives simultaneously (schema markup, entity clarity, site structure)
Track combined metrics including organic traffic, AI citations, brand mentions, and assisted conversions.

The New AI Shopping Era: How Gemini, ChatGPT and Perplexity Are Redefining Retail

Alex Velinov — Fri, 28 Nov 2025 06:40:35 GMT

Artificial intelligence has entered a decisive new era in retail - one where AI no longer simply supports product research but actively participates in the shopping journey. In 2025, three flagship platforms - Google Gemini, ChatGPT, and Perplexity AI - launched capabilities that turn discovery, comparison, and even checkout into smooth, conversational experiences. Instead of navigating endless tabs, reviews, and product grids, consumers now articulate what they want in natural language and receive personalised, actionable results instantly. To help you navigate this fast‑moving landscape, this article breaks down the newest shopping features in Gemini, ChatGPT and Perplexity, offers real‑world prompt examples, and explores how AI agents will reshape the future of retail.

Google Gemini - Conversation, Automation & “Buy‑For‑Me”

Google Gemini has quickly become the most action‑driven AI shopping assistant, shifting from traditional search to tasks that previously required human effort.

Agentic Checkout

Google’s launch of agentic checkout represents the clearest example of AI taking direct action on a shopper’s behalf. As of late 2025, users can instruct Gemini to automatically purchase a product once specific conditions - such as price drops or restock events - are met. The purchase is then completed using Google Pay, making shopping less about waiting and more about delegation.

AI‑Powered Store Calling

Another standout feature is Gemini’s ability to call physical stores to check real‑world inventory. Using enhanced Duplex technology, Gemini verifies stock levels, confirms pricing, and summarises store responses. This feature bridges the gap between online search and offline retail, reducing friction during high‑demand seasons.

Conversational Search With Shopping Graph Intelligence

Gemini’s interpretive power comes from Google’s vast Shopping Graph, which maintains billions of updated product listings. Users can describe exactly what they want - “a cozy leather jacket under £150, dark green, size M” and Gemini returns curated products with rich detail, including images, availability, and shipping options. This natural language flexibility marks a major shift from the rigid keyword searches of traditional ecommerce.

From Browsing to Buying in One Flow

When combined, these capabilities turn Gemini into a full shopping agent. A single conversation can now lead from inspiration to inventory checking to automated purchasing - all without switching apps or opening retailer websites. It’s a seamless, modern, AI‑first buying experience.

In short: Google isn’t just helping you search - it’s helping you shop.

ChatGPT - Shopping Research, Personal Guidance & Soon: Checkout

ChatGPT has evolved rapidly from a Q&A engine into a high‑context shopping advisor that understands nuance, preferences, and personal constraints.

Shopping Research Mode

Introduced in November 2025, Shopping Research Mode offers curated, digestible buyer’s guides based on user queries. Whether the question is “What’s the quietest cordless vacuum for a small flat?” or “What’s a great gift for a 4‑year‑old art lover?”, ChatGPT pulls from product specs, expert reviews, user insights, and pricing to build a personalised overview. It’s available to free and paid users and supports heavy seasonal usage.

Shopping research in ChatGPT

Contextual, Conversational Guidance

ChatGPT avoids product grids and instead guides users through a conversation. It asks clarifying questions “Is battery life or weight more important?” to refine selections. This makes the process feel personalised and intuitive, especially for users who dislike comparison shopping.

Checkout Coming Soon

While Shopping Research Mode doesn’t yet include in‑chat purchasing, OpenAI’s partnership with PayPal - via the new Agentic Commerce Protocol - means checkout is coming soon. Once live, ChatGPT will function as a complete pre‑purchase and purchase hub.

ChatGPT is evolving into a personalised advisor that brings clarity to complex buying decisions.

Perplexity AI - From Search to Instant Buy With Privacy‑First Design

Perplexity has taken a lighter, speed‑focused approach. It serves users who want fast answers, clean interfaces, and minimal noise.

AI-powered shopping assistant

In November 2025, Perplexity launched its own shopping assistant that can deliver product suggestions tailored to you. Perplexity’s AI-powered shopping assistant is designed for speed, clarity, and minimal friction, offering one of the cleanest end-to-end shopping experiences available today.

Shop with Perplexity: AI-Powered, Personal, Effortless

Instant Buy Checkout

Perplexity’s Instant Buy feature integrates PayPal directly into the chat interface. When a supported retailer appears in results, users can complete the purchase in seconds - no new tabs, no account creation, no complexity.

Conversational & Context‑Aware Discovery

Perplexity retains conversation context, allowing users to refine product needs fluidly. They might begin with “winter coat under £120” and then adjust to “make it waterproof, minimalist, with a hood,” and Perplexity responds with updated results instantly.

Curated Product Cards

Unlike traditional ecommerce search results cluttered with sponsored listings, Perplexity presents clean, digestible product cards with essential details only. This improves readability and speeds up comparisons.

End‑to‑End Flow

Perplexity now offers one of the fastest discovery‑to‑checkout experiences on the market, especially for US shoppers. The platform even highlights holiday deals and PayPal perks during peak seasons, further reducing friction.

Perplexity delivers an efficient, privacy‑forward shopping workflow built for speed.

Prompt Examples to Use While Shopping in LLMs (3 Quick Ones)

Product Discovery

“Help me find a lightweight laptop under £900 that is suitable for travel and offers long battery life.”

Visual Lookalike Search

“Find jackets similar to this photo, but waterproof and under £120.”

Deal Hunting

“Find today’s best deal on Nike Pegasus running shoes and tell me if any student or loyalty discounts apply.”

👉 Download the full AI Shopping Prompt Pack (10 categories + 30 examples)

How the Future Looks: AI Agents Will Transform Shopping

Personal AI Shopping Assistants

AI shopping agents will soon function like hyper‑personalised concierges. They will understand sizes, style preferences, past purchases, and lifestyle habits - allowing them to proactively curate items. Shopping will begin with a conversation, not a search bar.

Autonomous Buying and Deal Hunting

Automation will define the next decade of retail. AI agents will continuously monitor prices, compare specifications, scan reviews, and recognise sales cycles. When a product meets your criteria, the assistant will handle the purchase automatically.

AI‑Driven Retail Websites and In‑Store Experiences

Retailers will embed AI into their websites for instant product help, guided comparisons, and personalised bundles. In physical stores, AI‑powered augmented reality will overlay product data and guide shoppers directly to the right aisles.

Hyper‑Personalisation at Scale

Future AI agents will adjust to your mood, life events, travel plans, and even household inventory. This level of personalisation will feel bespoke, evolving with every interaction.

Seamless Online‑to‑Offline Journeys

AI will merge online research with in‑store shopping. Assistants will guide users to products in - store, highlight local alternatives, check availability, and coordinate pick‑up or return options.

New Commerce Standards and Infrastructure

To support these capabilities, new commerce protocols will emerge - enabling secure AI‑driven checkout, unified product data exchange, and integrations with frameworks like Stripe’s Agentic Commerce Protocol.

Final Words

Shopping has entered a new era defined by clarity, speed, and intelligence. The traditional cycle of opening countless tabs, comparing reviews, and hunting for deals is disappearing. Google enables automated purchasing and real‑world stock verification. ChatGPT offers deep, personalised buyer’s guidance. Perplexity provides fast, distraction‑free answers with in‑chat checkout.

Across all platforms, one theme is unmistakable: the future of shopping is conversational. Consumers simply describe what they want - AI handles the rest. The process becomes quicker, easier, and more elegant than ever before.

The 6% Blueprint: Seven Traits That Separate AI High-Performing Companies from the Rest

Alex Velinov — Fri, 21 Nov 2025 06:31:04 GMT

Artificial intelligence has become a defining force of modern business. According to McKinsey’s State of AI 2025 study, one of the most comprehensive examinations of global AI maturity 88% of organizations now report regular AI use in at least one business function. Adoption is widespread, curiosity is strong, and experimentation is everywhere.

But beneath this enthusiasm lies a stark and revealing truth: most companies are not yet realising meaningful value from AI. They’re experimenting, piloting, or testing-but not transforming. The study, which surveyed 1,993 leaders across 105 countries, set out to understand exactly why. Its goal was to identify how AI is being used, where impact is being created, and what separates those capturing value from those falling behind.

The answer lies in a single, powerful insight: only 6% of companies achieve significant, enterprise-level returns from AI. These organisations, labelled AI High Performers are not simply using AI more. They are behaving differently, making bolder choices, and designing their organisations for a future in which AI is embedded at the core of their operations.

This article focuses solely on that top 6%, because their behaviours reveal what’s possible when ambition, leadership, and disciplined execution align.

7 Traits of AI High Performers

Before we explore each trait in detail, it’s important to recognise that these behaviours are not accidents. They are intentional choices that shape how High Performers approach AI. In the sections that follow, we examine the seven characteristics that set these organisations apart and what they mean for any leader ready to elevate their AI strategy.

1. They Aim for Transformation, Not Incremental Efficiency

Most companies approach AI with modest goals: improve a workflow, automate a few tasks, or reduce small percentages of cost. High Performers take a radically different approach. They pursue transformation. They look at AI not as a tool for tweaking the edges of the business but as a catalyst for reimagining it entirely.

The data is clear: High Performers are 3.6× more likely to target significant, enterprise-wide transformation. This ambition acts as an accelerant. It shapes priorities, focuses investment, and inspires teams. It becomes a forcing mechanism that pulls the organisation toward a more innovative future.

2. They Redesign Workflows End-to-End

Where most organisations attempt to insert AI into existing processes, High Performers rebuild the processes themselves. They recognise that AI cannot deliver transformative impact if it is layered on top of outdated, inefficient, or overly complex workflows.

This group is nearly 3× more likely to fundamentally redesign the way work gets done. They construct new workflows where AI and humans interact seamlessly, where value is maximised, and where inefficiencies are eliminated rather than inherited.

Workflow redesign is one of the strongest predictors of value in the entire study. It’s not about adding AI-it’s about designing the business around it.

3. Their Leaders Personally Own the AI Agenda

In most companies, AI lives inside IT, strategy, or digital teams. But in High Performer organisations, AI is a leadership priority. Senior leaders don’t delegate AI-they embody it.

These leaders are 3× more likely to champion AI initiatives, model its usage, and invest consistently. They set the tone, create urgency, and reinforce AI as a strategic enabler rather than a technical curiosity. When leaders lean in, teams follow. When leaders hesitate, the organisation stalls.

Leadership ownership is one of the clearest signals of future AI success.

4. They Scale AI and Agentic Systems Across Functions

While most organisations conduct pilots, High Performers scale. They move quickly from experimentation to enterprise-wide deployment, integrating AI across multiple business functions.

They are at least 3× more likely to be scaling AI agents across areas like finance, operations, product development, and marketing-far beyond the <10% adoption levels seen in general industry. This commitment to scale allows them to capture compounding returns that pilot-bound organisations never experience.

Scaling isn’t a technical challenge; it’s an organisational discipline. High Performers excel at both.

5. They Industrialise Human-in-the-Loop

High Performers recognise that humans play a critical role in AI accuracy, trust, and decision-making. But rather than relying on informal oversight, they build structured, disciplined human-in-the-loop systems.

They define where human judgment is essential, establish validation checkpoints, and bake governance into the workflow. This clarity accelerates adoption by reducing uncertainty and establishing trust.

Human-in-the-loop is not a barrier to AI-it is the mechanism that enables safe, scalable AI.

6. They Invest Like AI Is a Strategic Priority

High Performers invest at levels that reflect their ambitions. One-third of them spend more than 20% of their total digital budgets on AI-compared with only 7% of others. This investment fuels the foundations that matter: high-quality data infrastructure, agile development, reusable data products, and strong cross-functional talent.

Their investment signals commitment. It tells their organisation that AI is not a side project; it is the future.

7. They Accept Risk Because They Operate Where Value Is Real

Interestingly, High Performers experience more negative AI consequences than others-accuracy issues, compliance challenges, and unexpected outputs. But there’s a simple reason: they are applying AI in areas where real impact is created.

They move fast, experiment aggressively, and learn quickly. They understand that risk is not something to be avoided but managed. And because they push AI into mission-critical contexts, they discover challenges earlier and resolve them sooner.

Where others see risk as a barrier, High Performers see it as part of innovation.

Download The 6% Advantage: A Leader’s Checklist for AI Success

AI Success Is a Choice

Every organisation today faces a crossroads. You can pursue AI as a series of small, incremental steps-or as a strategic transformation. You can play it safe and stay in the 94%-or make different choices and join the 6%.

AI success is not predetermined. It is not a product of industry, budget, or geography. It is a function of ambition, openness, experimentation, and the willingness to accept that progress often includes missteps.

The organisations that succeed with AI are not the ones who avoid failure. They are the ones who learn from it. They iterate, test, redesign, and commit to bold visions that extend beyond short-term wins.

The future is being built by those willing to act decisively. The question now is simple:

Do you want to be part of the 6% and if so, what will your next move be?

The Battle For Christmas: AI vs Tradition, And Why Coca-Cola Is Betting On The Future

Alex Velinov — Fri, 07 Nov 2025 04:40:24 GMT

Christmas ads have always been emotional rituals, not just commercials. They are cultural signals. For decades, certain campaigns have become part of how the world feels Christmas: Coca-Cola’s red trucks rolling through snowy landscapes. Hershey’s Kisses ringing like bells since 1989. John Lewis launching animated worlds capable of making a nation stop, cry, remember, and feel. These campaigns weren’t just successful - they set the global expectation that Christmas advertising must create warmth, nostalgia, and connection. No other genre of advertising depends so deeply on emotional resonance. Christmas is a season built on memory - and great brands learned early: emotion is the real performance metric.

Coca-Cola’s Bold AI Pivot (2024–2025)

2024: The First Big Leap

In 2024, Coca-Cola made one of the most radical creative decisions in modern Christmas advertising: they used AI to recreate their most sacred Christmas narrative. Three studios were involved. Multiple AI models were combined. Thousands of short fragments were generated. And in the end — the execution struggled. Trucks changed shape mid-frame. Wheels didn’t spin. Human faces drifted into uncanny valley territory. Audiences instantly felt the absence of human emotional texture.

This wasn’t a quiet experiment — it was one of the most visible AI brand activations of the year. And because Christmas is emotionally sacred territory, the internet responded with unusually strong criticism. The backlash was global. The ridicule was loud. This is the brutal cost of being first.

2025: The Second, Smarter Attempt

But Coca-Cola did not retreat. In 2025, instead of abandoning the idea — they doubled down.

The brand removed human characters entirely, reducing the risk of uncanny valley by leaning into a world of animals and an AI Santa inspired by original Coca-Cola illustrations. Production shifted to highly constrained animal-based storytelling and stylized environments. It was a more intelligent design choice. The technical execution was noticeably better.

They generated more than 70,000 shots across three days and curated down to only 20–30 usable sequences. The improvement was clear. The emotional impact was still not enough. Trust metrics still underperformed. But the creative ambition was unmistakable.

Coca-Cola wasn’t trying to win Christmas this year. They were trying to define how Christmas ads might be produced in five years.

Behind the scenes video

Traditional Brands Held Their Ground

While Coca-Cola was taking arrows, every other major Christmas advertiser stayed with traditional production. John Lewis’s “Where Love Lives” (2025) was shot traditionally. Sainsbury’s “BFG” 2024–2025 used classic cinematic production. M&S, Amazon, Boots, Tesco, Lidl, Asda, Waitrose, Morrisons - all chose craft. All chose emotional storytelling. All chose human production.

It worked. The sentiment data is clear: traditional Christmas ads dramatically outperformed AI ads emotionally in 2024–2025.

Sentiment Analysis: What the Data Actually Shows

According to multiple sentiment platforms (DAIVID, System1, CARMA, Truescope and consumer surveys), Coca-Cola’s 2024 AI Christmas campaign recorded only 10.2% positive sentiment, 32.0% negative sentiment, and the rest neutral. The words that dominated feedback: “soulless”, “creepy”, “disappointing”. It actively damaged trust. It created public brand backlash. The 2025 version improved in craft, but was still 2% more likely to evoke distrust than industry norms.

Meanwhile, traditional Christmas ads drove overwhelmingly positive sentiment. For example, M&S’s “The Journey” (2024) delivered 55.4% intense positive emotion, Amazon’s “Midnight Opus” (2024) reached 55.2% intense positive emotion, and Aldi’s Kevin the Carrot continued to generate ~65% positive sentiment

Emotionally, there was no comparison. Traditional ads delivered the Christmas spirit that audiences crave. AI ads, today, do not.

Yet there’s a paradox beneath the numbers: Coca-Cola’s AI ads still generated attention, discussion, recall. They were distinct - even if they didn’t feel human.

Comparative Analysis: Innovation vs Authenticity

The industry is standing at a crossroads. Traditional storytelling has become extremely consistent, predictable, and safe - and consumers still reward it emotionally. Meanwhile, AI-generated Christmas ads currently fail the emotional bar required for Christmas advertising - but they collapse production time from one year to roughly one month. And Coca-Cola is not pretending they have “perfected” AI Christmas creativity yet. They are simply refusing to be late to the curve.

This is why this moment matters. Yes - today, traditional Christmas ads are 15–300% more effective at creating positive emotions. But innovation cycles move fast. And the very thing Coca-Cola is being ridiculed for now - may, in five years - become the standard pipeline everyone uses.

Christmas ads are not just about production. They are also about signaling who is inventing the future.

Who Will Be Remembered?

Christmas is about emotion. Christmas advertising is about creating emotion at scale. Right now, AI struggles with soul - and consumers can feel it. But innovation has always moved through this phase: early friction, early rejection, early contempt. Before it becomes normal.

Coca-Cola knew the risks. Coca-Cola acted anyway. And history rarely remembers the brands who stayed safe - it remembers the ones who dared to shape the next frontier.

So here is the real question every leader in this industry must now ask:

In five years, when AI-native production becomes standard - will Coca-Cola be remembered as the brand that was wrong… or the brand that was first?

When Markets Start Thinking - The Rise of AI Agents and the End of Human-Only Economics

Alex Velinov — Thu, 30 Oct 2025 07:49:43 GMT

A new kind of participant is entering the global economy - one that doesn’t sleep, negotiate pay, or tire from comparison shopping. These are AI agents: autonomous systems that can perceive, reason, and act on behalf of humans.

A recent paper by economists from MIT, Harvard, and BU explores how these agents could transform markets by dramatically reducing transaction costs - the hidden frictions of searching, negotiating, and contracting that shape nearly every business activity.

The authors argue that AI agents are not just tools, but market actors in their own right - capable of transacting, reasoning, and learning across digital environments.

For business leaders, this research provides an early map of how markets might reorganize around AI. Just as the internet redefined access to information, AI agents could redefine access to action - executing decisions for individuals and organizations alike.

Demand for AI Agents

Why People Will Use AI Agents

Humans will turn to AI agents for the same reasons they hire human ones - to save time, gain expertise, and preserve privacy.

These motivations will drive two main types of adoption:

Substitution - replacing human intermediaries such as recruiters, brokers, or advisors.
Expansion - enabling transactions that were previously too costly or complex to pursue.

By converting time and cognitive effort into low-cost computation, agents unlock new possibilities across industries. They can compare thousands of insurance offers, schedule services, or negotiate contracts faster and cheaper than people can.

Where Adoption Will Start

AI agents will likely gain traction first in markets already dependent on human intermediaries or digital platforms - places where high stakes, complex options, and large counterparties exist.

Think of real estate, hiring, or investment advice. On platforms like LinkedIn, Zillow, or Upwork, agents can search and negotiate tirelessly. Over time, this may shift market power toward consumers, as agents act purely in their principal’s interest - rational, data-driven, and free from fatigue or bias.

What People Will Expect

Just like human professionals, users will want AI agents that are capable, informed, and loyal. They must understand intent, act ethically, and remain aligned with their user’s goals.

Trust will become a key differentiator. Users will compare agents based on benchmarks, transparency, and performance reputation, not just price or speed.

Designing AI Agents

Designing AI agents involves both engineering and economic challenges.

On the technical side, the goal is to build systems that can perceive, reason, and act reliably across digital contexts. On the economic side, the challenge is alignment - ensuring that agents act according to their user’s true preferences.

Understanding Human Preferences

A core difficulty lies in figuring out what people actually want.

Traditional recommendation systems infer preferences from limited signals like clicks or ratings. But AI agents operate in open-ended natural language, allowing them to handle far more complex goals - and far more room for misunderstanding.

Users may struggle to articulate their real preferences, while agents may misinterpret or hallucinate them.

In high-stakes decisions - like buying a home or making an investment - even small misinterpretations can have major consequences.

Balancing Autonomy and Oversight

Agents must also learn when to act on their own and when to consult the human.

Too much autonomy risks unwanted outcomes; too little wastes the agent’s potential. The authors refer to this balance as meta-rationality - knowing the limits of one’s own reasoning.

As agents interact in markets, they’ll face adversarial manipulation - attempts by others to exploit their logic or decision rules. Designing for robustness, transparency, and clear delegation boundaries will be essential to maintaining trust.

Supply of AI Agents

On the supply side, two forces shape the emerging agent economy:

How agents are produced and priced
How they are owned and specialized

From Foundation Models to Market Products

Most agents are built on foundation models - large, general-purpose systems like those developed by OpenAI or Anthropic.

This structure creates two tiers of suppliers:

Model owners, who build and control access to foundational systems.
Agent developers, who customize these systems for particular industries or functions.

Since the cost of producing each additional agent is close to zero, supply is effectively unlimited. However, compute and data requirements mean high-stakes or domain-specific agents could still carry higher costs.

Ownership and Specialization

Consumers will encounter agents that differ by ownership and specialization.

Each combination carries trade-offs between alignment and convenience.

A user-owned vertical agent may best protect interests but require setup and cost. Platform agents will be easier to use yet may deepen lock-in and self-preferencing, reinforcing today’s platform power dynamics.

Market Design for AI Agents

As agents proliferate, markets themselves will need to evolve - not only through policy but through how participants are identified, trusted, and coordinated.

Identity and Accountability

Distinguishing between humans and machines will become vital.

The authors emphasize the need for digital identity systems that verify both humans and their AI representatives.

Two models could emerge:

Walled gardens, where platforms tightly control verification and participation.
Open systems, using cryptographic proofs or digital IDs for universal recognition.

“Proof-of-personhood” systems could ensure one verified identity per human, protecting markets from mass automation and manipulation.

Rethinking Platform Economics

AI agents will also disrupt how digital platforms earn revenue and compete.

Today’s platforms monetize human attention - optimizing for clicks, engagement, and emotion. But AI agents don’t click or scroll. They act rationally, filtering content and making decisions without distraction.

This shift could undermine attention-based business models, pushing platforms toward subscriptions, utility-based pricing, or machine-to-machine APIs.

Over time, the internet may divide into two layers:

A human web designed for people.
An agent web optimized for autonomous systems.

Enabling New Market Mechanisms

AI agents could also enable new kinds of market mechanisms that were once too complex to implement.

For example, job or housing markets could move from simple recommendations to equilibrium matching, where agents representing both sides exchange structured preference data to reach optimal matches.

Agents could even enhance privacy and fairness, negotiating sensitive details without revealing personal information.

If designed responsibly, they could make markets more efficient, transparent, and personalized than ever before.

Final Words

The rise of AI agents marks a pivotal moment in how economies are organized.

By automating the costs of searching, negotiating, and coordinating, these systems can make markets faster, fairer, and more inclusive. Yet efficiency also brings risks - congestion, opacity, and power concentration among dominant platforms or model owners.

The outcome will depend on how demand, supply, and regulation evolve together.

Businesses that adapt early to an agent-first economy - where decisions and transactions are handled by intelligent digital counterparts - will gain an advantage.

AI agents are not just tools within markets; they are becoming participants in the market itself.

For leaders, the challenge ahead isn’t just adoption - it’s understanding how these agents will change competition, collaboration, and the very logic of value creation.

Read the full paper “The Coasean Singularity? Demand, Supply, and Market Design with AI Agents,” by Peyman Shahidi, Gili Rusak, Benjamin S. Manning, Andrey Fradkin, and John J. Horton (MIT, Harvard, BU, and NBER, 2025).

https://www.nber.org/system/files/chapters/c15309/c15309.pdf

The Third Browser War - Atlas vs Comet

Alex Velinov — Thu, 23 Oct 2025 05:40:30 GMT

Something interesting is happening in the world of browsers. For the first time in decades, the default experience of how we interact with the web is being reimagined. The launch of OpenAI’s Atlas and Perplexity’s Comet signals a new phase-what many are calling the third browser war. The first battle was Netscape vs Internet Explorer. The second was Google Chrome’s rise and domination. And now, the third is shaping up around AI-powered browsers that don’t just show you the internet-they think alongside you.

2025: What Happened with Comet and Atlas

2025 marked the start of the so-called third browser war, with OpenAI and Perplexity taking AI browsing mainstream. Here’s how the year unfolded:

Early 2025: News spread that OpenAI was building an AI-native browser under the codename Atlas, led by Ben Goodger (ex-Chrome). Development was based on Chromium and tightly linked with ChatGPT.
May: Perplexity CEO Aravind Srinivas shared his vision of building an “operating system for everything.” The company’s ambition was clear: own the default browser experience.
July 8–9: Perplexity launched Comet as a limited release for Max subscribers ($200/month). The waitlist exploded, and users started calling it the “most wanted AI product of the year.” The same week, media confirmed OpenAI’s plans for Atlas, sparking industry-wide speculation.
August: Perplexity expanded Comet’s beta and fixed a security flaw reported by Brave. Later that month, it launched Comet Plus ($5/month), giving access to premium content from publishers like CNN, Fortune, and The Washington Post, sharing 80% of revenue with them.
October 2: Comet went free worldwide on Mac and Windows, removing the paywall. Two weeks later, Enterprise tiers were added ($40 and $325 per seat), complete with SOC 2 and HIPAA compliance.
October 21: OpenAI officially launched ChatGPT Atlas globally. It was free for all ChatGPT users, with macOS available immediately and other platforms coming soon. Features included Agent Mode (for Plus/Pro users), inline writing help, and a persistent chat sidebar.
October 22: Industry analysts formally declared the start of the third browser war. Alphabet’s stock dropped 4.8% following the announcement, and Google accelerated Gemini integration into Chrome.

By the end of the year, both browsers had defined their territory: Comet led on speed, verification, and enterprise readiness, while Atlas focused on privacy, integration, and conversational control. The battle for the “front door to the internet” had officially begun.

Comet vs Atlas: Feature Comparison

Use Case Optimization

Atlas

Ideal for multi-step automation and document drafting, where Agent Mode executes clear, supervised workflows.
Excels in privacy-sensitive environments with granular memory settings and logged-out agent operation.
Supports complex reasoning tasks such as structured problem-solving and multi-step planning.
Integrates seamlessly with ChatGPT Teams for collaborative content creation and workflow automation.

Comet

The preferred choice for research and analysis, with citation-first synthesis and verified sources.
Designed for speed-critical operations, using parallel agents for rapid data retrieval and completion.
Trusted by enterprise research teams needing SOC 2/HIPAA compliance and traceable collaboration.
Delivers real-time information synthesis through live web grounding and accuracy-first retrieval.

Final Words

Neither Atlas nor Comet is ready to dethrone Chrome yet. They’re early, ambitious, and still finding their place. But they represent something bigger: a shift from passive browsing to active collaboration.

Atlas is the better fit for users who want seamless automation, conversational help, and privacy-conscious task execution. Its sequential agent mode and memory controls make it the natural choice for those who prefer proactive assistance with clear safety lines.

Comet, on the other hand, shines for those who care about speed, verification, and trust. Its citation-first design, multi-agent reasoning, and transparency ethos make it perfect for research-intensive workflows.

Both are glimpses of the next era of the web-where browsers stop being windows and start becoming partners.

Get the Most Out of Your Calls and Meetings with AI

Alex Velinov — Fri, 17 Oct 2025 05:30:55 GMT

Artificial Intelligence shines brightest when applied to repetitive, insight-rich tasks, and few areas fit that description better than our everyday calls and meetings. Whether it’s sales pitches, client updates, or internal reviews, each conversation holds valuable data that often goes underused. AI transforms these exchanges into structured, actionable insights that help teams make smarter decisions faster.

When used effectively, AI doesn’t just summarize meetings, it uncovers the why behind decisions, identifies follow-ups, and even measures engagement or sentiment. The result: less time spent decoding conversations and more time acting on what matters.

This article offers practical ideas for how to apply AI prompts on top of your call transcripts, turning those transcripts into actionable insights.

When used effectively, AI doesn’t just summarize meetings — it uncovers the why behind decisions, identifies follow-ups, and even measures engagement or sentiment. The result: less time spent decoding conversations and more time acting on what matters.

How to Apply These Prompts

You can use any AI note-taker tool to record and transcribe your calls — whether they’re video meetings or audio conversations. Tools like Fathom, Fireflies, or Otter can automatically generate a transcript for you.

Once you have your transcript, the next step is to use an LLM (Large Language Model) of your choice such as ChatGPT, Gemini, Copilot, or Claude or use your note-taker tool to turn that transcript into actionable insights.

All you need is:

Your meeting transcript (the raw text from your call).
The prompt you want to use (based on your goal, e.g., summarizing, extracting actions, analyzing sentiment).
Your chosen LLM to process and interpret the transcript.

Simply paste your transcript into your preferred AI tool, apply one of the prompts below, and watch how AI transforms your call data into clear, structured insights — ready to be shared, acted on, or archived.

7 Core Use Cases for AI in Calls

1. Call Summaries and Key Decisions

The most common and useful application of AI is to automatically summarize calls. Rather than rewatching recordings or scanning transcripts, AI can generate concise overviews with decisions, owners, and deadlines — freeing up time for execution.

Prompt:
“Summarize this call in 6-8 bullet points. List all decisions made, next steps, and task owners with due dates.”

2. Action Item Detection

AI can capture the “who, what, and when” from your calls, ensuring accountability across teams. This turns loose conversation into structured next steps that can feed directly into project tools.

Prompt:
“List all action items mentioned in this call, sorted by owner. Include due dates if specified.”

3. Sentiment and Engagement Analysis

AI can detect tone and emotion in conversation — identifying whether discussions were positive, neutral, or tense. This is particularly valuable for client relations, sales negotiations, or performance reviews.

Prompt:
“Provide an overview of the overall sentiment of this call. Then, for each participant, summarize their tone and attitude in one or two sentences.”

4. Call Scoring and Continuous Improvement

AI can score calls based on key dimensions such as clarity of purpose, decision density, balance of participation, and focus. These insights enable teams to coach themselves toward more effective communication.

Prompt:
“Score the call on Clarity of Purpose, Decision Density, Action Orientation, Participation Balance, Focus, and Relevance (0–5 each). Provide 3 strengths, 3 gaps with time-stamped examples, and the one change that would most improve the next call.”

5. Sales and Growth Enablement

In sales, AI can analyze lead quality, highlight buying signals, and even draft follow-up emails that align with the discussion tone. This helps sales teams close loops faster and maintain consistency.

Prompt:
“Use the call transcribe to qualify this opportunity with BANT (Budget, Authority, Need, Timing) and highlight buying signals vs red flags. Recommend the next 2 moves and give a plain-English close likelihood with a one-sentence rationale.”

6. Customer Success

For customer-facing or technical roles, AI can help validate ideas, capture client feedback, and extract useful quotes or phrasing that reflect the customer’s own language — improving empathy and alignment.

Prompt:
“Extract from the call phrases the client uses to express success or frustration. Use their language to craft 3 short positioning statements or talking points that show you understand their goals and can guide them as a specialist. Frame each suggestion as helpful, outcome-focused, and aligned with their priorities.”

7. Internal Knowledge and Training

AI can turn recorded internal meetings into knowledge assets. From onboarding materials to technical documentation, AI can transform conversations into searchable, shareable content.

Prompt example:
“Convert the call into a step-by-step SOP with Prerequisites, Steps, and Validation checks. Write it so a new teammate can follow it unaided.”

From Data to Insights

The power of AI lies in its ability to turn raw conversation data into usable intelligence. Every recorded call becomes a goldmine for:

Improving team performance through feedback and scoring.
Spotting client trends across multiple meetings.
Creating searchable archives for decisions and lessons learned.
Identifying patterns that support strategic decisions.

Once these insights are captured, teams can apply filters, automate alerts, and build workflows that keep important learnings visible, rather than buried in forgotten transcripts.

Download Now: The AI Call Playbook

16 uses cases and prompts
Download now to transform your meetings into actionable results with The AI Call Playbook.

Final Thoughts

Every call is an opportunity, not just to connect, but to learn. By integrating AI into your call workflow, you ensure that no idea, action, or insight slips through the cracks. The future of effective communication isn’t about having more meetings, it’s about getting more from every meeting.

Why AI Adoption Isn’t a Tech Project, but Change Management Project

Alex Velinov — Thu, 09 Oct 2025 12:48:15 GMT

Artificial Intelligence is not simply another wave of technological advancement - it is an inflection point reshaping how businesses operate, compete, and create value. The velocity of change is staggering; in this new era, speed and consistency are the only moats. For leaders, this means the real competitive edge no longer lies in deploying the most advanced algorithms but in orchestrating human adaptability at scale. The organizations that thrive will be those that manage change - rapidly, responsibly, and with purpose.

5 Quick Answers

1. Why is AI change management more important than technology in digital transformation?

AI adoption succeeds when people, not just machines, evolve. Around 80% of AI transformation depends on change management—aligning leadership, culture, and workforce capabilities before introducing new technology.

2. How can business leaders overcome employee resistance to AI adoption in the workplace?

Leaders overcome resistance by building trust through communication and upskilling. Clear messaging that AI enhances—not replaces—human work fosters acceptance and engagement.

3. What are the best change management strategies for successful AI adoption in organizations?

Successful AI adoption blends top-down leadership with bottom-up innovation. Encourage hands-on learning, reward early adopters, and focus efforts on the middle 40% who shape company culture.

4. How can companies improve AI literacy and prepare employees for automation?

Companies boost readiness by making AI literacy a continuous learning priority. Weekly training, role-based workshops, and practice-driven sessions empower teams to integrate AI confidently.

5. What is the best approach to AI governance and ethical risk management for businesses?

Effective governance means being an enabler, not a blocker. Define guardrails for ethics, data privacy, and model transparency while encouraging safe experimentation and innovation.

AI as a Change Management Project, Not a Technical One

While many organizations still frame AI adoption as a technical challenge, the truth is clear: AI transformation is 80% change management and 20% technology. Technology can be purchased; adoption must be earned. Companies that succeed invest disproportionately in the human side - mindsets, skills, and structures. A practical rule of thumb: for every dollar spent on technology, allocate three dollars to change management. This 1:3 ratio ensures that people, processes, and leadership evolve alongside the tools.

Human factors - ego, fear, and uncertainty - are the real barriers to scaling AI. These are the elements of what some call “primate behavior”: our innate resistance to losing control or expertise. Overcoming them demands empathetic communication, transparent leadership, and deliberate design of new habits and incentives.

The Imperative and Pace of AI-Driven Change

The risk of inaction now outweighs the risk of experimentation. AI is hollowing out repetitive and cognitive tasks - the transformation is subtle but pervasive, happening through “termites, not tornadoes.” Jobs are not disappearing overnight but being reshaped piece by piece. Every white-collar profession is experiencing a quiet redefinition of value.

We have moved from an era of digital enablement to an era of digital outcomes. AI doesn’t merely offer better tools; it performs work, makes decisions, and produces results autonomously. Business leaders must therefore accelerate their organizational metabolism - adopting an “always learning” posture and empowering teams to adapt continuously.

Change Management as the Critical Bottleneck

The biggest obstacle to AI success is not the model - it’s the human response to change. Resistance, fatigue, and fear can paralyze even the most promising initiatives. As one executive put it, the bottleneck isn’t technology - it’s primate behavior. Leaders must navigate ambiguity with confidence, not false certainty, providing context, reassurance, and opportunity.

This is why the Chief AI Officer of the future must be a change leader first and a technologist second. Their success depends on partnership with HR and culture leaders to guide reskilling, career pathways, and new definitions of performance. Change management isn’t an add-on; it is the operating system for AI adoption.

Strategies for Driving Adoption

Successful AI integration requires both top-down sponsorship and bottom-up engagement:

Model the behavior: Leaders who actively use AI tools set the tone for the organization. Adoption cascades when people see their managers engaging authentically.
Combine carrots and sticks: Incentivize experimentation but track usage. Recognize champions while addressing persistent resistance.
Target the middle: Focus change efforts on the 30–40% of employees who are open but uncertain. They represent the tipping point between enthusiasm and inertia.
Define the WIIFM (“What’s in it for me”): Clearly articulate how AI will eliminate drudgery, enhance performance, or open new career paths.

The most effective programs blend structured governance with flexibility for local innovation - creating both accountability and agency.

Organizational Learning and AI Literacy

AI literacy is now a life skill. Yet, many professionals still lack even basic understanding of how AI works or how to apply it responsibly. Learning can no longer be episodic; it must be continuous, practical, and engaging.

Make learning a contact sport: People learn AI by doing, not by reading. Encourage hands-on exploration through internal sandboxes and collaborative projects.
Build weekly rituals: Regular, short learning sessions - such as “Fuel Up Fridays” - help normalize ongoing education.
Shift from prompting to agent creation: Move beyond using AI for simple queries toward designing and supervising autonomous agents.
Teach navigation over memorization: The next generation of education and training must focus on how to find, validate, and apply knowledge rather than retain it.

Organizations that invest early in workforce AI literacy will not only adapt faster but also attract and retain top talent who want to grow with the technology.

Culture, Governance, and Risk Management

A successful AI transformation demands a new cultural operating model - an AI-first mindset grounded in trust, ethics, and stewardship. Governance must evolve from a compliance gatekeeper to an enabler of safe speed.

AI stewardship over control: IT and risk teams should set clear guardrails while empowering business units to experiment responsibly.
Strong yet flexible governance: Address critical risks like data leakage, bias, and hallucination with proactive controls - not reactive bans.
Crisis readiness: Prepare for contingencies such as outages or dependency failures. Build resilience into systems and decision-making processes.

Ultimately, culture and governance must work together to make ethical AI use habitual - not optional.

New Organizational Structures and Roles

The workforce of the future is already here - hybrid teams of humans and AI agents working side by side. Organizations must evolve to support this new architecture of work:

Hybrid operations: Deploy internal AI agents to handle repetitive or non-differentiating tasks, freeing people to focus on creative and relational work.
Decentralized ownership: Empower business leaders to evaluate and prioritize AI use cases, avoiding “pilot paralysis.”
Flexible and fungible structures: Design roles around outcomes, not static job descriptions. Teams should reconfigure fluidly as new capabilities emerge.
CISO as enabler: The security function must shift from enforcing to partnering - building safe experimentation environments that accelerate, not slow, progress.

These shifts redefine how we build teams, design processes, and measure value in an AI-powered enterprise.

Common Challenges and How to Overcome Them

Leaders consistently face recurring barriers when implementing AI. Below are the most common challenges and actionable ways to overcome them:

Challenge: Employee resistance and fear of AI
Solution: Communicate early and often, showing the clear benefits of AI. Emphasize that AI is designed to augment, not replace human work.

Challenge: Lack of AI literacy
Solution: Invest in hands-on training and integrate learning into everyday work. Encourage employees to become AI ambassadors who share knowledge across teams.

Challenge: Leadership misalignment
Solution: Ensure visible executive sponsorship and that leaders model AI usage to set the tone across the organization.

Challenge: Integration and workflow friction
Solution: Design AI systems to fit seamlessly within existing workflows. Embed them in the tools employees already use to reduce disruption.

Challenge: Ethical and privacy concerns
Solution: Build transparent governance frameworks and form cross-functional AI ethics boards to oversee responsible usage.

Challenge: Unrealistic ROI expectations
Solution: Start small, focus on measurable learning outcomes, and scale gradually as understanding and confidence grow.

Proactive communication, structured training, and continuous feedback loops are the foundation of overcoming these challenges.

Best Practices and Recommendations

Anchor AI to strategy, not novelty. Every use case must tie to measurable business outcomes.
Fund change as a core capability. Maintain a 1:3 ratio between technology and change management investment.
Adopt dual-speed leadership. Combine decisive top-down sponsorship with grassroots experimentation.
Build continuous learning loops. Weekly engagement beats annual training.
Make governance an accelerator. Guardrails that enable, not constrain, innovation.
Scale with intention. Use the Value × Data × Culture framework to prioritize where to deploy AI next.

Final Words

Artificial Intelligence is transforming work through evolution, not revolution - quietly, persistently, and irreversibly. The organizations that emerge stronger will not be those with the most sophisticated algorithms, but those with the most adaptive people.

This is the leadership challenge of our time: to make AI adoption a story of empowerment, not fear; of stewardship, not chaos. AI success is, and will remain, a human story - one written by leaders who understand that true transformation begins with trust, learning, and the courage to change.

PIVOT Framework for AI-Driven SMB Transformation

Alex Velinov — Thu, 25 Sep 2025 05:31:14 GMT

Nearly 40% of Americans already use generative AI at home or at work - faster adoption than both the internet and personal computers. This is no longer a trend; it’s a fundamental shift.

AI has emerged as a great equalizer, giving SMBs the ability to scale operations with speed and compete with larger firms on more level ground. But while the opportunity is massive, the challenge is real: how do you adopt AI in a way that creates real value, without wasting time chasing hype?

That’s where the PIVOT Framework comes in a simple, five-pillar approach to help SMBs integrate AI into products, processes, and culture with clarity and speed.

Goal of the Framework

The PIVOT framework helps small and medium businesses (SMBs) adopt AI effectively. It breaks the transformation into five clear pillars that focus on strategy, innovation, execution, operations, and culture. The goal is to give you simple, actionable steps to integrate AI in a way that drives growth, agility, and long-term competitiveness.

The 5 Pillars of PIVOT

1. Pinpoint AI Impact – Find Where AI Creates Value

The first step in any AI journey is clarity. Too many businesses adopt tools just because they’re trendy, without understanding if they solve a real problem. Instead, begin by mapping your business processes and customer touchpoints. Look for repetitive, manual tasks that slow you down or create errors. Generative AI is particularly effective with tasks involving text, images, audio, or video, so start there.

This isn’t just about efficiency inside your company, it’s also about market awareness. Customer expectations are changing: they want faster responses, tailored recommendations, and 24/7 support. Meanwhile, competitors may already be embedding AI into their workflows. By pinpointing where AI creates value (or disruption), you can design a focused roadmap and avoid wasted effort.

Action Points:

Conduct a full audit of products, services, and workflows to locate repetitive, error-prone, or manual processes.
Focus on areas with heavy use of text, images, audio, or video - AI is strongest here.
Evaluate customer expectations being shaped by AI (e.g., faster service, personalized recommendations).
Analyze competitor activity: who is already embedding AI, and where might disruption come from?
Define a strategic AI roadmap, prioritizing a few high-impact, feasible use cases with measurable goals.
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2. Innovate Offerings – Upgrade Products & Services

Once you know where AI matters, it’s time to rethink what you offer customers. AI is changing industries by turning old differentiators into commodities. For example, what once took hours of creative or analytical work can now be generated in minutes. That means SMBs must innovate faster than ever to maintain an edge.

Innovation can take many forms: embedding AI features in your products, creating AI-powered services, or using AI to deliver hyper-personalization at scale. Customers today expect experiences tailored to them. AI makes this possible by analyzing behavior, predicting needs, and providing recommendations automatically.

However, differentiation doesn’t come from AI alone. The best companies are blending automation with human expertise. While AI handles scale, speed, and repetition, humans provide creativity, empathy, and trust. This balance - “AI + Human” - becomes your competitive advantage.

Action Points:

Assess if AI is commoditizing your current offerings; plan differentiation strategies.
Embed AI-powered features into your products and services (chatbots, personalization engines, AI-driven insights).
Use AI for hyper-personalization of customer journeys - analyze behavior and tailor recommendations.
Balance automation with human value: position your offering as “AI + human” rather than AI alone.
Experiment quickly with small AI-driven enhancements, monitor customer reactions, and refine continuously.

3. Velocity & Agility – Move Fast with AI

In the AI era, speed is everything. Technology and customer expectations evolve so quickly that waiting too long to act means missing opportunities. Unlike large corporations, SMBs have the advantage of less bureaucracy and more flexibility. The key is to turn that flexibility into action.

This pillar is about embracing rapid experimentation and agile execution. Instead of spending months on strategy documents, launch a pilot in days or weeks. Use short sprints, collect feedback immediately, and adapt quickly. Perfection is not the goal - learning fast is. A 70% solution today often beats a 95% solution a year from now.

Velocity also requires cultural change. Employees should feel safe to experiment, and failures should be treated as learning opportunities, not punishments. When teams see quick wins and leadership celebrates them, agility becomes part of the company’s DNA.

Action Points:

Adopt a “rapid experimentation” mindset - start small, test quickly, and iterate.
Use cloud-based AI tools to prototype ideas without heavy investment.
Break projects into short sprints, integrating customer and user feedback in each cycle.
Empower cross-functional teams to trial new ideas without excessive approvals.
Streamline decision-making by reducing red tape and delegating authority to AI-focused teams.
Share and celebrate early successes to drive adoption and build momentum.

4. Operationalize AI – Embed AI in Daily Work

Adopting AI isn’t only about customer-facing products - it’s about weaving AI into the fabric of daily operations. Many of the biggest wins come from automating time-consuming, repetitive tasks that eat up staff energy. Invoices, expense reporting, email sorting, or meeting notes can all be offloaded to AI.

Beyond automation, AI enhances decision-making. Tools that forecast demand, prioritize sales leads, or suggest pricing strategies give managers sharper insights and faster responses. But success depends on strong data and governance. AI thrives on high-quality, integrated data; without it, results will be unreliable.

Equally important is measurement. You need to track improvements in speed, error rates, cost savings, and customer satisfaction to prove ROI and refine usage. Operational AI isn’t a one-time setup - it’s a cycle of testing, monitoring, and improving.

Action Points:

Identify and automate time-consuming internal tasks (data entry, reporting, inquiries).
Launch a pilot project (e.g., internal chatbot, automated report summarization) and refine through feedback.
Use AI to augment decision-making with forecasts, recommendations, and scenario planning.
Invest in data quality and integration - unified, clean data is the foundation of reliable AI.
Track metrics like processing speed, error rates, cost savings, and satisfaction to measure ROI.
Continuously review and update workflows as AI tools and business needs evolve.

5. Transform Culture – Build an AI-Ready Team

Technology alone doesn’t guarantee success - people and culture do. For AI adoption to stick, your workforce must be trained, confident, and willing to experiment. This means investing in AI literacy at every level, from entry-level staff to executives.

At the same time, leadership must model openness. When leaders champion AI transformation and treat it as a strategic priority, employees follow. Equally critical is addressing ethics and trust. Customers and employees want to know that AI is used fairly, transparently, and securely. Organizations that prioritize ethical AI practices - like bias checks and data protection - will turn trust into a competitive advantage.

Finally, transformation means embedding AI into the rhythm of your company. Encourage cross-functional collaboration, reward innovation, and continually look for ways to combine human expertise with AI insights. Over time, this makes your organization AI-native and AI-first not just AI-enabled.

Action Points:

Provide AI literacy training across all levels, from basic tools to specialized roles.
Establish clear usage policies to guide safe, responsible AI use.
Reskill staff for emerging AI-related functions, while supporting ongoing learning.
Leaders should model openness, showing curiosity and responsibility with AI adoption.
Embed ethical practices: fairness, transparency, bias checks, and data security.
Encourage cross-functional collaboration between business and technical teams.
Identify your unique competitive advantages (niche expertise, agility, proprietary data) and amplify them with AI.

Download The PIVOT Framework Playbook

Final Words

The PIVOT Framework breaks AI transformation into manageable steps. Pinpoint where AI adds value, innovate your offerings, move with speed, embed AI into daily work, and build an AI-ready culture.

But the most important question isn’t what the framework is - it’s what you’re going to do today.

AI is moving fast, and so must you. The real difference comes from small, tangible wins - launching that pilot chatbot, automating one process, or testing personalization with your next campaign. These small steps create momentum, build confidence, and compound into real transformation.

Don’t wait for the perfect plan. Start today, move quickly, and learn as you go. In the AI era, speed is your strongest advantage.

You can also check:

The VECTR™ Framework: An Operating System for AI Adoption

The FCE™ Framework: A Fast, Focused Way to Measure AI Value

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