Former Google CEO, Eric Schmidt, stood in front of about ten thousand graduates at the University of Arizona on Sunday, and a few minutes in, the booing started.

Why?

He mentioned AI.

“I can hear you,” he said, over the noise, and kept going.

He named the graduates’ fear back to them - the machines are coming, the jobs are evaporating, you are inheriting a mess you did not make - and called it a rational fear.

But when someone offers you a seat on a rocket ship, he said, you do not ask which seat. You just get on.

It is tempting to file this under out-of-touch billionaire gets what was coming to him - but the speech was stranger than that. Because it was two speeches at once. One was a real defence of agency - their future was not yet written, theirs to shape. The other told them the rocket ship was already here, and the only move was to get on.

A stadium of twenty-two-year-olds heard only the second speech.

He was trying to encourage them - but then undid it in the same breath.

And Schmidt was not the only one.

Around the same time, a speaker at the University of Central Florida called AI the next industrial revolution and was drowned out by boos, while a music executive at Middle Tennessee State told graduates to “deal with it.”

What the booing was actually aimed at

So what’s really going on here?

Well, I wanted to know whether the booing was just a crowd doing what crowds do. So I went to the comments, on YouTube and Reddit.

Last year I did this on a larger scale: fifty thousand comments under three Diary of a CEO episodes about AI and work. What struck me then was the range. There was fear, but also curiosity, scepticism, people describing what they had already built. Enough variety to sort into five rough types.

Based on my research this week, there’s no longer so much diversity of opinion.

In fact, if you read the threads under the booing clips there’s one overriding feeling: anger (and in the billionaire tech CEO’s case, a large dollop of contempt).

This is seemingly not technophobia, by the way. Many of the angriest comments are written by people who use AI every day, often because their employer requires it.

Instead, the perception is that all three speakers were delivering the same instruction - this is settled, your future is fixed, and the only move left to you is your attitude. Be grateful. “Get on the rocket ship." That was what the boos were aimed at.

And the commenters flipped that rocket-ship line back on Schmidt - not passengers being offered a seat, but “the fuel”.

Zoom out, look beyond the comments, and you’ll see a similar story.

In recent polling, 70% of Americans say AI is moving too fast, and just 18% of young people say they feel hopeful about it.

Share Ollie on Work

There is also a real feeling of grievance in the case of the students.

A version of the deal these graduates were sold - do the degree, take on the debt, a career follows - has been rewritten while they were still holding up their end. One of the sharper observers called the feeling not fear but a sense of having been “double-crossed.”

You do not boo a weather forecast. You boo a promise you believe someone has broken.

Why uncertainty is the argument for agency, not against it

So where does that leave the advice?

Well, agency still matters - but not because it guarantees a safe, secure future. It matters precisely because nobody knows what is coming.

Because the evidence is genuinely split. Some of it shows AI hollowing out entry-level work, while some finds no clear sign of AI in the job numbers at all. They are looking at the same labour market and disagreeing about what’s happening.

And that is the point writ large.

If you knew you would be fine, what you did next would not matter. If you knew you were finished, it would not help. It is because no one knows, that what you do still counts - building range, trying things, treating your career as something to act on, not something that happens to you.

And frankly, it is not just young people.

If you are reading this, you may be doing two jobs at once. Leading other people through this, and wondering about your own working life. The answer to both is the same - stop waiting for the picture to get any clearer, because it will not. Act now.

Oh, and the doomer belief - that nothing they do matters - is even more dangerous. Believe it, and you stop trying altogether. And not trying is what will make it true.

Ten thousand graduates filed out of that stadium into an uncertain future. What happens to them now will not be decided by an ‘inspiring’ speech, or by their booing. It will be decided by what each of them does next. And, crucially, whether we give them the chance to learn and make mistakes the way we did.

That was always true - AI has just made it impossible to ignore.

Thanks for reading.

Ollie

Share

Ollie on Work is a weekly newsletter about what I’m learning from building with AI, advising leadership teams, and trying to bridge the gap between what technology can do and how businesses actually work. If someone forwarded this to you, you can subscribe here:

In David Sedaris’s essay Stepping Out, he describes what happened when he got a Fitbit.

It started reasonably - ten thousand steps a day, a gentle tingle on his wrist when he hit the target. Then fifteen thousand. Then twenty-five thousand. By the end he’s doing sixty thousand steps a day, picking up litter along the roads of Sussex, and when his Fitbit dies he lasts five hours before ordering a replacement, hands shaking as he opens the box.

Sedaris started walking because he enjoyed it. Then the measurement replaced the enjoyment as the point.

He stopped asking “is this a good walk?” and started asking “is this enough steps?” The answer was never yes.

There’s a story in Brad Jacobs’s book that runs in the other direction.

Jacobs has built multiple billion-dollar-plus companies from scratch, but the best bit in his book is about something considerably more mundane.

His home AC broke during a heatwave. He called Steve, his repair guy, who came on a Sunday without hesitation.

Steve had originally worked for the company that installed the system - a large outfit that got bought by a private equity firm - not one of the good ones - which cut costs, stripped the service back, and resold the business.

Steve saw what was happening and left to set up on his own.

“How’s the old company doing?” Jacobs asked him.

“Terrible,” Steve said. “There’s no pride in their service. They’re giving the industry a bad name around here.”

Steve isn’t perfect. He’s misdiagnosed problems. He’s ordered the wrong part. But he owns up to it immediately and fixes it fast. Customers flock to him. The company he left is haemorrhaging clients.

The PE firm did what Sedaris did with his Fitbit.

They found the thing they could measure - cost - and optimised for it until they forgot what the business was actually there to do. The service got cheaper to run. It also got worse. The people who cared about doing good work left. The customers followed them.

Share Ollie on Work

The cost case

The same pattern is playing out in many AI conversations right now.

Cost reduction is the easiest business case to make.

“We’ll save X by automating Y” sounds like a no brainer.

“We’ll make our best people more effective” is harder to put a number on. And much harder to actually achieve.

So the cost case wins, and six months later you’ve got a cheaper operation that nobody - customers or employees - feels good about.

Microsoft’s latest Work Trend Index, based on 20,000 AI-using workers across ten countries, backs this up. The organisations getting value from AI aren’t just handing people tools or using them to reduce headcount. They’re changing the culture, management habits, and workflows around them.

Steve’s old company had the same technology available as Steve. The difference was everything around it.

The better question

So, the question to ask yourself is really quite simple:

When the AI’s been there a while, are your best people more valuable or less?

If the technology handles the routine so your best people can focus on the stuff that only they can do, you’re building Steve’s business.

If the people with experience and skill matter less than they used to, you’re building the bad version of the PE story.

And your best people will notice before your customers do. Steve didn’t leave because the PE firm fired him. He left because they made it impossible for him to do his job the way he knew it should be done. The people you most want to keep will do exactly the same thing.

Sedaris, at least, only ruined his own walks.

Thanks for reading.

Ollie

Share

Ollie on Work is a weekly newsletter about what I’m learning from building with AI, advising leadership teams, and trying to bridge the gap between what technology can do and how businesses actually work. If someone forwarded this to you, you can subscribe here:

Last Sunday, Sabastian Sawe ran the London Marathon in 1 hour, 59 minutes and 30 seconds. He became the first person to break two hours in an official race, a barrier many people said wasn’t humanly possible.

His backstory is remarkable.

Sawe grew up in a village in Kenya’s Rift Valley without electricity, raised mostly by his grandmother. He only ran 5,000 metres for the first time in 2019 - by accident, because he arrived late to an athletics meet and it was the only race left. He was injured in January this year and only started focused preparation for London in February.

But the thing that interests me most about Sawe isn’t how he races. It’s how he trains.

His coach, Claudio Berardelli, described it last week: nine to ten days of hard training, then a break. Workouts combined with recovery, adjusted depending on how Sawe’s body responds.

On hard days he pushes hard. On recovery days he actually recovers.

So when race day comes, he has everything available.

Your brain is a battery

Obviously we’re not running marathons. But the contrast is pretty obvious.

I’m guessing you start the average week with your calendar almost full. With no margin for anything unexpected. Certainly no time to reflect on where to focus your energy, let alone recover.

Because there’s nothing left, the thing that gets sacrificed is always the same: the thinking, the strategy, the stuff that never feels urgent but ends up mattering most.

It's not a willpower problem, and Samuel Marcora, a sports scientist, showed why.

He took two groups of people, of a similar age and same fitness level. One group watched a documentary for 90 minutes. The other spent 90 minutes doing a cognitively demanding task.

Then both groups did the same cycling endurance test. The mentally tired group gave up 15% earlier.

Now consider the typical working day for you and your team. Emails from the moment you wake up, context-switching all morning, back-to-back meetings - by mid-afternoon your brain is done. And you’ve probably still got a few hours of work to go.

We’ve only got about four to five hours a day of our best thinking, so why aren’t we pickier about what gets them?

Share Ollie on Work

Focus, not volume

Back to running for a moment.

When Roger Bannister broke the four-minute mile in 1954, he was training a fraction of the volume of his main rival - John Landy was running roughly five times more distance per week.

But Bannister - who was also a medical student and couldn’t train full-time - had figured out that the bottleneck to him performing at his best wasn’t endurance. It was his body’s ability to process oxygen and lactic acid at exactly the pace needed for four minutes. So all of his training was built around that one thing.

As you know, he broke the record first.

David Epstein and Cal Newport were chewing over this exact idea on Newport’s podcast this week. Epstein’s new book Inside the Box is full of examples of the benefits of constraints.

He tells the story of a company that makes custom gearboxes - every one unique and built to order. They were taking a year to deliver each one and when they looked at why, they found, on average, people were switching between tasks more than 50 times a day.

To fix the problem, they introduced one rule, one constraint: you can’t start a new design until you’ve finished the current one.

Within months, three times as many designs were going out the door. Delivery time dropped from a year to two months.

Again it’s easy to see a parallel here with how many teams are operating right now - and in this case, it’s a problem made worse by the ease with which AI can help you write a report, generate code, or create marketing campaigns.

Just because you can, doesn’t mean you should.

Race days and training days

Sabastian Sawe doesn’t run fewer miles than anyone else (200km a week seems pretty hefty!), but he and Berardelli are deliberate about which days are for pushing and which are for recovering.

They designed constraints.

I’ve been writing for the past few weeks about how companies should think about AI. But none of it matters if nobody has the headspace to act on any of it. Every leader I know agrees they should be experimenting more. Almost none of them have a single unprotected hour in their week to do it.

In this case, the biggest bottleneck isn’t technology, it’s their time.

Very unlikely you're going to break a world record this week, but you might need an hour to think clearly about something important. Your calendar won't give you that by default. You'll have to design the constraint yourself.

Thanks for reading.

Ollie

Share

Ollie on Work is a weekly newsletter about what I’m learning from building with AI, advising leadership teams, and trying to bridge the gap between what technology can do and how businesses actually work. If someone forwarded this to you, you can subscribe here:

In Jiro Ono’s sushi restaurant in Tokyo, the apprenticeship starts with cleaning.

For months, that’s all you do. You clean, you watch, you say yes. After about four months, you’re allowed to touch fish. After years, you might stand behind the counter.

One apprentice made the same egg dish over two hundred times before Jiro deemed it acceptable. When he finally got the nod, he cried.

Around a decade. That’s how long it takes before an apprentice is considered ready to make sushi for a customer.

Rice is the thing that reveals why. It accounts for about 80% of what makes great sushi, and there’s no recipe. The moisture content changes with the season, the age of the grain, the weather that morning. The only way to learn it is to do it, badly, for years, until your hands know what your brain can’t put into words.

The apprenticeship problem

The most obvious short-term impact of AI on jobs is on younger people.

In the US, recent graduate unemployment hit its highest level in over a decade last year. In the UK, youth unemployment just overtook the EU average for the first time on record. Entry-level hiring is slowing in many parts of the labour market. AI adoption at work is being led not by the youngest employees but by higher-earning, more experienced workers.

I speak to dozens of businesses every month and I’m hearing the same thing - a feeling, from leadership at least, that AI can now do a lot of the work we’d typically give to people just starting out.

They’re not entirely wrong.

AI is very good at producing work that looks like it came from someone who knows what they’re doing. And experienced people can usually tell when it’s actually good - that’s why they’re comfortable using it. They’ve spent years developing the instinct that lets them spot when something’s off.

The problem is where that instinct came from.

It came from doing the work badly for years. From producing stuff, being told it wasn’t good enough. Developing a feel for when something isn’t quite right - the kind of thing you can’t get from reading about it. The reps.

AI won’t tell you your rice is sh*t. It’ll tell you it’s excellent and move on to the next question.

And if we stop letting juniors do that work, the next generation won’t have the instinct that today’s leaders can rely on.

Share Ollie on Work

The value of a fresh pair of eyes

That’s the long-term cost. There’s a short-term one too.

Juniors aren’t just trainees-in-waiting. They bring something to a team that experienced people can’t, because they don’t carry the baggage of how things used to be done. They ask the questions you’ve stopped asking. They notice the things you’ve trained yourself not to see.

Removing them from teams doesn’t just delay the problem. It makes today’s work worse too.

So before you automate a task that a junior used to own, ask what they were learning by doing it. If the answer is genuinely nothing much, then fine - automate it. But if they were developing an instinct for what good looks like - the slippery idea of ‘taste’ that’s become so popular in the AI era - you need to replace that learning with something more deliberate.

So who’s learning to make the rice?

Thanks for reading.

Ollie

Ollie on Work is a weekly newsletter about what I’m learning from building with AI, advising leadership teams, and trying to bridge the gap between what technology can do and how businesses actually work. If someone forwarded this to you, you can subscribe here:

In 1998, thirty thousand Americans were asked two questions: how much stress have you experienced this year? And do you believe stress is harmful to your health?

Eight years later, the researchers checked who had died.

High stress increased the risk of dying by 43 percent. But only for people who believed stress was harming them. People who reported high stress but didn’t see it as harmful had the lowest death risk in the entire study - lower even than people who reported very little stress at all.

The researchers estimated that over those eight years, 182,000 Americans may have died prematurely. Not from stress. From believing stress was bad for them.

That story comes from Kelly McGonigal’s book The Upside of Stress. It came back to me after reading something Christine Armstrong wrote the other day.

Christine’s old boss used to tell her: “never be fragile.” Great advice, she said, but rather out of fashion nowadays, quoting people avoiding personal questions at work for fear of causing offence. And colleagues who don’t know each other well enough to trust each other, so they say as little as possible.

She’s right, of course.

We’ve spent the last five years optimising work for individual comfort - remote, async, fewer awkward conversations, less proximity - and somewhere along the way we’ve confused ‘safety’ with avoidance.

The intention was good. But the result is that we’ve quietly built organisations full of people who aren’t used to being uncomfortable anymore.

Share Ollie on Work

The worst possible timing

AI demands exactly the qualities we’ve been letting atrophy.

On an individual level, this could be the acknowledgement that some of the skills you’ve spent years developing may not be as useful any more - and the willingness to pick up a tool you don’t understand and fail with it for an hour to learn something new.

At a team level, it’s most likely something like having the confidence to say “this process we’ve been running for three years is a waste of time” and deal with the silence that follows. Or the patience and courage to try something, show it to someone, and hear that it doesn’t work.

All of it requires a tolerance for friction that we’ve spent five years systematically removing.

Nassim Taleb has a word for systems that get stronger under stress: antifragile.

Fragile breaks. Robust survives. Antifragile improves. Most companies aren’t any of the three. They’re just...comfortable.

The team that never disagrees, the leader who never hears bad news, the organisation where everything runs smoothly right up until the moment it doesn’t. That’s comfortable.

An antifragile team has difficult conversations and comes out more aligned, not less.

Start something uncomfortable

McGonigal’s point is simple: stress isn’t the enemy. The belief that stress is the enemy is the enemy.

If you’ve been putting off getting to grips with AI, the discomfort of not knowing what you’re doing isn’t a sign you’re not ready. It’s what getting ready feels like. Start before you have a plan.

If there’s a conversation you’ve been avoiding - about your career, about how the team works, about something that’s broken - the willingness to raise it is the thing. You don’t need the answer before you walk in.

And if you can see problems in how your company operates but you’ve been keeping quiet because it’s easier - the ability to say what needs to change, and bring people with you while you do it, is one of the few skills that becomes more valuable the more AI can do.

Machines are very good at optimising what exists. They are useless at telling you it shouldn’t exist.

Thanks for reading.

Ollie

Share

Ollie on Work is a weekly newsletter about what I’m learning from building with AI, advising leadership teams, and trying to bridge the gap between what technology can do and how businesses actually work. If someone forwarded this to you, you can subscribe here.

You know that route you take every morning without thinking?

The one where your legs just carry you and your brain is somewhere else entirely?

You probably couldn’t tell me when you last considered whether there’s a better way to go. It works. You stick with it.

On the evening of February 4th, 2014, millions of London commuters had that choice taken away from them. Tube workers walked out on a 48-hour strike. 171 out of 270 stations closed.

Most people were furious. But something unexpected happened.

Researchers from Cambridge and Oxford got hold of anonymised Oyster card data from around 18,000 regular commuters - people whose journeys were completely predictable in the weeks before the strike. The same station in and out, every single morning.

During the strike, half of them started and ended their journey at a different station. Forced out of their routine, they improvised.

Here’s the interesting bit. After the strike ended and every station reopened, about 5% of the most habitual commuters permanently switched their route. They’d accidentally discovered something better - faster, less crowded, more pleasant - that had been available to them all along.

They’d been commuting the same way for years. Some of them, presumably, for decades. It took someone else shutting down their usual option to make them notice.

Herbert Simon, the Nobel-winning economist, had a word for this: satisficing.

It means settling on a solution that’s good enough rather than continuing to search for the best one. Most of the time, that’s the right strategy. Life is too short and too complex to optimise everything.

The problem is when “good enough” calcifies into “the only way we do things.” When nobody questions it anymore. Not because the process is bad. Because it’s functional. It works. Nobody’s complained. So nobody’s bothered doing anything to improve it either.

Share Ollie on Work

The belts and shafts problem

Last week’s piece was about understanding what a job really involves before you automate it.

This is the harder question underneath it: should the job exist in its current form at all?

In the 1890s, factories started replacing their steam engines with electric motors. On paper it should have changed everything. In practice, almost nothing changed for decades. Productivity barely moved.

Because the factories were designed around steam. One enormous engine in the basement, driving everything above it through a complicated system of belts, shafts and pulleys. When electric motors arrived, owners ripped out the steam engine and bolted in an electric one.

And the problem was they stuck with the same layout, the same belt and the same shafts.

The new technology was doing the old job.

The real gains came much later, when someone worked out you could give every machine its own small motor. That let you redesign the factory floor entirely - placing machines where they made sense, not just where the belts reached.

Productivity exploded.

Most AI projects right now are in the belts and shafts phase. Drop an AI tool into an existing workflow, get a marginal improvement, declare victory. But the process itself - designed for an era when humans did everything by hand - goes completely unexamined.

You’ve made a slightly quicker commute but on the wrong route entirely.

Manufacture your own tube strike

Now, ideally, you don’t want to wait for a crisis to rethink how you work. But you can simulate the conditions for one?

Pick one workflow that nobody's questioned for years.

Get the people who do the work in a room.

Map what actually happens.

Then ask: if we were starting this today - with the tools, the team, and the information we have now - would it exist, and if so, would we build it this way?

You might find that you would. Fine. At least you’ve pressure-tested it.

But in many cases, you’ll find a process - a way of doing things - that looks a lot like a commuter who’s been taking the Circle Line for fifteen years when there’s a pleasant walk from Liverpool Street that would have been better all along.

Thanks for reading,

Ollie

Share

Ollie on Work is a weekly newsletter about what I’m learning from building with AI, advising leadership teams, and trying to bridge the gap between what technology can do and how businesses actually work. If someone forwarded this to you, you can subscribe here:

Subscribe now

New Jersey is the last state in America where it’s illegal to pump your own gas.

Every station has an attendant. Someone comes to your window, fills your tank, maybe checks your oil. It’s been this way since 1949. Multiple attempts to change the law have failed - not because of some powerful gas station lobby, but because 73% of New Jersey residents prefer it this way.

In a country that self-serviced everything else decades ago - checkouts, check-ins, banking, boarding passes - the people of New Jersey are actively choosing to pay a bit more to have a human do something they could easily do themselves.

They’re not stupid. They’re not lazy. They just understand something that most businesses have forgotten.

The doorman fallacy

There’s a concept I keep coming back to, popularised by Rory Sutherland. He calls it the doorman fallacy. It goes like this.

A consultant visits a hotel. Sees a doorman. Defines the doorman’s job as “opening the door.” Replaces him with an automatic door. Claims the cost saving. Leaves.

Five years later, what the hotel can charge per night has dropped. There are people sleeping in the entrance. The most loyal guests have quietly moved on. But the consultant who made the recommendation is long gone - and nobody connects the decline to the decision.

Because the doorman was never just opening the door. He was hailing taxis. Recognising regulars. Providing security. Signalling status. Making guests feel like they’d arrived somewhere worth arriving at. None of that was in the job description. None of it appeared on a spreadsheet. All of it was real.

The saving was easy to measure. The value destruction was invisible - until it wasn’t.

Share

Where the conversation goes wrong

I’ve been in a lot of these conversations recently. The ones where a team sits down to figure out where AI fits into what they do. And the thing that strikes me every time is how early the conversation goes wrong.

Not because people are rushing to automate things they shouldn’t. Most aren’t anywhere near that stage yet. The problem is more basic than that. People talk about “workflows” and “processes” without ever having properly defined what those actually are. The word gets thrown around in every meeting, every strategy document - but when you ask someone to describe, step by step, what actually happens in a process they own, most people can’t do it. Not because they’re incompetent. Because nobody ever asked them to.

So the first thing I tend to do isn’t talk about AI at all. It’s much simpler. I ask people to describe what they actually do.

I was in a session recently where a team was looking at how they handle incoming data from external partners. On paper, it’s straightforward: data comes in, gets checked, gets logged. The kind of thing that looks perfect for automation.

But when I asked the person who actually does this work what their day really looks like, a completely different picture emerged. They’re not just processing data. They’re spotting when something looks off before it becomes a problem. They’re maintaining a relationship with the people sending it. They know which partners are reliable, which ones cut corners, and which ones need chasing before a deadline. They’re an early warning system dressed up as an admin function.

That conversation alone - before anyone mentions a single tool - changes everything. It changes what you’d choose to automate, and how.

Automate the data processing? Absolutely. But if you remove the person without understanding what they were really doing, you don’t just lose an employee. You lose the early warning system. And you won’t notice until something blows up.

Among the companies that are actually deploying AI - and honestly, it’s still not that many - the default is to define a role by its most visible task, automate that task, and claim the saving. Without ever asking what invisible value gets destroyed in the process.

The better question isn’t “which roles can we automate?” It’s “what’s actually happening here that we haven’t bothered to articulate yet?”

And even once you’ve understood the full picture, there’s a harder question underneath it - whether the process should exist in its current form at all. But that’s for next week.

One thing you can do this week

Before you automate anything, go and sit with the person who does the work. Not their manager. Them.

Ask them what they actually do all day - not what the process document says. Listen for the things that don’t have names. The judgment calls. The relationship maintenance. The pattern recognition that only comes from doing the same thing, with the same people, for years.

That’s where the real value lives. And that’s what no spreadsheet will ever show you.

Share

Subscribe now

I was in Paris last weekend with my wife. We spent most of the time doing what the French do best - sitting around eating. Slowly, properly.

Three courses at lunch because why not. A coffee that lasted an hour because nobody was in a rush.

It reminded me of a study I read a few years ago by a researcher called Ashley Whillans. She and her colleague Romain Cadario surveyed over 10,000 people about how they eat, and found something interesting, but probably not surprising for anyone who’s into national stereotypes.

The French spend more time than anyone eating their meals. By which I mean, sitting down, tasting the food. Being present for it.

Americans, on the other hand, spend more time than anyone choosing what to eat - researching restaurants, scrolling through delivery apps, reading reviews, debating menus, comparing options - but much less actually eating it.

Same amount of time committed to the overall activity. Completely different allocation.

And guess what - the French reported significantly more satisfaction from the experience and less stress around it.

The Americans weren’t making better choices with all that research. They were just postponing the moment of commitment.

I realise I’m about to do the thing where someone connects a holiday anecdote to a business insight, and I apologise in advance, but… I’ve been thinking about that study a lot since I got back, because I keep seeing the same pattern in a completely different context.

The menu problem

A leadership team decides AI is important, so they start evaluating.

They read reports. They book demos. They run a comparison spreadsheet. They attend a webinar. They set up a working group to assess the options. Someone writes a proposal. The proposal needs sign-off. The sign-off requires another meeting. Someone raises a procurement question. Legal gets involved.

Three months later, they haven’t deployed a single tool into a single workflow. But they’ve spent an enormous amount of time and energy on the project, so it feels like progress.

This is the menu problem. All the time goes into choosing. None of it goes into eating.

Meanwhile, down the corridor, someone on the team has been quietly using ChatGPT or Claude every day for the last year. They’ve figured out which tasks it’s brilliant at and which ones it’s useless for. They’ve built little workflows that save them hours a week. They’ve made mistakes and learned from them.

They have more practical knowledge about AI than the entire working group - not because they’re smarter, but because they started.

Share

Why we get stuck

Because there are a few reasons companies get trapped in evaluation mode. The problem is that most of them feel perfectly rational from the inside.

Fear of picking wrong. The AI landscape is moving so fast that whatever you choose today might be obsolete in six months. So you wait. The thing is, waiting doesn’t reduce the risk - it just guarantees you learn nothing while you’re waiting.

The search for the perfect use case. Leaders want to find the highest-impact application before they commit. This sounds sensible but it’s backwards. You don’t know where AI will have the most impact in your business until you’ve used it in a few places and seen what happens. The best use cases reveal themselves through use, not through analysis.

Consensus as a delay mechanism. Getting everyone aligned before moving forward feels responsible. In practice, it means the most cautious person in the room sets the pace for the whole organisation. The French don’t hold a committee meeting before ordering lunch.

The Deliveroo data

Sticking with the food theme, there’s a nice piece of research that backs this up from a different angle.

A team at the Max Planck Institute partnered with Deliveroo and analysed 1.6 million food orders from 195,000 customers across 197 cities.

They found what you’d expect: when people tried a new restaurant, the average rating was lower than when they stuck with an old favourite. 4.26 versus 4.52. Exploration has a short-term cost.

But over time, the more people explored - the more new restaurants they tried - the higher their average satisfaction climbed. Because they kept discovering better options and dropping the duds.

The people who stuck exclusively with what they knew plateaued. The ones who kept trying new things kept improving.

And interestingly, people were actually more attracted to restaurants with fewer reviews. Not less. Fewer reviews meant more uncertainty - and uncertainty meant there might be an undiscovered gem that everyone else had missed.

The lesson isn’t “be reckless.” It’s that the discomfort of trying something new is the price of finding something better. And the longer you spend reading reviews instead of ordering, the longer you wait to start climbing.

Just pick one and start

If your team has been evaluating AI tools for more than a month without committing to one, stop.

Pick the best available option. Not the perfect one - the best available one. Give it to a small team. Point it at one real workflow - not a sandbox, not a test environment - a real thing with real stakes.

You’ll learn more in the first week of actual use than you learned in three months of demos. Some of what you learn will be that it doesn’t work the way you expected. Good. That’s information you couldn’t have got from a comparison spreadsheet.

Order something. Eat it. Then decide if you want to order it again.

The ROI isn’t in the selection. It’s in the savouring.

See you next week.

Ollie

Share

“Ollie ran an energetic, fun, informative and practical session with lots of takeaways for how to make the workplace more effective and efficient. His session was a highlight of the collaborative leadership course.”
Shân Wareing, Vice Chancellor & CEO, Middlesex University

This is the final part of From Zero to World-Class AI Manager. If you’d like to discuss how I can help your team, book a free, no-obligation call HERE.

Share Ollie on Work

You’ve spent seven weeks building AI capability.

But personal productivity gains have a shelf life. If it stays with you, it dies when you leave - or just fades when you get busy. The real value comes from making it stick across a team.

This week is about how to share what you’ve learned without being that person who won’t shut up about AI. Oh, and how to know if any of this is actually working.

Why people hide their AI wins

Remember, there’s a reason your colleagues aren’t sharing what’s working.

Fear of being given more work. “Oh great, you finished early - here’s more.” Worry they’ll look like they’re cheating. Concern about judgment from people who haven’t started yet.

The fix isn’t mandating adoption. It’s making it safe to experiment, and creating lightweight ways to share.

How to share without preaching

Don’t: “Everyone should use AI!”

Do: “This saved me 3 hours on the quarterly report. Here’s how, if you’re interested.”

The difference matters. The first sounds like an edict. The second is an invitation. People adopt things when they see someone like them getting results - not when they’re told to.

Three ways to build momentum

1. Create a Slack or Teams channel for experiments

Not a channel where you broadcast announcements. A place where anyone can post “I tried this, here’s what happened.” Low stakes. Failures welcome. The weird output that made you laugh gets as much engagement as the polished workflow.

Build a one-page guide

This is your call to action from this series. Create a simple doc with:

• 3 use cases that actually worked for you

• The prompts you used

• What to watch out for (where AI gets it wrong)

• How long it took to get good at it

Share it with 2-3 people who’d benefit. Ask: “Would you try this?”

That doc becomes the start of a shared prompt library. Others add to it. It becomes institutional knowledge that gets better over time.

3. Run a hackathon (the non-technical kind)

Get people from different teams together. Give them a real scenario - a client brief to respond to, a messy situation to untangle, a process to improve. A few hours. No coding required.

The goal isn’t to build something technically impressive. It’s to see what’s possible when you combine different perspectives with AI capability. And to make experimentation feel normal.

This is a great idea for your next away day, by the way.

Knowing if it’s working

Measuring this stuff is never going to be perfect. And let’s be honest, if you’re now using AI a bunch more, you’re probably not actually working less. That’s not really the point.

What’s more likely is that you’re doing more. Or doing different work, Or doing the same work but better. That’s harder to measure than “I saved 3 hours.”

But here’s one simple way to get a rough sense of time saved.

The mini audit:

For one week, spend five minutes at the end of each day noting roughly how long you spent on different types of tasks. Not just AI - everything in your work day (and maybe even your personal life). Don’t overthink it - just top-line categories and rough time.

Do the same thing a month from now. Compare.

You’ll see where time has shifted.

And you’ll notice something else: you become more intentional about what you’re actually spending time on. That’s valuable in itself.

What you’ve learned

Over the past eight weeks, you’ve learned what a workflow actually means, how to talk to AI like a colleague, how to fix your meetings, how to set up your AI to work better and faster, how to research without disappearing down rabbit holes, and how to use AI to think more clearly.

There’s plenty more to cover - agents, deeper automation, workflows that run without you. I’d love to hear what you want to read more of in 2026. Reply and let me know what would be most useful.

Thanks for following along.

Ollie

Share

Catch up on the previous 7 weeks of From Zero to World-Class AI Manager here:

• Week 1: You can’t lead an AI transformation you don’t understand

• Week 2: Talk to AI like a colleague

• Week 3: Fix your meetings before you automate them

• Week 4: Setting up your AI to help you work better and faster

• Week 5: AI research that actually works

• Week 6: How I built a product solo using AI research

• Week 7: Using AI to think

“After initially meeting Ollie at the IAB leadership summit, we were thrilled to be able to invite him to be the external speaker at our Coty summer conference.

Ollie’s session was captivating for the whole audience, as it not only broadly humanised some of the conversations bubbling around AI, but was also packed with practical applications that our team left eager to explore and implement.

Ollie’s skill is that he can make complex concepts easy to understand, and he inspired us all with real-world examples. If you’re looking for an engaging speaker to ignite creativity and knowledge amongst your teams, look no further!”

Susie Thompson, Senior Director Media & Communications at Coty

I’m working with leaders to take lessons like these into their organisations - through workshops, advisory, and speaking. If you’d like to discuss how I can help your team, book a free, no-obligation call HERE.

This week is about using AI to actually think – getting clarity, challenging yourself, and knowing when to stop

1. Learning new domains fast

I travel a lot for work. Different industries, different clients, different problems.

The challenge isn’t finding information. It’s building enough understanding to have a point of view - fast.

Before high-stakes meetings, I don’t just ask “tell me about this company.” I ask AI to help me understand their world well enough to ask smart questions and connect what I do to what they care about.

I have a meeting with [person/company] in [X days].

Context:
- What they do: [brief description]
- What’s happening in their industry: [any relevant trends]
- What I’m trying to achieve: [your goal for the meeting]

Help me:
1. Understand what people in their position actually care about day-to-day
2. Identify 3 angles I can take into the conversation that show I understand their world
3. Suggest 5 questions I could ask that would make me sound like an informed peer, not a tourist

The goal isn’t to become an expert. It’s to move from “I’ve heard of this” to “I have a perspective worth sharing.”

Other uses: Preparing for board presentations in unfamiliar sectors, or understanding new technology before you decide whether to invest time.

2. Untangling messy situations

Sometimes I can’t think clearly because there’s too much going on.

Projects competing for attention. Decisions that all feel urgent. Can’t see the wood for the trees.

This is about organising what you already know but can’t see clearly.

When my brain feels full, I dump everything out and ask AI to help me see it. Recently I pasted in a list that included speaking work, advisory projects, a product I’m building, and content - then asked it to help me separate what’s actually important from what just feels urgent.

I’m overloaded and can’t see clearly. Here’s everything on my plate:
[bullet list - projects, commitments, decisions, worries]

1. Restate my situation in no more than 10 bullet points
2. Separate those into: FACTS, ASSUMPTIONS, and FEARS
3. Given only the facts and my goal of [briefly state], what are the 3 most important priorities for the next 90 days?
4. For each, give me one action I can take this week

AI’s job here isn’t to decide for you. It’s to organise the mess so you can.

Other uses: Preparing for a difficult conversation. Working out what’s actually bothering you about a project. Separating real constraints from imagined ones.

Share Ollie on Work

3. Finding the real question

This is different from untangling. That’s about clarity. This is about realising you’re solving the wrong problem.

Often I’ll ask AI something and realise halfway through that I’m asking the wrong question.

A client says “we need help with X” - but the actual problem is Y. I think I’m stuck on a decision, but really I’m avoiding a harder question underneath it.

Here’s the question I think I’m asking: [surface question]

Context: [why this matters, what’s at stake]

1. What do you think the *real* question is that I’m trying to answer? Give me 3 possibilities.
2. For each, explain what decision would change if that were the real question.
3. Which one do you think matters most and why?
4. What should I ask myself to make sure I’m focused on the right problem?

Half the time, the breakthrough isn’t finding the answer - it’s realising you’ve been asking the wrong question.

Other uses: When you’re stuck and don’t know why. When a client’s brief doesn’t quite make sense. When you keep circling the same problem without progress.

4. Stress-testing ideas

I have a tendency to fall in love with my own ideas. Most people do.

So before I commit serious time or money, I ask AI to attack. Not “is this good?” but “why wouldn’t this work?”

Here’s my idea: [describe in 5 sentences or less]

Assume you’re a sceptical investor who’s seen a hundred pitches like this.

1. List the top 5 reasons this is weak, risky, or unlikely to work. Be harsh.
2. What are 3 warning signs that would tell me to kill this idea quickly?
3. If you had to keep the core insight but radically change the execution, what would you do?
4. Based on this critique - is this worth testing at all? If so, what’s the smallest test that would prove or kill it?

The value isn’t in AI being right. It’s in forcing you to defend your thinking before you’ve invested too much to change course.

Other uses: Before you hire someone you’re excited about. Before you announce something publicly. Before you invest serious money in something.

5. The rabbit hole warning

I have a tendency to over-research.

Sometimes it’s trivial. On Sunday, I spent too long working out whether 125 or 140 degrees was the “correct” temperature for slow-cooking beef. The stakes were low. The cost of being slightly wrong was basically zero. But I kept pushing for the “right” answer.

Sometimes it’s more dangerous - like spending weeks refining a product idea when five customer conversations would tell me more, faster, and with more accuracy. Using research as a sophisticated form of procrastination.

Now I try to catch myself:

I’ve been thinking about [topic] for [time].

Am I overthinking this? What’s the minimum I need to know before I act?

If AI tells me I’m in a rabbit hole, I try to listen.

Other uses: Any time you’ve been “researching” something for more than an hour. When you’re on your third draft of something that was fine on the first.

6. Knowing when to stop

The flip side of the rabbit hole - knowing when you already have the answer.

I’ve asked AI the same strategic questions multiple times. “Where should I focus? Which project should I prioritise?” The answers don’t change because there’s no new information. At that point I’m using AI to avoid committing.

You don’t need a prompt for this. You need a pen and paper.

Before you start, write down:

• What question am I trying to answer?

• What would “good enough” look like?

• How will I know when I’m done?

Just like going down a YouTube rabbit hole or doomscrolling on Instagram, AI can suck you in. And just because it seems smart doesn’t mean it’s any better way to waste your time.

The discipline isn’t just in thinking well. It’s in knowing when to stop thinking and act.

The point

AI is a powerful thinking partner - but only if you use it that way.

Most people prompt once, get an answer, and move on. The real value is in the back-and-forth. Using AI to learn faster, challenge harder, see clearer.

And knowing when to close the chat and just do the thing.

Thanks for reading.

Ollie

Share

Could I use AI to create a product on my own that would have previously required a whole team?

The problem

Understanding how and when to monetise content is tough.

Not that there’s a shortage of tools that can help you do it. In fact, media and entertainment businesses are drowning in monetisation tools. Hundreds of vendors but no real framework for making decisions.

I wanted to help solve this problem.

The method

Three types of AI research, looped until the right product emerged.

1. Synthesise interview insights

I sat down with 15 people in the industry for wide-ranging conversations on what’s happening right now, how their businesses have responded, and their perspectives on what was likely to happen in the near future.

I fed the interview transcripts into ChatGPT:

I’ve conducted 15 interviews about subscriptions and advertising. [Attach transcripts]

Synthesise key patterns:
- What challenges come up repeatedly
- Where people disagree or have different approaches
- Gaps between what they want and what tools provide
- Non-obvious insights about what’s actually working

Format: Themes with supporting quotes, then strategic implications.

The analysis picked up on something I was hearing.

When it comes to the technology driving content and monetisation decisions, people’s problems weren’t necessarily about how to use specific products. The challenge seemed to be connecting tool capabilities - whether analytics, identity and access, or subscription management - to their business model and audience behaviour.

In other words, it wasn’t a tools problem. It was a decision-making problem.

People had plenty of options but no framework for figuring out which ones actually fit how their business worked and how their audience behaved.

That told me what I needed to research next.

(Examples of other uses for synthesising call transcripts to generate insights: L&D teams synthesising training feedback to redesign programmes. Product managers finding patterns across user research. Marketing analysing customer feedback.)

Share

2. Track industry trends

I now had a sense of what was going on, but fifteen people is hardly a significant sample size. So, the next step was to zoom out and use AI Deep Research to generate analysis of the wider market.

Research: Current trends in media monetisation for content businesses

Focus on:
- Shifts in business models (what’s gaining/losing ground)
- New approaches people are testing
- Failed experiments and why they didn’t work
- Emerging patterns in audience behaviour

Prioritise recent news, operator posts, case studies from 2024-2025

This surfaced patterns I wouldn’t have found doing manual research alone - mainly because of the breadth of sources.

• AI bot traffic was inflating dashboards while revenue stayed flat - metrics that looked like growth but weren’t

• The biggest blocker wasn’t content or pricing - it was the infrastructure. When simple changes require engineering time, you’re maintaining systems, not running a business

• Everyone talking about combining revenue streams while the underlying systems can’t share data

Cross-referencing these findings with my interviews showed what people were saying versus what was actually happening.

I had the big picture. Now I needed the details.

(Examples of other uses for Deep Research to validate customer insights: Product marketing teams tracking competitor positioning shifts. Strategists researching how others handled transitions you’re planning. Agency teams mapping client pain points to shape their pitch.)

3. Deep dive vendors

I now knew the trends and the problems. Time to research the actual vendors.

Deep Research: [Vendor name] - comprehensive profile

Include:
- Core capabilities vs marketing claims
- Pricing structure and hidden costs
- Integration complexity
- Who it actually works for (use cases, company size)
- Common complaints from users

Format: TL;DR, strengths/limitations, implementation reality check

I ran this for every major vendor whose name kept coming up in my research - companies like Adobe Analytics, Auth0, Google Analytics and Paddle.

I ended up with detailed research reports on each company, which I knew I needed to validate.

First with multiple AI platforms, cross-referencing outputs.

Then, importantly, I got real people back in the loop - asking for their experiences implementing the systems, building on the platforms, assessing the support they’d received along the way.

Now I had everything I needed to start building.

(Examples of other uses for Deep Research on specific products: Procurement teams comparing SaaS vendors before negotiations. HR leads assessing applicant tracking systems. Operations comparing logistics providers.)

The result

This process took me a few months - mainly because of the human conversations. But I know from experience it would have been impossible in the past without a full team working flat out.

In fact, until I worked with a developer to build the Monetization Works website - which I’d designed myself using Lovable, by the way - the only support I had was from AI.

Monetization Works is now live here.

You can find vendor profiles on companies like Stripe. And you can read comparisons of products in the same category like Recurly vs Zuora.

The profiles don’t read like feature lists because they’re not based on marketing material. They’re based on what people actually experience. Where tools fail. Which trade-offs matter.

And the added bonus - I designed the content so it would rank for searches people were actually making using LLMs like ChatGPT and Gemini.

Someone searching for “Stripe alternatives” or “Recurly vs Zuora” finds Monetization Works because the content answers strategic questions, not surface comparisons.

That’s Answer Engine Optimisation (AEO) - I built it that way from the start.

The research creates the product. The product creates its own distribution.

The point

Strategic research used to require a team because one person couldn’t move fast enough through the cycles.

AI compresses those cycles from weeks to days. You can operate solo at a level of depth that used to need multiple people.

But here’s the trap - AI will confidently tell you everything sounds great. It’ll validate every insight, support every hypothesis. It’s easy to build an entire product on research that sounds smart but doesn’t reflect reality.

The discipline is validation.

Checking with people who actually use these tools. Who’ve lived these problems. Who know where theory and reality diverge.

AI accelerates research. Humans tell you if it’s real.

Thanks for reading and good luck on your projects.

Ollie

Share

Share

I just scrolled through my ChatGPT history - past the client work and strategy questions, it’s a mess.

How to isolate my son’s voice from a noisy video clip. Whether athlete’s foot cream is safe for an eight-year-old. A detailed plan for connecting a Technics turntable through Sonos with a Cambridge pre-amp.

Your chat history probably looks similar. Random life stuff you’d have previously Googled.

But AI can do way more than answer questions.

Most people know AI is good for quick answers. What they haven’t discovered yet is that it can do research that used to take hours - and do it better than you could manually.

Not just faster. Actually better.

Finding patterns you’d miss. Synthesising insights from dozens of sources. Connecting dots that aren’t obvious when you’re reading one article at a time.

Beyond a quick search

My family and I are going to Mexico’s Caribbean coast over Christmas.

Instead of Googling “best day trips Riviera Maya,” I gave ChatGPT’s Deep Research this:

We’re a family of five with three school-age kids planning a two-week holiday on Mexico’s Caribbean coast over Christmas and New Year. We’ll be staying in three different coastal areas, driving between them, and want to mix relaxing beach days with local exploration.

Do a Deep Research synthesis to identify the most rewarding and family-friendly day trips and activities across this region.

Focus on:
1. Top 5–7 day trips suitable for kids aged roughly 6–13
2. Best times of day to visit (crowds, weather, energy levels)
3. Travel logistics: driving distances, parking, food options, facilities
4. Seasonal insights for late December to early January

Prioritise:
- Real 2024–2025 visitor experiences over generic travel sites
- Recent, verifiable sources (TripAdvisor, Reddit, Google Maps reviews)
- Balanced days that combine activity and rest

Format: For each day trip:
- Title + brief description (max 100 words)
- Ideal visit timing and duration
- Family-specific notes (ease, facilities, food, safety)
- Seasonal considerations

Conclude with a summary table comparing each option.

30 minutes later: an 18-page synthesis covering eight destinations.

This includes advice on specific timing, like “arrive at Chichén Itzá by 8 AM to beat the ~5,700 daily visitors.”

Seasonal notes like “late December means cooler cenote water - still swimmable but bring towels for after.”

Every claim is cited, each piece of advice is linked back to its source - TripAdvisor, Reddit, local blogs etc. That lets me verify anything that matters and pick up on hallucinations.

Could I have pieced this together manually? Yes, if I spent a full day reading reviews and forums.

But Deep Research did it for me in half an hour - and I can interrogate it further if I want briefer conclusions or clarification.

As ever, for important research, run it through multiple tools and cross-reference (see last week’s newsletter for more on this).

Why deep research takes your work up a level

So that’s really useful for planning a holiday, but how do you use this for your work?

Well, let’s be honest, most work research is surface-level. You Google around, read a few articles, piece together something reasonable. You know what everyone else knows.

If done right, AI research creates differentiation.

When you structure it properly, AI doesn’t just find information faster - it finds insights you wouldn’t discover manually. Patterns across dozens of sources. Non-obvious connections.

Share Ollie on Work

Three examples

1. Competitive intelligence

Research: [Competitor]’s go-to-market strategy over the past 18 months

Focus on:
- How they’re positioning against us specifically
- Changes in messaging, pricing, target market
- Which customer segments they’re winning vs losing
- Recent hires indicating strategic shifts

Prioritise:
- Customer review sites and buying decision discussions
- Job postings that reveal priorities
- Patterns in case studies and customer logos

Format: Strategic overview, then tactical details. Flag anything suggesting they’re moving into our territory.

2. Strategic planning

Research: Companies that successfully pivoted from SMB to enterprise in B2B SaaS

Focus on:
- What changes were required (product, team, sales, pricing)
- How long transitions took
- Early indicators it was working/not working
- Common mistakes that slowed transitions

Prioritise:
- First-hand accounts from founders/operators
- Companies of similar size/stage to ours
- Pivots in the past 5 years

Format: Phase-by-phase breakdown with milestones and warning signs.

3. Content strategy

Research: Underexplored angles in [your market/topic area]

Focus on:
- Topics that should be getting attention but aren’t
- Angles that differ from standard approaches
- Questions your audience asks that competitors aren’t answering

Prioritise:
- Community discussions, forums, subreddits
- Comments on competitor content revealing unmet needs
- Adjacent industries dealing with similar issues

Format: 10 underexplored angles with explanation of why it matters and why competitors miss it.

When to use this

Deep Research takes at least 10 minutes. Longer if you go super deep into a topic. Don’t use it for everything.

Use it when:

• Research informs a significant decision or needs synthesis across multiple sources

• Others will see this work (clients, leadership, your team)

• Understanding patterns matters as much as facts

Don’t use it when:

• You need a quick fact

• The answer exists in one place

• Speed matters more than depth

“What is design thinking?” doesn’t need Deep Research.

“How do companies actually implement design thinking in practice - what works and what’s just theory?” does.

The point

Being a world-class AI manager isn’t about knowing every tool.

It’s about understanding which capabilities create genuine competitive advantage - and using them when it matters.

AI research is one of those capabilities. Done properly, it doesn’t just save time. It produces insights you couldn’t get manually. Patterns you’d miss.

It can help you get your job done better and faster.

But if in the meantime, it helps you plan a better holiday, then why not start there.

Good luck experimenting.

Ollie

Next week: How I used AI Research as part of an end-to-end AI-supported workflow to build Monetization Works - from initial market research through to product framework.

Share

But that doesn’t mean there’s no room for quick wins.

In fact, across all three workshops, the same tactical questions kept coming up. Small setup moves that mean the output of work with your LLM of choice is faster and higher value (large-language-model = ChatGPT, Claude, Gemini, etc).

These are things I’ve discovered that most people don’t know are possible or haven’t thought to use. But with 20 minutes of setup, they’ll save you hours every month.

So, this week, let’s focus on some quick wins.

1. Tell AI who you are (2 mins)

Most people open ChatGPT or Claude and just start typing. The AI has no idea who you are, what you do, or why you’re using it.

That’s like having a conversation with someone who has no context about your life. Everything has to be explained from scratch every time.

Fix it in two minutes:

Settings → Personalization → “More about you”

If you want to keep it simple:

I’m an account director in adtech, based in London, using AI for client reporting and campaign analysis.

Or if you want to give ChatGPT real context for your work, you can write something like mine:

I’m a founder, writer, and advisor based in the UK. I help leadership teams use AI to rethink how they work and make better decisions. 

I run Bamyazi, an advisory focused on practical AI adoption, and I write the “Ollie on Work” newsletter about systems, leadership, and the future of work. 

I also run Monetization Works, a project helping media and content businesses design smarter monetization stacks.

That’s it. Now every conversation starts with this baseline context. The AI won’t give you generic answers about “companies” - it’ll give you answers relevant to your actual work.

One caveat - as with most things AI, this is not completely foolproof. But I’d give it a 99% success rate.

2. Tell AI how to respond (2 minutes)

This is where it gets useful.

AI has a default personality, and frankly, it’s annoying. It’s overly enthusiastic. It agrees with everything. It adds unnecessary fluff.

You can change this.

Settings → Personalization → “Custom Instructions”

Here’s what I use:

I like my responses succinct but engaging. No flowery language. Never give me information you can’t validate with evidence. If you don’t know the answer, tell me briefly. Don’t agree with me if it’s not a fundamentally sound idea. Always challenge me. Adopt a skeptical, questioning approach. Never use em-dashes.

That last bit about em-dashes is personal preference (they’ve become the telltale sign of AI writing), but the critical part is this:

“Don’t agree with me if it’s not a fundamentally sound idea. Always challenge me.”

AI is programmed to be agreeable. This one instruction makes it useful instead of just pleasant.

Share Ollie on Work

3. Set up a project (10 minutes)

If you work on the same types of tasks repeatedly - client reports, campaign planning, product specs - you’re wasting time re-explaining context every time.

Projects solve this.

Think of a Project like a folder that remembers everything about a specific piece of work. All your context, all your documents, all your preferences for how to handle that particular task.

For example, if you manage multiple clients, create a Project for your biggest client:

New Project → “Acme Corp Account”

Add:

• Previous campaign reports

• Their brand guidelines

• Notes from key meetings

• Their objectives and KPIs

Then add custom instructions specific to this client:

Acme Corp prefers data-heavy reporting with minimal narrative. Always reference their three core KPIs: CAC, LTV, and engagement rate. They’re in fintech so avoid casual language.

Now, when you work on anything Acme-related, open this Project. The AI already knows the context. You don’t have to re-explain their business every time you write a report.

This 10-minute setup will save you loads of time in the future.

4. Cross-reference everything important (ongoing)

Here’s the thing about AI - it gets stuff wrong. And it gets it wrong confidently.

The quickest way to catch errors is to use AI to check AI.

If Claude writes something important - a client report, a data analysis, a strategy doc - don’t just assume it’s right.

Copy the output.
Paste it into ChatGPT.
Ask: “What’s wrong with this?”

For some reason, LLMs are more willing to criticise each other than themselves.

ChatGPT will often spot errors that Claude missed. Then you can take that feedback back to Claude and say, “ChatGPT thought X was wrong - what do you think?” And Claude will either fix it or explain why ChatGPT was mistaken.

This back-and-forth catches most hallucinations before they reach your clients or your boss.

5. Turn off what you don’t want (2 minutes)

AI learns from your conversations. That’s useful for work context. It’s less useful when it remembers that random chat you had about roast chicken recipes or your kid’s science project.

Clean it up.

Settings → Personalization → “Memory”

Scroll through what it’s learned about you. Delete anything irrelevant to how you actually use AI.

If you share your account with someone else (like a partner), this is especially important. Otherwise, in my case, the AI gets confused about whether you’re asking about birth education courses or B2B marketing campaigns.

Quick fix: Add specific memory entries like:

When I mention work projects, I’m referring to B2B marketing, not birth education courses.

Two minutes to stop your work AI from getting confused by non-work conversations.

Faster, higher-quality work

None of this is AI mastery. It’s setup work.

But it’s the difference between AI that feels like starting from scratch every time and AI that actually knows your work, challenges your thinking, and saves you real time.

Do these five things this week. Twenty minutes total. Let me know how you get on.

Next week: how to research anything 10x faster.

See you then,

Ollie

Share

Atlassian surveyed 5,000 knowledge workers: 72% say meetings are ineffective, 78% say they can’t get work done because of those ineffective meetings, and half end up working overtime because of meeting overload.

If you’re spending half your week in meetings that achieve nothing, AI-powered meeting notes won’t help. You’ll just have better documentation of wasted time.

So this week isn’t just about reducing meetings (though that helps). It’s about improving the ones you actually need - before, during, and after.

Last week, you learned to brief AI properly. This week, you’ll use that skill to run fewer, better meetings.

Before the meeting

Show up underprepared and the quality of the meeting inevitably suffers. Failing to plan is planning to fail, and all that…

Your first step to a better meeting is generating a tight agenda - this only needs to take a couple of minutes.

Use the same five-part brief from last week:

I’m a [role] leading a meeting about [topic] with [attendees].

Goal: [what needs to be decided/achieved]

Context: [current situation, why we’re meeting]

Constraints: 25 minutes, needs to end with clear next steps

Create a tight agenda with:
- 3-4 topics (time-boxed)
- One clear decision point per topic
- Pre-work for attendees
- Success criteria

Then list what assumptions I should verify before the meeting.

Brief yourself on people and documents

Meeting someone new?

I’m meeting with [name/role] tomorrow to discuss [topic].

Research their recent work, priorities, and likely concerns.

Return:
- 3 key facts about their current focus
- Likely objections or questions they’ll raise
- 2 ways to frame this topic for their priorities

List sources to verify.

Or if you realise at the last minute that you’ve not read the 40-page doc you were supposed to (I’m not advocating skipping essential research by the way - but, hey, it happens…):

Summarise this document for a 30-minute strategy discussion.

Focus on:
- Key recommendations
- Data that supports/contradicts our current approach
- Questions or concerns to raise
- What’s missing

Format as discussion prompts, not bullets.

During the meeting

You’re trying to facilitate, take notes, watch the clock, and think - all at once.

That’s why recording and transcribing meetings is fundamentally a good thing.

Of course, before you start recording you need to get everyone’s permission. But don’t be afraid - frame it positively:

“I’m recording this so everyone gets clear notes and can focus on making this meeting as valuable as possible. I’ll make sure the follow ensures you know exactly what’s expected, what the priorities are, and who’s doing what. Any concerns about that?”

Most people will appreciate the clarity. The few who don’t will tell you.

If you can - and some tools like Fireflies.ai let you set custom templates - start the meeting by telling AI what to capture:

This is a strategy meeting to decide [X].

Capture:
- Decisions made (with rationale)
- Action items (owner + deadline)
- Open questions for follow-up
- Disagreements or concerns raised

Ignore: introductions, small talk, off-topic tangents.

When you’re not frantically taking notes, you notice more. Who’s disengaged. What’s not being said. When to cut off circular discussions. When you’ve actually reached a decision.

Better meetings aren’t just about efficiency - they’re about everyone engaging and contributing in order to create progress.

After the meeting

Meeting ends. Everyone leaves. Nothing happens. Two weeks later you’re meeting again about the same thing.

That’s why you need to turn the transcript into structured notes:

Turn this meeting transcript into structured notes:

DECISIONS MADE:
- What was decided
- Rationale
- Who made the call

ACTION ITEMS:
- Specific task
- Owner
- Deadline
- Dependencies

OPEN QUESTIONS:
- What needs follow-up
- Who’s responsible

NEXT STEPS:
- When we meet again
- What needs to happen before then

Flag any commitments that seem vague or unclear.

Then draft the follow-up:

Draft a follow-up email based on these meeting notes.

Include:
- Quick recap of decisions
- Action items with owners
- Open questions
- Next meeting date

Keep it under 150 words.

While you’re on, identify who didn’t need to be there.

Look at your meeting notes. Who spoke? Who contributed to decisions? Who was just there?

Next time, send them the 2-minute summary instead.

I’ve set this process up with one of my clients and after a month, we realised 3 people in a weekly team meeting contributed once in 4 weeks. They stopped inviting them and sent them the summary instead.

All three preferred it - because guess what, they’ve got a million others filling their day - and the meeting went from 60 minutes to 40.

One last thing - should this meeting even exist?

Before scheduling your next recurring meeting:

I’m considering a [weekly/monthly] meeting about [topic] with [number] people.

Alternative approaches:
- Async updates via Slack, docs, or paired check-ins
- Shared doc with comments
- Other options I haven’t considered

Which would achieve the goal with less coordination overhead?

List trade-offs for each option.

You could do the same audit of your current meeting too, of course.

Sometimes AI suggests something you hadn’t considered. Sometimes it confirms the meeting is necessary. Either way, you’ve made a conscious choice.

Remember - half of all people work overtime because of too many ineffective meetings. If you cut just 3 hours of meetings weekly across a 10-person team, that’s 30 hours of real work time you get back every week.

Action plan for this week

Pick one meeting you’re leading this week - ideally a recurring one.

Before: Use AI to create a tight agenda

During: Explain why you’re recording the meeting, then tell your meeting tool what to capture

After: Use AI to structure notes and draft the follow-up

Then measure:

• Did the meeting stay focused?

• Did we end with clear next steps?

• Who didn’t contribute - should they be there next time?

• Meeting length - did you finish early?

• Time saved on prep and follow-up

Let me know how you get on.

See you next week,

Ollie

Share

New research from BCG and Columbia Business School found that 80% of executives think their employees understand the company’s AI strategy. Only 29% of employees agree.

Leaders think 76% of employees are excited about AI. Employees say it’s closer to 31%.

Here’s the real problem - when people don’t understand how AI helps them personally, they don’t use it. And when they don’t use it, organisations stay stuck.

This series fixes that by teaching you to use AI on your actual work - one task at a time.

Last week, you mapped your work. This week, you’ll learn the foundational skill that makes everything else possible: how to brief AI properly.

Brief it like a colleague, not a search engine

Most people fail with AI not because the technology isn’t good enough, but because they treat it like a search engine.

You type “How can AI help me with meetings?” and get back a generic list of tips you already know.

The problem wasn’t the AI. The problem was the question.

If you asked a smart colleague “How can I improve meetings?” they’d say “Tell me more - what kind of meetings? What’s not working? What have you tried?”

AI needs the same context.

When you search, you type 2-3 keywords and scan results, which works fine for facts.

But AI can reason, synthesise, and create - if you give it enough to work with.

Most people type lazy prompts:

• “How can AI help with client meetings?”

• “Write me a strategy deck.”

• “Summarise this article.”

These give you generic output because you haven’t given AI any context about your specific situation.

Instead, brief AI like you’d brief a competent colleague. Give context. Explain constraints. Define what good looks like. Give context. Explain constraints. Define what good looks like.

Share

The five-part brief

Think carefully about how you’d brief someone on your team.

Just as your team-mate would ask clarification questions, you’d be more specific with you’re asking for. So, you wouldn’t just say “Sort my email.” You’d give them who you are and what you’re working on, what you’re trying to achieve, why the current approach isn’t working, what constraints you’re dealing with, and what a good result looks like.

That’s exactly how to brief AI:

1. Role - Who you are and your context

2. Goal - What you’re trying to achieve

3. Context - The current situation

4. Constraints - Deadlines, tools, format, tone

5. What Good Looks Like - The output you expect

Here’s the template:

I’m a [role] at a [company size/industry].

I’m working on [specific task] with [constraints, timelines, tools].

Current approach: [brief description]. This isn’t ideal because [why].

Help me improve by [specific improvement desired].

Return [structured format or bullet summary].

Then list the top 3 likely errors or missing assumptions I should verify.

That last line is critical. Always ask AI what you should double-check.

Now, if you have time, copy-paste this scaffold into ChatGPT and fill it out for your own work before reading on.

Here’s an actual example to make it more real:

“I’m a sales lead at a SaaS firm preparing Q4 pipeline updates. I need to shorten my weekly report from 3 pages to 1 without losing key metrics. Help me summarise the data clearly for execs making resource decisions.”

You’ve given AI your role, the problem, the constraint, and the audience. That’s what gets you useful output instead of generic advice.

Here’s another one - I mentioned this example from my own work last week (I’ve used Ramp founder and CEO Eric Glyman as an example in Claude).

What I get back: structured brief with company context, intelligent questions I wouldn’t have thought of, and specific things to verify.

Interested in seeing the output? You can see the first draft of the full exec summary here.

Share Ollie on Work

Time saved: 30-40 minutes. And the quality’s better than what I’d have written myself, because AI pulls patterns from hundreds of similar companies I don’t have time to research.

AI didn’t hasn’t replaced me - it gave me a better starting point.

The trust test

Of course, you can’t believe everything you read. And that’s certainly true of AI. So, before using AI on anything important, test it on something you already know well.

Ask it to summarise your own brand strategy, draft your performance review process, or explain a framework you use daily.

You’ll immediately see where it gets basics right but misses nuance, where it overstates certainty, and where it suggests things that sound good but wouldn’t work in your context.

You’re building your internal ‘AI radar’ - knowing when to trust output and when to sanity-check it.

Iterate like you would with a person

And, it’s unlikely you’re going to hit the jackpot first time around. Most people type one prompt, get a mediocre output, and then give up.

But you wouldn’t do that with a colleague. Use natural follow-ups:

• “What’s missing from this?”

• “What would you prioritise first?”

• “List the risks or trade-offs”

• “Give me a version that’s 40% shorter”

• “Reframe this for an executive audience”

• “What’s the contrarian view here?”

You’re having a conversation. Each response helps refine your thinking.

My first prompt gives me structure. Second tightens it. Third catches what I missed. Etc etc. You can do this amazingly quickly (particularly if you use voice mode).

Your plan for this week

Here’s a 30-minutes plan of action:

1. Take your workflow from Week 1

2. Pick the most time-consuming or frustrating step

3. Use the five-part brief to describe it to ChatGPT, Claude, CoPilot or whatever you have access to

4. Ask: “How could AI help me do this faster or better?”

5. Try ONE suggestion manually

6. Record:

   • Time spent before vs after

   • Output quality (1-5)

   • Confidence level (1-5)

Bonus: Try the same task tomorrow, refining your brief based on what worked.

Next week

You’ve learned to brief AI properly. Next week, we’ll tackle one of the biggest problems in modern work - too many bad meetings.

I’ll show you how AI helps you prepare better, run tighter meetings, and figure out who actually needs to be there.

Good luck!

Ollie

Share

“How do I build an AI strategy when I’m still figuring out what it means for my own work?”

I was asked this revealing question by a COO last week.

I suspect there are plenty of others I’ve spoken to over the past few months who feel the same, but haven’t felt able to be quite so honest.

After all, you’re supposed to have a vision. Set direction. Make confident decisions. But you’re also frantically trying to find the time to master ChatGPT prompts in the evening, wondering if you’re even using this stuff right.

So, here’s what I told him.

You can’t lead what you can’t do.

Not “do” as in write the code. “Do” as in - you’ve mapped one of your own workflows, automated a real task, measured the result, and now you understand what’s actually possible versus what’s still marketing bullsh*t.

Once you’ve done that - even once - everything changes. You stop sounding like you’re repeating what you heard on a webinar. You start making clearer decisions. Your team stops rolling their eyes in Slack.

The reality is we’re all learning two things at once:

1. How to use AI in your own work

2. How to lead your team through the same transition

Most people skip straight to number two and, sure enough, three months later nothing’s changed.

So, I figure it’s worth trying something different this time.

How this series works

Between now and Christmas, you’re going to build your own AI operating system - one workflow at a time, using your actual work.

One lesson per week. One action under an hour. Real examples and practical templates.

By January, you’ll have:

• Mapped and optimised a real workflow from your own job

• Built confidence in what works and what’s oversold

• Created a simple framework you can teach to your team

• Measured the time saved and quality change

Today, let’s start from the very beginning.

Share Ollie on Work

WEEK 1: What does workflow even mean?

The dream - especially if you’re designing an AI strategy for your organisation - is a world where AI automates the repetitive work while your team focuses on the uniquely human stuff - creativity, judgment, client relationships.

Sounds good. Achievable too.

But you can’t design that strategy from 30,000 feet. You need to understand what a workflow actually is, how it breaks down into tasks, and where AI genuinely helps versus where it’s still bullsh*t.

That’s what this week is about.

Not because you personally need to become an automation expert or understand every single task within every single job role. Because you can’t lead a transformation you don’t understand at the task level.

A workflow is just a repeatable sequence of steps that turns an input into an output.

The reason most AI strategies fail isn’t the technology. It’s that leaders skip this step - they’ve never mapped what their teams actually do, step by step. So the strategy stays theoretical.

Let’s get super practical.

For me, the obvious place to start optimising how I work was with preparing for important conversations - podcast interviews, client meetings, sales calls. The workflow looked like this:

Input: Person’s name, company, their recent content

Here are the steps:

1. Google the person and company

2. Scan their LinkedIn

3. Review the website

4. Read a few articles and/or videos

5. Open a doc and brainstorm questions

6. Review and refine for the conversation

7. Send an agenda (meetings) or preview doc (podcasts)

Output: Prep doc with context and talking points
Time: 45-60 minutes

Once I could see it laid out like this, something became clear. AI could handle the research gathering and give me starter questions. I still needed to shape the conversation for what I wanted to explore, and manage the relationship with the guest.

This doesn’t require automation - but it does contain multiple single steps that I can get done faster (and better) with AI.

• I use ChatGPT with browsing (or Perplexity Pro) to pull public information from their LinkedIn, company site, and any recent articles.

• I can then use this to create a detailed exec summary.

• I then prompt it to generate 20-25 possible questions before cutting it down to the 8-10 that matter.

I’ve done this 15+ times now. I timed it - consistently 45-60 minutes before, 15-20 after, with better starting material. Sometimes I leave it there and bank the extra time. Mostly, I reinvest it into going deeper into the individual or company. So I’m getting quicker and my work’s better.

The broader point here is that now when I talk about practical examples of how to use AI, I’m not guessing - I’m showing what I’ve learned first-hand.

Your mission this week: Map one of YOUR workflows

Pick something you do at least once a week that takes 30-60 minutes and has a clear output - this could be:

• Preparing for important conversations

• Reviewing work and giving feedback

• Weekly updates or reports

Use this template:

Workflow name: (e.g., “Preparing for client strategy calls”)

Input: (Client name, company, recent news or content, meeting goal)

Output: (One-page prep doc summarising client context, key talking points, and 5-10 tailored discussion questions)

Steps (aim for 5-10):
1.
2.
3.
etc.

(For each step, note: Could AI help with this? Or does this need my intervention?)

Current time:

Success metric: What proves this worked? (e.g., reduced prep time, better meeting outcomes, stronger client feedback)

Crucially, be specific - “Better meetings” isn’t a workflow. “Preparing context for client strategy calls” is.

And remember you’re not committing to fully automate anything yet. You’re creating clarity about what you do repeatedly, what takes time, and where AI might fit.

Once you can see the pattern - where the repeatable steps are and where you need human judgment - you can make better decisions on where AI will work for you and your team. Everything else gets easier from there..

And if it’s helpful, reply with:

1. Your workflow name

2. Your success metric

I’ll send you a quick checklist to validate your thinking and help you spot where AI might help.

Good luck!

Ollie

Share

I spent a decade running an agency and still work with a lot of media and advertising businesses. Q4 has a special meaning in this world - much as it does in other sectors like retail and hospitality. In short, everyone’s going to be bloody busy. It’s going to be stressful. And there’s not really much you can do about it.

But, as I’ve been discussing frequently over the past month, that’s no reason to push yourself to breaking point.

Now, while I’m quite happy talking about the importance of resilience, I’m very careful to frame it constructively. It’s not about grinding yourself into the ground. In fact, if you approach it in the right way, it can be the making of you and your team.

So, today, I thought I’d share some of the ideas I presented this past Tuesday in what was a first for me - three workshops on resilience, across four continents in one day. I hope you find it useful.

Collective resilience - stronger together

In 2018, twelve boys and their football coach were trapped nearly four kilometres inside a flooded cave in northern Thailand.

When rescuers finally reached them, the scale of the challenge was clear. The tunnels were narrow, pitch-black, and flooded - even elite Thai Navy SEALs couldn’t navigate them safely.

That’s when they called for two middle-aged hobbyist cave divers from England - Rick Stanton and John Volanthen. An engineer and an IT consultant who spent their weekends exploring caves for fun.

And the SEALs did something extraordinary - they handed the rescue over to them.

From that moment on, hundreds of people - Thai, British, Australian, American - came together. Engineers, medics, farmers, meteorologists. Each contributed one piece of the puzzle. Every day they regrouped, tested ideas, adapted, learned.

No one person played the hero - it was just a team of people working collectively, listening deeply, and staying focused on the mission.

That’s what real resilience looks like. In Bruce Daisley’s excellent book Fortitude, he quotes social psychologist Alex Haslam:

“Resilience is something that when you look at it in the world, it isn’t a manifestation of individuals as individuals. It’s a manifestation of groups and of individuals as group members. Resilience is something that only occurs in and to groups.”

In other words, it’s built through trust and open conversations. And collective rituals that build connection.

Here are a few I like:

At Pixar, they use the Braintrust - regular feedback sessions where directors present work-in-progress to a small group of peers.

The rule is simple: no hierarchy, no defensiveness. Anyone can challenge an idea or point out what isn’t working. The goal isn’t to criticise - it’s to make the story stronger. This kind of candid, but meaningful feedback builds trust because people know the conversation is about the work, not about them.

In Sweden, they have Fika - a cultural ritual where colleagues take a break together, usually with coffee and something sweet.

When I was running my agency, we created our own version. Mid-afternoon, every laptop closed, we’d sit together for 15 or 20 minutes - a cup of tea, a bit of cake, a proper chat. Not about KPIs or deadlines - about life, family, what we were cooking for dinner. It cut through that mid-day fatigue and reminded people they were humans first, colleagues second. And when people feel genuinely connected, they handle pressure better and support each other more naturally when things get tough.

In your team, it could be as simple as a no-meeting morning - just a few hours of collective breathing space to think and focus on getting your head down.

Because resilience isn’t just what you do when things go wrong. It’s what you design into the system so you don’t reach breaking point in the first place.

Personal resilience: the power of agency

Teams that work together handle pressure better. But there’s also a personal dimension - feeling like you’re in control of your own situation.

And that starts with personal agency.

Agency is the sense that your actions matter. That even when everything feels chaotic, you can still decide what to do next.

Few people embody that mindset better than Sara Blakely, founder of Spanx.

She started with an idea - tights that didn’t show a visible panty line under her clothes. “This is something other women need,” she thought. But the problem was, she was selling fax machines in Florida at the time. She had no experience, no funding, no connections.

But here’s what she did have - total belief that she could figure it out.

So despite spending two years hearing “no”, every rejection became a learning experience. Every failure gave her fresh insight. She didn’t wait for permission - she experimented her way forward.

• She taught herself patent law and filed her own patent.

• She drove repeatedly to North Carolina to find a factory that would make a prototype.

• She was rejected by every male-run mill until one called back because his daughters loved the idea.

• She cold-called department stores and convinced a buyer to give her ten minutes. During the meeting, she even took her own trousers off to demo the product.

• She eventually sent a sample to Oprah with a handwritten note. Oprah loved the product and featured it on air. The rest is history.

That’s agency. Not pretending you can control every outcome, but taking ownership of actions.

When you’re overwhelmed, agency is what pulls you out of paralysis. So here’s a simple framework to shift from reaction to action - the 3-2-1 rule.

When everything feels urgent, ask yourself three questions:

What has the biggest impact?

Preparing for an upsell conversation with a key client, not sending 20 generic outreach emails. Talking to five users about their real problem, not another internal prioritisation meeting. What’s the one thing that moves other things forward?

What’s urgent vs important?

That inbound email feels urgent - but finishing tomorrow’s client presentation is more important. The Slack ping about a minor issue feels urgent - but preparing for your strategy session matters more.

The rule: urgent and important → do it now. Urgent but low value → respond quickly, keep it light. Important but not urgent → block calendar time. Neither → drop it.

What can I drop or delegate?

That meeting where you’re one of twelve people - can someone else cover it? Formatting the internal presentation deck - can you hand it off? Dropping and delegating isn’t shirking. It’s protecting time for higher-value work.

Focus your effort where it counts.

Bouncebackability: the ultimate reset skill

The third layer is bouncebackability - the ability to reset quickly and go again.

Not my words. The words of Iain Dowie, former footballer and manager.

In what was one of the earliest internet memes, he managed to inadvertently articulate the secret to resilience (and coin a phrase that’s ended up in the Oxford English Dictionary) Because resilience isn’t about never falling - it’s about how fast you can recover.

Two great examples: Michael Jordan and Lin-Manuel Miranda.

After a loss, Jordan wouldn’t sulk or lash out. He’d go straight to the gym. His reset ritual was physical - action over rumination. He channelled frustration into focus.

For Miranda, the creator of Hamilton, it’s more emotional. He keeps a “Good Job File” - a folder full of messages, notes, and tweets from people who’ve been touched by his work. Whenever he faces criticism or creative fatigue, he reads through it to regain perspective.

Different rituals, same outcome - a deliberate reset. You don’t wait for energy to return - you create it.

Here are four types of Reset Rituals you can experiment with in your team:

Physical reset - stand up, stretch, take a short walk, some deep exhales.

Perspective reset - write down what’s in vs out of your control.

Story reset - share the lesson learned with a teammate.

Symbolic reset - change environment, close your laptop, swap tasks.

Pick one. Try it out. Tell the rest of your team how it goes.

Crucially, make having a Reset Ritual a normal thing to do! Because it works.

We talk a lot about performance at work, but not enough about recovery. And yet, the most successful teams and individuals - in sport, in business, in creative work - are the ones who design for both.

They connect. They take agency. They nurture bouncebackability.

Good luck for Q4.

Ollie

If your team’s heading into Q4 and feeling the pressure, I’m running “Resilience Through the Grind” workshops - practical, energising sessions designed to help teams protect focus, recover faster, and adapt smarter…

Get in touch

Share

It was the first time I’d been in a room full of media and advertising people since I left my agency five years ago. What struck me wasn’t the presentations or the panels, it was the conversations afterwards. Everyone was talking about AI. How to get it into products. How to build features audiences will pay for. And how to get the teams using it.

Important questions.

But as we dug a bit deeper, it was clear that not much is actually happening – internally at least. Even at the biggest household names – the tech companies leading the conversation on AI - adoption inside their own teams is still only happening around the fringes. When it happens at all.

In a way, this makes sense. Their priority has been - rightfully - their product. Commercial priorities. Getting AI to market.

But here’s the thing - building AI products doesn’t mean your teams are using AI to do their jobs. And if people don’t understand why they should use it or what it actually helps with, they won’t bother.

Unfortunately, most businesses skip straight past those questions to “which tools should we buy?”

Back to the world I used to live in

It reminded me of running my last business for a decade. Endless decisions about technology. Which platforms? Which vendors? Which integrations?

And inevitably, I experienced the same hype and overcomplexity that you no doubt suffer from.

If anything, these choices have gotten harder since then. Even more options. More pressure to “do AI.”

I’ve spent months thinking about this from an AI adoption perspective but, honestly, this problem exists everywhere.

Take media. Publishers are facing their own version of this challenge, but with even more urgency.

They’re watching traffic numbers climb while revenue stays flat or declines. Why? Because much of that ‘audience’ isn’t human anymore. It’s bots scraping content to train AI models. The metrics they’ve relied on for decades - page views, impressions, time on site - don’t mean what they used to.

So they need to redesign their monetisation models. Rethink their technology stacks. Make quick calls about billing platforms, identity systems, paywall technology, analytics - and now, bot management.

But the technology vendor landscape is typically chaotic. Everyone claims their platform is the future and they have the AI features everyone needs. Making it very difficult to separate truth from bullsh*t.

What I learned building Monetization Works

I’ve spent the last few months building something to help with exactly this problem. And here’s what surprised me.

When I started talking to media operators about their monetisation stacks, I expected them to want comprehensive vendor comparisons. More research. Deeper analysis.

That’s not what they wanted at all.

They wanted someone to cut through everything and say:

“For your situation, these three vendors matter. Here’s why. Here are the trade-offs. Now make the call.”

Nobody wants another 47-page comparison spreadsheet. They want someone who understands the landscape well enough to say “this matters, that doesn’t” for their particular context.

And that’s true whether you’re choosing monetisation tech or AI tools for your team.

The bottleneck isn’t information - it’s getting clear on what actually matters so you can decide and move on.

Telling your team “everyone needs to use AI” isn’t a strategy. They need to know why they’re using it, what tasks it actually helps with, and how it fits into their work. Without that, you get what I saw at IAB - companies building AI products while their own teams barely use AI at all.

Be the person to bring clarity - that’s what actually moves things forward.

Share

In particular, this from someone quoting their CEO below a Financial Times AI article:

“Even if workers are 20% more efficient, how do I know that freed up time will go into more work to increase output?”

Notable because I’ve been asked a variation of this numerous times by CEOs and other business leaders over the past few months. And while it’s a perfectly reasonable question – “how can we actually realise productivity gains from our team using AI?” - it also reveals something most leaders haven’t considered.

The important role of human nature in AI adoption.

Specifically, if people think sharing their AI productivity gains will just result in more work being dumped on them, they’ll keep those gains secret.

According to MIT’s recent research, while 90% of employees use AI regularly, only 40% of companies provide it. In other words, your staff already use AI. They just don’t use yours.

This is Shadow AI in full effect.

In this case, people - who often already feel overworked - have discovered AI can make them more efficient. But they’re not telling anyone. They’re learning to keep AI efficiency improvements to themselves. The business gets zero benefit.

This is a hot topic in the companies I’m working with – how can we transform Shadow AI into Shared AI?

Well, first understand the reasons, starting with the one we’ve already uncovered.

1. Incentives: there’s no upside to sharing efficiency gains

2. Policy: they’re not really sure what they can and can’t do

3. Perception: they’re under the misapprehension that using AI reflects an inability to their job effectively, rather than a welcome opportunity to innovate

4. Capability: they recognise that the tools they’re using personally are better than the solutions the company is mandating

Understand these points and you can start tackling Shadow AI.

First, the easy bit. Create a clear policy that everyone understands outlining what people can and can’t do. The things to watch out for. The behaviours you encourage.

Then, ideally, buy the tools your people are already using. If 90% of your team are on ChatGPT, get ChatGPT for Business. Stop forcing them to use inferior enterprise apps while they sneak around with the good stuff.

Again, this is a story I hear again and again.

An agency I was with the other day spent considerable time and money creating their own internal AI. Yes, it’s secure, but objectively – judged by the team’s usage – it’s not very good. So, everyone either just uses their own accounts anyway (meaning the data security concerns you’re trying to avoid are happening anyway - just without visibility or control). Or, worse, they don’t really use AI at all.

Next, make AI fluency part of your culture, not a secret. Create channels where people share what works. Run internal sessions on prompting techniques. Celebrate AI experiments - even if they don’t always go perfectly to plan.

And how about those productivity gains?

Well, here’s a simple idea: split the savings with them.

If someone saves 20% of their time through AI, redeploy 10% of their time back into additional work but let them keep the remaining 10% for learning, skill development, or more AI experimentation. Crucially, let them decide where to focus. You’ll get a dual benefit - more willingness to innovate and share in the first place, and more incentive to continue pushing to improve.

The problem underlying all of this is that most companies haven’t considered much beyond “we should do something with AI.” The result is that they’re struggling to see the promised benefits.

That CEO comment at the top was from an FT article on this very subject.

They analysed 374 S&P 500 companies that mentioned AI last year. 87% of earnings calls were positive. But their regulatory filings told a different story: vague productivity claims, pilots that deliver nothing measurable.

The truth is that the secret to success here is really no different from any major technological transformation. It’s the boring stuff that makes the difference.

• Clean data.

• Clear processes.

• People who understand what they’re trying to achieve.

• Incentives that reward efficiency rather than punish it.

So, consider the basics before investing more time and money into your AI strategy.

1. What specific problem or opportunity are you focused on?

2. How will you measure success?

3. Where does the saved time go?

You can keep wondering where the productivity gains went, or you can design incentives that make people want to share them.

See you next week.

Ollie

Share

50,000 of them, spread across three massive Diary of a CEO episodes about AI and work.

Not because I enjoy internet debates, but because this might be the best real-time mirror we have for how we collectively feel about the future of work.

One of the biggest podcasts in the world, with a hugely engaged audience representing a cross-section of society. Not just anonymous trolls or tech enthusiasts, but teachers, accountants, managers, lawyers.

Your colleagues. Your friends. Your family members.

All processing the same uncertainty you're navigating. All asking the same questions your teams are asking.

What I discovered isn't clear cut – how could it be with 50,000 comments from tens of thousands of people? But it does highlight interesting trends for how we should think about AI's impact on our work, both as individuals or leaders.

And it's proven an incredible insight into how people process change when everything feels like it's shifting.

1. The dominant feeling is complexity

The first thing that strikes you isn't panic or optimism. It's complexity.

"The biggest problem is human greed."
"Smart people racing toward our apocalypse."
"If AI changes everything, I'll still know how to sell."
"Not everyone can be an entrepreneur. That's survivorship bias."

People aren't neatly divided into believers and sceptics.

They're wrestling with multiple contradictory feelings simultaneously. Excited about possibility. Worried about fairness. Sceptical of hype. Practical about adaptation.

People are holding complexity because the situation is genuinely complex.

I see this in every client conversation about change too. People don't want - or need - simple answers to complicated questions.

2. Agency is the real dividing line

The clearest pattern wasn't about optimism versus pessimism. It was about control.

Comments focused on what might happen to people carried helplessness:

"What's the point of planning if everything changes overnight?"

"Feels like we're all waiting to be replaced."

On the other hand, plenty of people are approaching the challenge with a sense of agency.

"I've been using ChatGPT for months. Here's what works..."

"This gave me the push to start building instead of worrying."

In short, one clear dividing line is whether you feel like you can influence what's happening to your career? To your future?

This is critical for all of us, but particularly those leading team – anxiety fades when people feel they have options.

Share

3. The same personalities keep showing up

After reading thousands of comments, I kept spotting the same characters.

And it’s not hard to see how they map to the people you sit alongside in meetings every day:

The Alarmist sees danger everywhere. Raises risks others ignore.
"Smart people are racing toward something that might be our apocalypse."

The Stalwart is deeply invested in their craft. Resistant to throwing it all away.
"I spent 15 years building this career. Now I'm supposed to start over?"

The Sceptic cuts through hype, demands evidence.
"Easy to succeed when you already have capital and connections."

The Builder doesn't argue, just experiments.
"Built a workflow yesterday that saves our team hours."

The Seeker hungers for direction. Constantly asks what to do next.
"Should I learn to code or learn to manage AI?"

None of these responses are wrong. Each reflects a valid way of processing major change.

For leaders, the insight is simple: stop trying to convert people. Start working with them.

4. Scepticism dominates, but it's constructive

Amongst the fear and excitement, the voice of The Sceptic can be heard loud and clear.

"We can't get basic software to work reliably. Why trust this?"
"Another day, another 'everything will change' prediction."

These are people who've lived through multiple hype cycles. They've seen promises that didn't deliver.

But scepticism can be healthy if it leads to action.

In one episode's comments, nearly one in four responses were practical suggestions, not panic. People applying past experience to current challenges.

So take them as a challenge.

Sceptics often become the strongest advocates once convinced - they've already stress-tested the idea.

5. People want direction, not predictions

The most engaged comments weren't debates about AI capabilities. They were practical questions:

"What careers are actually safe?"

"Should I be learning to code or learning to manage AI?"

"How do I explain to my team that their jobs aren't disappearing, just changing?"

People aren't looking for more information about AI. They have plenty of that.

They're looking for frameworks to make decisions under uncertainty

.

What this means for leaders

Here's what I'd focus on if I were running your team:

• Stop selling certainty you don't have. People can handle uncertainty if you're honest about it.

• Create experiments, not strategies. Give people small ways to build experience rather than big transformation plans.

• Work with sceptics, not around them. They're doing your risk assessment for free. Put them in charge of testing assumptions.

• Address agency, not anxiety. Fear comes from helplessness. Give people things they can control.

• Focus on next steps, not final destinations. People need direction, not predictions about 2030.

If it feels like your team is resisting change, that’s probably not it. They're just processing it the same way humans always have – by asking what it means for them and what they can do about it.

The answers to those questions determine everything else.

And they're already being argued over in the comments.

Thanks for reading.

Ollie

Share