What is that magic sauce?

This is the question that I kept coming back to during the research for my forthcoming book AI-Q.

What I discovered was that it wasn’t technical skill, nor budget. It wasn’t even how early they’d started. Those getting measurable value from AI shared something else: a particular way of thinking, particular human abilities, and a particular set of day-to-day practices, that together shape everything about how they engage with AI.

These patterns underpin AI-Q.

They also show up in the results. Data from around 200 senior leaders who took the full AI-Q diagnostic found that high AI-Q leaders were three times as likely to report a quantified business outcome from AI in the last twelve months. That held after controlling for role, organization size and how mature their organization was on AI.

Defining AI-Q

First let’s take a step back. This is how I define AI-Q:

It is built across three interdependent dimensions: Mindset, Abilities, and Practices (MAP):

1. Mindsets reflect how you think about AI and your role as a leader in relation to it.

2. Abilities are the human capabilities that make your leadership effective in an AI-enabled world. Across my research, five consistently emerged as differentiators: critical thinking, empathy, strategic thinking, creativity, and learning agility.

3. Practices are the concrete actions through which mindsets and abilities show up in your approach to AI. They are the ways of working that embed AI-Q into everyday leadership.

If mindset shapes how you think about AI, and abilities shape how you work with it, practices shape what you do.

Why Mindset is the starting point for AI-Q

Mindset shapes how you instinctively orient toward AI before you do anything else.

Every leader I interviewed who was getting value from AI described a mindset shift first, before any change in skills or tools.

Abilities only deliver value if you have the mindset to recognize where they apply.

Three mindsets in particular distinguished leaders who translated AI capability into value.

1. A growth mindset of treating AI readiness as a capability to be developed rather than innate talent.

2. A systems mindset of understanding how changes in one area ripple through the organization.

3. A foresight mindset of anticipating how AI reshapes industries and roles, and the external operating environment.

   Together they form the foundation of individual AI-Q.

What do these mindsets look like in practice?

1. Growth: “this is not my area, yet”

Growth mindset is the belief that capabilities can be developed through effort, including the capability to work with an unfamiliar and fast-moving technology.

Take Panos. He is a Risk Director at a global investment bank, where he frequently reports on the bank’s current risk exposure from different trades and positions held in financial markets. We met at the start of our careers when I was an economist at the Association of British Insurers and he was a risk analyst at Barclays.

He has spent twenty years building deep expertise in risk reporting.

He told me that he had initially assumed AI was for the data scientists in his team. Then he decided to teach himself.

I asked him what motivated the change of heart.

“I wanted to focus on higher value-add tasks: examining data, critiquing it and giving strategic advice rather than preparing repetitive reports.”

Within three months, the eight hours per month that he spent extracting data and creating PowerPoint presentations had been redirected to those strategic activities.

2.Systems mindset: “see the whole to redesign it”

A systems mindset is seeing how the individual parts connect to each other and to a larger whole.

This is becoming essential as AI agents become more prevalent at work. It is tempting to think of AI agents as hiring more staff, only faster and cheaper. But AI does not simply add capacity. It changes the structure of the system itself.

You are no longer managing a team of people. You are managing a team of people who are each managing agents. What looks like a team of five can quickly become a much more complex system involving dozens of interacting components.

Mike Jones, the CTO of loveholidays, describes systems thinking as the ability to step back from individual tasks and see the whole end-to-end process that produces an outcome. He is a big proponent of drawing a system to understand it.

I asked him why it matters so much for AI.

“Once you can see the system, you can redesign it.”

Now think about where most AI pilots happen. In one department, on one process, with one team.

AI adoption is about driving outcomes across the business, not in a particular function. High AI-Q leaders apply a systems mindset to create coordination and cooperation across departments, so they can drive change across their organization.

3.Foresight mindset: “avoid the tyranny of the urgent”

AI is making the future more uncertain than ever. A foresight mindset helps you anticipate change systematically. It treats the future as part of the job of leadership today, not an offsite luxury.

Leaders know, in theory, that they should look ahead. In practice, they spend most of their time in firefighting mode.

As a junior consultant at Accenture, I remember one of my managers, Paul Nunes, talking about “the tyranny of the urgent” in business. Paul has spent decades researching what sets leading companies apart. In his book Big Bang Disruption, he shows how for many leaders, by the time disruption feels urgent, the window to respond has already narrowed.

AI intensifies this. Competitive dynamics can shift quickly, and from unexpected directions.

I saw what this looks like at a roundtable with heads of research across consulting, legal and finance firms. One of them put it like this:

“AI can spit out data and answers, but what we truly need is people that can apply data to our specific firm’s context. That ability comes with experience. If we replace people with AI, where will that experience come from?”

She was not making a prediction and she did not have an answer. But it really stood out for me - because the fact that she was asking the question is the point!

High AI-Q leaders exercise foresight by creating space to imagine and anticipate what the future of their business will look like. They ask whether their strategy still holds given recent AI advances, and they learn from organizations at the cutting edge of adoption.

Where are you?

When I explain the three mindsets to clients, the first thing they ask is which one matters most.

My answer is to picture them as scaffolding. Each one provides the foundation that makes the next one possible, and reinforces the others in return. So it is worth asking all three.

Growth: how do I relate to AI as a learner?

Systems: how does AI reshape the system I operate in today?

Foresight: how might that system evolve, and what could change its direction?

A perfect score here is rare. In my conversations and the early diagnostic data, even leaders in the highest AI-Q band rarely scored equally across all three.

So which of the three is your weakest right now? That is usually where I would start. Keen to hear your thoughts in the comments.

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In the last two essays we have been looking at how to boost an individual leader’s AI-Q, but ROI requires building organizational AI-Q too - this is the theme of next week’s essay.

My book, AI-Q: The New Leadership Imperative for the Human+AI Era, is published by Practical Inspiration Publishing on 12 January 2027.

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Recently on Athenian Intelligence:

I was on stage with Chloe Smith from the CIPD (far left), Cien Solon from Launch Lemonade, Andy Ayim (who was moderating and is in the middle) and James Relph from HSBC (to my right) - that is all of us grinning in the photo above. We had spent most of the session on regulation, on leadership mindsets, on which skills matter next.

Then, near the end, an audience member asked us this question:

“As everyone gets access to AI, where will my competitive advantage come from?”

It was the best question of the night, and the panel had about ninety seconds to answer it. It is also the question at the centre of my forthcoming book AI-Q, so I want to give it the answer it deserves here.

Let’s start with the economics.

The fundamentals

Economic fundamentals tell us that when an input becomes universally available at near-zero cost, it stops being a source of advantage.

For example, electricity was a genuine differentiator for about thirty years and then it was just a utility bill.

Something similar will happen with AI.

Those that understand AI currently have a competitive advantage. I was talking to a Director at a consultancy that runs executive leadership training for senior executives in role transitions. The first thing they are encouraged to do is to get a formal certification in AI. This helps to get them up to speed with what the tech can do. Courses are proliferating. One of the questions I get asked when I deliver board briefing presentations is which one to pick, and some tell me they use AI to help them choose their AI course. Indeed, you can actually get Claude to design one for you, though in a corporate environment people value the structure and the credential that comes with a formal qualification.

This is not just a trend for corporate execs.

More and more of us are investing in our AI literacy skills, building our understanding of the technology, the various tools and where and when to use them. Yet soon that knowledge will be commonplace. As everyone becomes more AI literate, having an AI certification will become table stakes. In fact, I would argue it already is out there. It is just a matter of time until it diffuses across the population.

That brings us to a different question.

If AI knowledge becomes commonplace what helps you stand out as a leader?

If AI tools no longer give you an advantage, then what does?

What the leaders I interviewed for AI-Q told me

These are the questions that drove me to put forward a proposal for a book, taking part in a proposal challenge with Practical Inspiration Publishing, a publishing company that specialises in non-fiction books.

This was nearly one year ago as the proposal run in September 2025. And to my surprise I won the challenge! I have been asked to write a separate article for aspiring authors so look out for that soon.

What followed was a year of interviews with senior leaders about how AI is reshaping their work.

One question I asked every single interviewee, whether they were a Chief Finance Officer or Chief HR Officer, was “what will be the most important ability or skill in the human + AI era?”

Not one single leader said AI. Not one of them said AI literacy.

All of these leaders were from organisations spending real money on this, and none of them named the technology as the differentiating capability.

Is it because it doesn’t matter? Of course it does.

But a bit like financial markets, they have already priced that in to their decision making. Their expectations have adjusted to include AI literacy, and assume that it is the new baseline. What they’re now looking for is what you combine it with.

Five abilities came up again and again:

Critical thinking. Empathy. Strategic thinking. Creativity. Learning agility.

At this point you might be thinking, ok, but these abilities have always been “good to have”. This is not a new list. True. But the issue that emerges as AI becomes commonplace in our lives is further ahead. We are increasingly outsourcing a lot of these things to AI tools. I wrote in an earlier essay “The case for treating childhood as economic infrastructure” how eliminating friction means eliminating the mechanisms through which we build these abilities. As a result, supply is falling. Yet, demand, as my interviewees made unambiguously clear, is rising.

The five, and what is happening to each

1. Critical thinking

As AI generates outputs at speed and scale, your most important contribution is not using it. It is interrogating what it produces: the question you asked, the assumptions and biases baked into the answer, and whether the reasoning holds up.

But research has found that the instinct runs the other way: the more we trust the output, the less we examine it.

It can also be hard to have the discipline of slowing down to apply critical thinking when AI seems to speed things up.

High AI-Q leaders see critical thinking as their biggest differentiator and create verification mechanisms that protect their judgement.

2. Empathy

Emotional triggers to change can hold back AI adoption in companies, regardless of leadership rhetoric about productivity benefits.

Yet much of the economic analysis of AI uses a task-based lens, even though relationships, informal knowledge, and context are decisive in driving change.

Without empathy the risk is that we miss those human dynamics entirely and mis-direct effort and budgets. For example, you might invest in technical training when all your people need is reassurance.

Empathy is also the ability most quietly exposed to substitution, because AI is now genuinely good at producing empathetic-sounding text. That is not the same as understanding how the individuals in your organisation feel about what you are asking them to do.

High AI-Q leaders realize that their approach to AI adoption needs to go beyond an analytical lens, to also include empathy towards their employees, customers, peers and stakeholders.

3. Strategic thinking

AI radically increases the amount of information available to leaders, while doing nothing to resolve the underlying challenge strategy has always faced: making choices under uncertainty.

Worse, the “fear of missing out” on the AI race can drive reactive investment rather than strategic choices. Every pound spent on an AI tool carries the opportunity cost of not spending it where the business has a greater need.

When new competitors can enter your market with a fraction of the headcount and infrastructure you carry, or when AI compresses a three-day task into three hours and breaks your pricing model, the strategic question shifts.

High AI-Q leaders keep returning to the question that opened this essay: “if every firm has access to the same AI, where does our advantage actually come from?”

4. Creativity

Generative AI can produce ideas at scale. But it lacks contextual judgement, which is what you can bring. A two stage study by Stanford researchers compared the creativity of Large Language Models (LLMs) and humans on new research ideas.

Though LLMs outperformed humans in terms of raw ideation output, i.e. number of novel ideas put forward, when those ideas were assessed they were not feasible. The AI ideas looked better on paper and got worse when someone tried to build them. For example, some ideas were too expensive to execute, something that humans inferred because they had lived experience to provide that context.

In the human+AI era, what makes you indispensable as a leader is not raw ideation; it is knowing what your organization can realistically do next.

Leaders with high AI-Q use creativity to push their AI initiatives past efficiency. They go beyond the question “what can we automate?” to ask “what could we now do that we couldn’t before?”

5. Learning agility

Learning agility is the capacity to continuously acquire new skills and adapt to rapid change. This involves learning, unlearning, and creating effective feedback loops that embed learning.

It is the meta-skill that enables all the others that form part of AI-Q. You can’t enhance your strategic thinking or critical thinking without learning agility. You can’t adapt your leadership approach or embrace new ways of working without it. As Dave Light, one of my co-directors at Beacon Thought Leadership, aptly put it: “it’s the skill to end all skills.” Yet it can also be the hardest ability for some experienced leaders to embrace.

Back to our initial question

So, to the person who asked it in that room in London.

Your competitive advantage will not come from access to AI, and it will not come from a certificate proving you understand it. Both of those are diffusing across the market as we speak.

It will come from the abilities everyone agrees are essential and almost nobody is actively building, because the technology that made them essential is also making them harder to acquire. To discover how high AI-Q leaders are boosting their own abilities for the human+AI era, look out for my forthcoming book.

In the next essay we will explore the mindsets of high AI-Q leaders and how to boost your own.

My book, AI-Q: The New Leadership Imperative for the Human+AI Era, is published by Practical Inspiration Publishing on 12 January 2027.

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Recently on Athenian Intelligence:

“Nine in ten businesses hit by rising uncertainty.”

“Economic uncertainty hits confidence.”

And if I had a pound for every mention of “unprecedented uncertainty” in thought leadership reports….!

“Uncertainty” is the villain of the story. It makes it harder than ever for business executives to make long-term decisions, or in fact any decision, resulting in a ‘wait and see’ approach.

The World Economic Forum has stated that “in today’s global environment, uncertainty is no longer a periodic headwind, but a defining feature of the business landscape.”

It is stated as something new, but in fact this has always been the case.

This week I argue that uncertainty is good for business, or at least for the businesses prepared to embrace it, and it always has been. Therefore, the claims from AI vendors that they can take the uncertainty out of the difficult decisions that business leaders need to make are fundamentally flawed.

To do this, I need to first introduce you to another economist.

The difference between risk and uncertainty

In 1921 an American economist called Frank Knight published a book with a title that would probably not make anyone’s ‘summer reads’ list!

The title is Risk, Uncertainty, and Profit. Having tried to read the book end to end I can confirm it is as dry as it sounds.

But what really stands out is the distinction he draws between risk and uncertainty and where profit comes from.

We tend to use the words “risk” and “uncertainty” as if they mean the same thing. Knight’s whole point is that they do not.

Risk is something you can measure. You do not know the outcome, but you know the odds. A roll of the dice. The chance a part fails on the production line. The odds a customer churns. You cannot say what happens next, but you can put a number on it, price it, and, crucially, insure it.

Uncertainty is the thing you cannot measure. This is the product category that does not exist yet. The competitor not yet founded. The market nobody has tested. You cannot price it, because there is nothing to price it against.

One way to think about it is whether you can insure it. You can buy insurance against your office burning down, because fire is a risk and somewhere there is an actuary with the tables to prove it. You cannot buy insurance against whether the thing you are about to bet the company on will still make sense in five years. That is uncertainty.

What has this got to do with AI?

The reason this matters in the context of your AI investments is threefold.

1. The first is that profit is the reward that accrues to business leaders and entrepreneurs for bearing uncertainty and exercising judgement in an uncertain business environment.

2. The second is that AI is eroding the ability of business leaders to exercise judgement in two ways: a false sense of security and a slow atrophy of the judgement ability itself through delegation.

3. The third, and most important, is that business leaders will soon be confronted with the question of where their competitive advantage will come from when AI becomes commonplace.

Let’s look at each in turn.

1/Profit is the reward for judgement under uncertainty

The decisions that matter for business success sit in that second bucket of uncertainty.

As Knight writes:

The best example of uncertainty is in connection with the exercise of judgment or the formation of those opinions as to the future course of events, which opinions (and not scientific knowledge) actually guide most of our conduct.

And the market rewards a leader’s or entrepreneur’s ability to form good judgements about the future with profit.

Profit arises out of the inherent, absolute unpredictability of things, out of the sheer brute fact that the results of human activity cannot be anticipated….even a probability calculation in regard to them is impossible and meaningless. The receipt of profit in a particular case may be argued to be the result of superior judgment.

2/AI gives false comfort that uncertainty can be eliminated

Machine learning is an engine for measurable randomness. It learns the distribution of the past and projects it forward, and where the future resembles the data it does so with a facility no human can match. In fraud detection, demand forecasting, logistics and the rest of the vast territory of repeatable problems, this is a genuine and growing success, and leaders should take all of it.

Genuine uncertainty, however, offers nothing to learn from. That is precisely what makes it uncertain. The unprecedented event leaves no training data. Asked to forecast it regardless, a model does not decline. It returns a confident figure assembled from the nearest available history. The hazard is that the machine offers a false sense of safety.

Let me take you into a meeting from my Accenture days.

I am working on a trends project where we are quantifying different types of trends, from economic to geopolitical. The aim is to project them into the future so we can estimate the impact on the client’s business.

When my team and I present the findings we present the data for the established trends and we also present some for which there is no data yet, but we believe we should have on our radar. Voice based agents is one of the trends, based on our reading of innovations in the space.

The question we get is: “what about the data for these ones?” I explain there is no data because by definition they are so nascent that there has not been enough time yet to build a robust time series - and this makes it difficult to extrapolate into the future.

“We cannot use it if there is no data behind it” is the response. Data provides certainty and without it the insight was not considered robust enough to inform decision making.

The choice was made: the comfort of a number won over the value of judgement.

Knight, 100 years ago, writing about the booming market for information that uncertainty creates, put it thus: “From this point of view it is not material whether the ‘information’ is false or true, or whether it is merely hypnotic suggestion.”

Today AI vendors are making similarly hypnotic suggestions, promising business executives release from the need to make decisions under uncertainty - the very thing that is demanded of leadership.

3/When everyone has AI, profit will come from the very ability being undermined: judgement under uncertainty

Last week I spoke on a panel hosted at Canva with the title “AI, Humans and the Future of Financial Services”. One of the questions that emerged is one we need to spend more time discussing: “where does advantage come from when everyone has the same tools?”

Knight’s answer was that it cannot come from risk. Anything measurable can in principle be calculated by everyone, hedged, insured and competed away, until the return on it falls towards nothing. Real profit, the surplus that builds a firm beyond sustenance, is the reward for bearing uncertainty: for exercising judgement where no number exists, and being proved right by an outcome that no one could have demonstrated or calculated in advance.

As AI becomes more sophisticated, everything in the measurable bucket is becoming a commodity. If your AI model can compute it, so can your competitor's; the forecast, the score, the optimisation formula, all converge on the same answer for everyone, and the advantage that once lived there is competed away, exactly as Knight would have predicted.

What remains scarce, and therefore valuable, is judgement under genuine uncertainty: the bet on a future that has not happened, the call that cannot be delegated to a data point. If AI commoditises the measurable, the returns migrate to the unmeasurable. So, in time, will the power. This is also a distributional matter, for the people and firms able to exercise such judgement will capture a rising share of the rewards, and the distance between them and everyone else will grow. This emerging inequality is one of the aspects that I explore in my forthcoming book AI-Q and I can on leaders to address. One for a future essay here perhaps.

Three ways business leaders can respond

None of this is an argument against AI. It is an argument for being deliberate about where your judgement stays. One of the things I discovered during my research was that high AI-Q leaders are not the biggest users of AI tools (looking at you token maxers!), but they are definitely the most intentional.

With this in mind, here are three places to start.

1.Know which bucket you are in.

Distinguish the two buckets before acting on any machine output. Where the question is one of measurable risk, trust that the model can offer useful information and move quickly. Where it is one of genuine uncertainty, treat the output as the beginning of a judgement and never as a replacement for one. Mistaking uncertainty for risk can lead to a false sense of safety.

2. Build to survive being wrong rather than to be right.

Under genuine uncertainty, optimising for the forecast is optimising for a guess. The sounder objective to consider is robustness: more than one route to success, options held open, exposure that can absorb a surprise. Resilience beats prediction whenever the future is uncertain.

3.Prepare people now.

As value and power concentrate around those who can act well without a number to hide behind, the central task of leadership is to widen that capacity. The gap between leaders and their employees that can still exercise judgement under uncertainty and those that have handed it over to an AI model, is the gap that will decide who prospers. Buying more technology doesn’t close the gap.

My findings show that it closes by bolstering your foresight mindset, your strategic thinking capabilities and designing your own red lines around the decisions you will never hand to AI.

Uncertainty is good for business

Let’s do a quick thought experiment.

Imagine for a moment that AI delivers on the promise. Every outcome measurable, every business decision reducible to a calculation. It sounds like paradise for business. But wait. If everything could be calculated, everything would be, by everyone. Every opportunity would be priced, hedged and competed away to nothing, and margins would collapse until business was a utility. Profit exists because the future is unknowable. Remove the uncertainty and you remove the opportunity for profit. That is the sense in which uncertainty is good for business. Or as I said earlier on, good for those able to embrace it.

We have been sold a story that the whole point of AI is to take the uncertainty out of business. I think that story has it backwards. AI is going to commoditise the risks, the measurable, and there is real value and benefit to that. It can mean a better understanding of risk and the opportunity to insure against these risks, offering business a way to take on new ventures.

Something similar happened once before. In the 1700s, merchants gathered at Edward Lloyd's coffee house in London to share shipping news and sell each other slices of the risk of a voyage, writing their names under each share which is where the word underwriting comes from. This enabled them to pool information and pool exposure, and brought about a maritime explosion. Once ship owners had split out the risk, they were able to take on more uncertainty: new routes, new cargoes, new markets.

But it also means the measurable is no longer where a business plays to win.

What is left, the part AI cannot yet help us with, is the oldest and most human part of the job of leadership: the judgement to act when no number can tell you what to do. The necessity of acting upon opinion rather than knowledge.

Frank Knight understood this in 1921. How are you protecting that judgement?

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Then he introduces me to Joe, who used to organise events. I think to myself “how random” that I need to organise my book launch event and here am I talking to someone that did this for a living.

We take our seats for Matt’s interview about the book. In the spirit of the evening, I jot down the random words and ideas that stand out to me as he speaks. When I read them back the next morning, I realise they are not three random things after all. They have something in common. And the idea of randomness, it turns out, can teach us a great deal about how we approach AI adoption.

That is what I explore today.

The three random things:

1. Lottery

2. Apophenia

3. Luck Surface Area

1.Lottery

“Random selection leads to less biased outcomes than human judgement”, said Matt.

This is the kind of statement you don’t want to believe is true.

But think of it in terms of recruitment. You interview 10 different candidates. You quickly rule out the handful that are not a good fit. And you might have 2 or 3 that you think are particularly strong. How do you choose between them? Many companies add further interview rounds. It is not uncommon nowadays to go through 7 or 8 interview rounds for mid to senior positions.

Another option would be to use a lottery and choose at random. Dr Tomas Chamorro-Premuzic has previously argued that this would be worth experimenting with, though I have not come across any companies where this is publicly disclosed as part of their approach.

However, when researching this I came across other fields where this has been adopted, such as in grant applications for example. Volkswagen Foundation has experimented with grant lotteries, usually as a “lottery among the qualified,” where applications are screened to a threshold and then a winner is drawn at random from the shortlist.

The reasoning is that our human need to create consensus can actually hinder choosing really “out of the box” solutions. Having started this approach in 2017 VW has been able to assess the outcomes of this and they found that randomized selection avoids conflicts of interest and unconscious bias and has led to more diversity in grantees.

Humans construct narratives to justify picking one near-identical candidate over another, and that’s exactly where biases creep in. With a lottery or randomisation you admit “we can’t tell these apart, so we won’t pretend to.”

Enter AI.

The problem is not that AI is biased, at the end of the day us humans are too! The problem is that AI's bias appears as objectivity. It comes with a number and it is a black box. A committee or a leadership team can be argued with; someone can say "hang on, are we just picking the one who looks like us?" A score feels like a finding. This can also remove the friction that sometimes catches bias in the process. A bias that was previously visible and contestable becomes hidden and very hard to challenge.

How many of you have been in a decision making situation where someone says “the data says” or “the AI says” with an aura of authority and finality - as if that settles the question. In one meeting I heard a chief strategy officer say that they had asked ChatGPT of all things if their strategy was the right one. The catch for leaders is that accountability still rests with you. Yet can directors and executives truly understand and be held responsible for outcomes influenced by complex AI tools they cannot fully see inside?

2.Apophenia

This is our brain’s natural tendency to identify patterns and to spot connections even between seemingly unrelated things. This has its roots in psychology, though the word is from the Ancient Greek ἀποφαίνειν, “to appear”.

Our brains have evolved to be optimised pattern identifiers. We see patterns where there are none. We spot trends in what is simply randomness. I found this intriguing because one of the arguments around the use and benefits of AI is that they can be very good at identifying patterns. For example, the argument goes that it can be very hard for the human brain to review large amounts of customer data and spot a new trend in buyer behaviour, and AI can plug or complement that gap in human capability.

This means we have built a comfortable assumption into every AI business case: that AI finds patterns, and finding patterns means finding “insight”.

It does not. AI is built to find a pattern whether or not one is really there. Give it a blip in last year’s numbers and it will treat that blip as a rule and project it forward. If you ask it something it has no answer to and it will not pause or hedge. It will give you a confident, fluent, completely wrong answer in the same tone it uses when it is right.

That is the part that worries forward-thinking leadership teams. Not that the machine is sometimes wrong. We are also sometimes wrong. It is that we both try and look for patterns even where there are none and therefore the notion that investment in AI can help you address that human “shortcoming” for better business decisions, is flawed.

It is that the machine is wrong with confidence, without a warning sign. When you work next to a junior analyst you learn over time when they are unsure. They they hesitate, they flag the weak assumption. AI has none of that and it gives you the noise bundled up as signal. So “more data plus more AI equals better decisions” becomes “more confident noise, faster” instead and we cannot see the difference because our brain’s normal critiquing mechanisms are bypassed by the confidence of the AI output.

An even bigger risk comes when we read intelligence into the output. Because it sounds considered, we assume it has considered. We assume there is inherent understanding or judgement, even when that intent is simply not there.

It is perhaps unsurprising then when I was interviewing business leaders for my book “AI-Q” they mentioned that they are increasingly placing huge emphasis on critical thinking skills and the ability to have depth and to ask the right questions.

3.Luck Surface Area

This idea, from Jason Roberts, is that even when something looks like pure luck and feels beyond your control, two things are still in your hands: the work you Do, and the degree to which you Tell others about it. Luck is the product of the two.

L = D x T.

If you Do a great deal of work but never talk about it then serendipity can't find you; if you talk endlessly but produce nothing and there's nothing for luck to attach to.

The promise of AI for business leaders is that it can let you produce more and reach further at almost no cost. More content, more proposals, more outbound, more of everything, faster. In theory that should be pure upside. More chances for something good to land. But the problem with AI tools accessible to everyone is that it is not just happening to one business leader or one company, it is happening to everyone. The volume of everything goes up and it becomes harder to get noticed.

Mark Read, former CEO of WPP a marketing agency put it like this:

“The ability to produce stuff is obviously going to increase, but the ability to cut through is going to be harder.”

This means that the advantage you thought you were buying with AI, the cheaper production, the wider reach, will evaporate over time as AI tools become available to all.

Where will your competitive advantage come from then?

A couple of weeks ago I interviewed Francis Hintermann, Global Managing Director of Accenture Research, at the KIMRA conference, with research and insights professionals across banking and law firms. When asked this question he said it will come from us. Each one of us and the individuality and the unique combination of thinking and human skills we bring to the mix.

Therefore what is scarce will change. It is no longer production, and it is no longer reach, because those are now available to everyone. The scarce thing will be the ability to be the signal people actually choose to listen to in a sea of sameness. That is what does not multiply at the touch of a button, and where advantage relocates to.

What links a lottery, an ancient Greek word, and a formula for luck?

The three random things we just explored are not that random after all. Each one exposes a place where a company’s AI strategy may be more fragile than initial appearances suggest.

You buy AI to plug the gaps in human judgement. To take the uncertainty out of the decisions that keep you up at night. It is meant to reduce the uncertainty surrounding human-based decisions in business.

Except….not so fast:

• Stuck choosing between two near-identical candidates, or two equally good investments for the budget? Use AI to strip out the bias and make the call. Except it encodes the bias instead, and wraps it into a data point that no one feels qualified to question.

• Worried we are seeing patterns that are not really there? Use AI to surface the real ones. Except it sees phantom patterns too, and reports them with more confidence than any human would dare.

• Hungry to manufacture more luck, more reach for your products and brand? Use AI to produce more and get in front of more people. Except everyone else does exactly the same, the noise rises for all of us, and being heard gets harder, not easier.

In every case we reach for AI to do the same thing: remove the uncertainty. And in every case the uncertainty does not actually leave. It gets hidden, automated, and relabelled as objectivity, which is far more dangerous than leaving it in plain sight.

Perhaps the antidote is not minimising uncertainty but to accept it - and even to use it.

I leave you with the thought that uncertainty is good for business, an idea we revisit in next week’s essay.

Intrigued by randomness? Then you may want to explore Matt and Nick’s book:

“In Random, Matt Ballantine and Nick Drage explore how randomness and uncertainty benefit our lives and suggest strategies for making the best use of randomness and managing those situations where uncertainty is unavoidable.”

Next week’s essay: Knightian uncertainty. Frank Knight, the economist and author of “Risk, Uncertainty, and Profit” and on the value of uncertainty for business.

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Brainmaxxing and AI

In the golden age of "brainmaxxing" we live in a hyper-optimized, bio-hacked, metric-driven existence where every calorie, every minute of sleep, and every ounce of cognitive output is tracked, measured, and judged.

The availability of powerful, frictionless AI tools means the friction of starting a task has vanished. But because we can do everything faster, the expectation is that we should do more.

How many of you relate to this pressure:

• To spin up one more prompt before closing the laptop?

• To squeeze in one more deck to prepare for the morning?

• To deploy one more autonomous agent to grind through data overnight while you sleep?

We were promised more free time yet we feel we have less than ever.

In their Harvard Business Review “AI Doesn’t Reduce Work—It Intensifies It” the authors Aruna Ranganathan and Xingqi Maggie Ye discovered that AI tools didn’t reduce work, they consistently intensified it. In their eight-month study of how generative AI changed work habits at a US based technology company with about 200 employees, they found that:

“…Employees worked at a faster pace, took on a broader scope of tasks, and extended work into more hours of the day, often without being asked to do so”

This is by no means just an AI problem. As the Steven Bartlett story shows, social media influencers are incentivized to peddle the latest tracker or wearable, earning them affiliate fees, and leaving the rest of us feeling like lemons, desperately squeezing out the last available drop of daily productivity just to keep up.

The KIMRA confession: the luxury of low-value work

Last week I was speaking at the KIMRA conference, which brings together professionals in the knowledge management, insights, research and thought leadership function. The room was packed with brilliant minds from elite law firms, global management consultancies, investment banks, and data vendors. These are people whose entire career value is predicated on high-level cognitive processing.

In a fireside chat one of the participants, a leader at a professional services firm, said “give me some low-value work!”

She got woops and cheers!

She went to explain how she felt that her brain was so maxed out, essentially fried from working at full capacity for months on end. So much so that she craved the dull, repetitive work that is now being automated by most organizations.

How can this make sense in the context of a promised productivity boom?

When the dominant tech narrative is about being freed from the drudgery of dull work and promising us liberation, why are we craving it?

And why are knowledge workers suddenly begging to have it back?

The healthcare parallel: destabilising the workday

An interesting parallel comes from healthcare. Recently, while researching my forthcoming book, I had a fascinating discussion with Sue Lacey Bryant, the former Chief Knowledge Officer for the NHS in England.

What I observed from our conversation, and what it helped me to realize about the broader workforce, is that leaders introducing AI often completely fail to consider the psychological ecosystem of the work being changed. We look at efficiency gains on a spreadsheet, but we don’t look at how automation disrupts the invisible balance of a human being’s day.

For example, if you only give a doctor the tough work, and give the other parts to a nurse practitioner, then what you end up with is a doctor whose day is full of acute illness, and dire circumstances. The balance is entirely destroyed. There are no longer any “easy” moments in the shift to catch one’s breath, as it’s one problem after another.

Earlier this week the American Medical Association at their Annual Meeting in Chicago this week stated in one of their adopted resolutions:

“The relentless focus on productivity metrics has been associated with physician burnout, emotional exhaustion, moral distress and workforce attrition, all of which may threaten patient access to timely, high-quality care.”

Healthcare is a fascinating example because the stakes are literally life or death. I recall when I was working at Accenture some years ago there was a time when new experienced recruits included doctors. Their ability to make decisions in high-pressure, chaotic environments made them phenomenal consultants. But the corporate world should view this as a warning, not an example to follow.

The Slow AI movement

Is the accelerated pace of change that so many people feel leading to a counter-movement towards slow AI?

Just as the frantic pace of fast-food culture birthed the "Slow Food" movement, we are beginning to see the early indicators of a "Slow AI" movement: a deliberate pushback against the expectation of instantaneous, infinite output. We are already seeing the emergence of dedicated spaces exploring this shift, most notably Professor Sam Illingworth’s popular Slow AI Substack, which advocates for critical AI literacy and cautions against blindly outsourcing human judgment to machines in the name of speed.

There is a more useful way for leaders to think about this than simply going slow. Research for my book showed that the leaders with the highest AI-Q deliberately build in positive friction: small, intentional pauses that let them stop and interrogate an output rather than wave it through or delegate at speed to their teams. It is the discipline of slowing down to apply critical thinking at exactly the moment AI seems to speed things up. Seen this way, Slow AI is not a brake on productivity, but rather positive friction designed into the organisation.

What might this look like in practice? How can you deliberately build in that friction?

• Human first. A standing rule that the thinking starts with you, not the prompt: form your own view, then bring AI in to challenge or extend it. This is positive friction in a single habit and one that came up repeatedly in my interviews.

• Depth over throughput. Stress-test one idea rather than generate ten reports. Reconsider success to be how well something was considered, not how much was produced. The metric of success shifts from how much you produced to how deeply you considered it.

• No-AI zones. Name the decisions where AI stays out of completely or stays advisory only, especially high-stakes judgment and the difficult human conversations. Our instinct is to avoid what’s hard, but that dilutes the impact those decisions have and our own accountability.

The New Leadership Obligation

I gave healthcare as an example earlier, of what happens when introducing AI makes the balance of work wonky. Now to be clear, I’m not saying you should waste doctors, or in fact any employee or individual’s workday, on repetitive work. But there is something about what people can work with, as the KIMRA director’s comment showed.

When you are thinking about a new AI tool, are you also showing a level of empathy with how the work is going to change? Are you able to understand how that balance of work in a person’s day is going to change, and whether they’re well placed to cope with that change?

You could argue that everybody just needs to get on with things and adapt, and that it is a matter of time. Of course that’s true. All of us are constantly adapting to the new world of work that is emerging and every transition has its challenges. Yet the job of a leader is by definition to lead. And to lead, whether you are already in that position or aspiring to be, you need to be thinking, not just about the present but also about the future state.

Some of this is about protecting your people and being a responsible employer. But there are further considerations.

Are you thinking about:

• The financial cost: What will the hidden cost to your company be in medical insurance claims, mental health leaves, and talent churn as burnout spikes across your workforce?

• The psychological shift: How is sustained, non-stop interaction with AI changing the way your employees think, feel, and behave in these hybrid human-machine environments?

• The duty of care: Is protecting your employees’ cognitive bandwidth and managing AI-related wellbeing about to become your primary leadership obligation?

If l leave you with one thought it is this:

The backlash against Bartlett’s three lost days tell us a lot more than we realise about AI adoption and the new leadership obligations that are emerging.

This week I co-hosted a roundtable at SXSW London alongside Anita Cleare on “AI readiness starts at age zero”. Around the table were parents, educators, HR directors, and policy folk, which made for exactly the kind of cross-disciplinary friction this topic needs. This is the case I made to them.

Anita and I hanging out at the speaker’s lounge beforehand.

The useless binary

Many people are increasingly seeing that the binary division into AI optimists and pessimists is completely useless. The techno-optimists tell us that AI will bring a world of abundance whereas the techno-pessimists warn us about the destruction of humanity. Makes for excellent clickbait but it doesn’t leave you any wiser. In the debate around AI the optimist/pessimist binary is the equivalent of junk food.

How do we balance this out?

By taking a longer-term, systems-wide perspective.

The real issue we should be focusing on is what actions you take today and tomorrow to close the AI readiness gap.

The AI readiness gap that’s becoming a chasm

The AI readiness gap is the disconnect between what companies - and governments - are spending on their AI technology infrastructure on the one hand, and how prepared and confident people feel to use those tools. Only 17% of the UK public say they can explain AI in detail and 28% feel confident using AI at work.

Lack of confidence with AI is by no means just an employee issue. My own data from the research I’ve conducted for my forthcoming book “AI-Q” shows this. When I asked 200 leaders how confident they felt about leading their teams through AI-driven change just 31% of them report feeling very confident.

It is no surprise that words like techno-stress, AI-slop and brain-fry are now commonplace in our daily vocabulary. At another conference I spoke at this week, KIMRA, (yes June seems full of events!) I frequently heard people use the word “overwhelmed”. The audience was professionals from the research, insights, knowledge management and thought leadership functions across banking, legal firms and consulting.

There seemed to be two camps.

Those that felt giving us all a powerful tool doesn’t free up time, it instead expands the range of possibilities available to us, and we end up wanting to do more. All of a sudden you feel that you can do more, so you should, making it harder to switch off. With one attendee we talked about what this might mean for future health insurance claims down the line, but I am diverting away from the topic at hand.

The other camp was those that felt overwhelmed by the pace of change, and the renewed expectations by their stakeholders to deliver more and faster than ever before, and in some cases that led to a freeze response. For a function like research and insights where quality is paramount, that acceleration is hard to adapt to.

Why does all this matter? Because much of the conversation around AI still focuses on which tool is better, chatGPT or Claude, what is the mother of all prompts that will avoid hallucinations (er, none), and the different cost of tokens. When you talk to real people you realise the complexity of what is at hand.

Canaries in the coalmine

Today’s graduates are the canaries in the coalmine. The eponymous research paper by Erik Brynjolfsson, Bharat Chandar, Ruyu Chen find that:

“since the widespread adoption of generative AI, early-career workers (ages 22-25) in the most AI-exposed occupations have experienced a 16 percent relative decline in employment even after controlling for firm-level shocks. In contrast, employment for workers in less exposed fields and more experienced workers in the same occupations has remained stable or continued to grow.”

I see this on the ground and first hand through my work with BossUp.AI, where I get to speak to many young graduates here in the UK, and they tell me they feel let down by the education system’s failure to prepare them for a world of work, or at least the recruitment aspect and the skills they will be asked to prove in order to get a job. Some tell me they apply for 500 jobs without a response and how they find that “soul destroying”. Some wonder whether they should give up, or ask me what they are doing wrong. Is it any surprise that in the UK alone we have more than 1 million young people that are not in education, employment or training (NEETs)?

They see the headlines. Every week brings another story about AI replacing entry-level jobs, another CEO boasting about hiring freezes. Imagine being 22, applying for your five hundredth job, and reading that. The message young people receive is that the economy is being redesigned without them in it. No wonder that recent speakers at US graduation ceremonies have been booed at the mere mention of the word “AI”.

The AI skills paradox

I’ve been asking executive after executive that I interview or speak to “which skill, or ability, will be the most important in the future?”

They consistently say that the skills they will need, and the skills they will value the most are:

1. critical thinking

2. empathy

3. strategic thinking

4. creativity

5. learning agility

These abilities will be the biggest differentiators in a workplace where human and artificial intelligence co-exist, and what they all have in common is that they are deeply human abilities.

The irony lives in the fact that those same executives are investing in AI tools1 that are undermining the pipeline of these skills for the future. In many cases, whether it’s because that is the way the AI tools have been designed or because of the way we choose to use them, they end up removing the mechanisms through which these abilities are developed. How can you develop your critical thinking skills when AI tools are designed to bypass that altogether?

Here it’s essential to distinguish between someone that has developed a particular ability over the years, and that ability atrophies, and someone who never develops that ability in the first place, such as our children and young people. This is why the parenting/carer angle matters so much.

The parenting angle

Here are the provocations my co-host Anita Cleare shared with the group:

• Lack of time + cultural pressure to optimise children leads towards solutions that promise big learning gains for minimum adult input. Many parents see tech skills as a route to helping kids get ahead.

• Young children build their brains and human skills through real world play and inter-personal interaction. For example, the brain develops differently when you learn by touching something physical or by putting pen to paper, compared with reading something on a screen.

• The children who will thrive in an AI age won’t be the ones who use AI earliest. This chimes with what I hear from executives: the skills that will be needed most in the future are the human skills, and those are built away from the screen, in the playground, around the kitchen table, in the messy business of dealing with other people.

She also shared a stat that in some parts of the UK it is reported that 38% of 10 to 18-year-olds find it easier to talk to AI than to their parents.

The case for treating childhood as economic infrastructure

All this begs the question, what do we do about it?

My argument is that we need to start treating childhood as economic infrastructure. Preparing the future generation for the world of work should not start when they are 16 and approaching their final studies, or after they’ve graduated. It needs to start much earlier, hence “age zero”. It means that we need to start realising young people are the most essential diffusion mechanism.

One criticism I have had is that this is a rather cold view of seeing our children, and that it commoditises childhood. I would argue the opposite. Economic incentives right now are geared towards:

• eliminating friction

• speeding up teaching

• rewarding instant outputs over slow skill-building

But once we start seeing childhood as economic infrastructure, the logic flips. We do not commoditise our roads, energy grids or broadband networks by calling them infrastructure. We protect them. We invest in them for the long term, we maintain them, we hold ourselves accountable when they degrade, and we treat underinvestment as a national risk rather than a private problem.

What if we apply that lens to childhood?

Three things change.

First, responsibility shifts. The development of critical thinking, empathy and creativity stops being something we outsource to individual parents juggling impossible time pressures, and becomes a shared obligation across employers, educators, technology companies and government.

Second, time horizons shift. Infrastructure is planned in decades, not quarters. The executives investing billions in AI tools today would be forced to ask what those same tools are doing to the talent pipeline they will depend on in 2040.

Third, design shifts. If childhood is infrastructure, then friction is not always a bug or problem to be eliminated. Some friction, the kind involved in wrestling with a hard problem, learning to read another person’s face, or putting pen to paper, is precisely how the “asset” gets built. Tools designed for children, and for the adults raising them, would need to protect that productive friction rather than engineer it away.

Far from being a cold view, this is the warmest argument available. It says that what happens in a sandpit, around a dinner table, or in an unhurried conversation between a parent and child is not a soft, private nicety. It is the foundation of every deeply human ability those executives tell me they will pay a premium for. The coldness lies in the current arrangement, where we let market incentives quietly strip-mine those foundations and then express surprise when graduates arrive at the labour market underprepared and overwhelmed.

What you can change on Monday morning

For parents and carers: you do not need to teach your five-year-old to prompt. Prioritise the real-world play, conversation and boredom through which the brain builds the abilities AI cannot replicate. As Anita said in her commentary: “The children who thrive in the AI age will not be the earliest adopters, they will be the ones with the strongest human foundations.”

For employers: stop treating entry-level roles as a cost to be automated away and start treating them as the apprenticeship system for your future leadership. Examine how those entry level workers could be the very diffusion mechanism that your AI investments need to scale across the organization. If the bottom rung of the ladder disappears, so does everyone who would have climbed it. Audit what your AI deployments are doing to skill formation, not just to productivity.

For policymakers: the AI readiness gap will not be closed by infrastructure spending alone. If we can publish league tables for school buildings and broadband rollout, we can measure and invest in the development of human capabilities from the earliest years, and we can hold the system accountable for the one million young people currently locked out of it.

The optimists and pessimists will keep shouting at each other. Meanwhile the readiness gap widens, the canaries keep singing, and the abilities we say we value most are quietly going underfunded at the exact stage of life when they are built. Treating childhood as economic infrastructure is not about turning children into assets. It is about finally giving the foundations of human ability the seriousness, the investment and the protection we already give to everything else we cannot afford to lose.

I am trying to decide if statements like this by major consulting players are true and what could possible drive them. Do they reflect what their leadership teams actually believe - or is it a marketing ploy to show to their clients how much they are embracing AI and how progressive they are? I am not sure. On the one hand you have executive ignorance, on the other storytelling cynicism. I really hope it’s the latter.

Not because that is the right approach to take, but because the alternative is worse.

Here is what leaders think they mean when they say AI sceptics have no place in their company:

• We are a progressive, forward-thinking organization

• We are embracing the latest technologies

• We are leaders in AI, so clients you can trust us with your AI adoption journey too

Here is what everyone else hears:

• Leaders are detached from my everyday reality

• Leaders have never had to use the sub-par internal tools we’ve been given

• Leaders think that I need to train AI tools to take my job

And to be clear, though PwC is the one making these claims, this situation is playing out across many consultancies, so they are by no means alone in this approach.

So what is going on?

Executives are not paying enough attention to is what happens when you take a “drop and hope” approach to AI.

Employees are already overworked and overwhelmed. Research by LinkedIn showed an 82% increase in the number of professionals saying they are feeling overwhelmed. AI workslop, poor quality AI-generated workslop, is proliferating, with 50% of employees admitting to sending such content to their colleagues. Brain-fry is becoming a phenomenon as the addition of AI tools in the workplace intensifies the nature of work, making it a more pressurized environment.

And yet leaders look at all of this and conclude: I know what the problem is! Our people are just not positive enough about AI.

I don’t think that's what is happening here.

I think sceptics are giving us valuable information about where and why AI is not working.

The word sceptic comes from the ancient Greek skepsis , meaning to question, to interrogate, to examine carefully. The original sceptics were the ones asking the most thoughtful questions. Somewhere along the way, we turned that into a personality flaw.

Think about these AI related situations where people appear sceptical:

• The employee not using the AI tool you have built in house. Are they resistant to change or is the tool actually adding to their workload because in reality it's not that good?

• The senior manager that's being asked to hit the same targets with 20% fewer people because "AI makes up the difference". But are the AI tools a full match for those jobs yet?

• The software developer that raises the alarm about a bias but is told they are not moving fast enough. Are they slowing things down or ensuring your product doesn’t invertedly cause harm?

• The lawyer asking “am I losing the ability to review contracts for myself”. Are they stuck in old habits or raising the alarm about cognitive atrophy?

In many ways those individuals that you might initially deem to be sceptics you just want out of your company because they are holding back AI progress are the very people telling you where things are going wrong in your AI adoption. They are all giving you important signals about where to focus your attention in order to get the most out of your AI investments.

Some executives may choose not to listen to these signals out of fear of being left behind (FOBO). Others because there is so much noise out there that it becomes to hard to distinguish between what’s a true signal and what is not.

Ironically PwC’s own research found that 42% of executives say their companies are stuck and unable to unlock either revenue or cost benefits. Their own clients are showing scepticism around AI, how it is being implemented in their organization - and whether that impact will ever show up on their metrics.

This is why sceptics deserve to be heard instead of being shut down. We need to engage with their arguments and understand what is hiding behind their resistance. Because they can give essential information about where AI adoption is going off the rails, and how to fix it.

So before you write off your sceptics, ask yourself these questions:

1. Do I really know what my employees are experiencing when they use our AI tools day to day?

2. Have I personally invested in understanding AI well enough to lead this, not to become a technical expert, but to ask the right questions?

3. And if one of my best people came to me tomorrow and said "I don't think this AI rollout is working", would I hear that as a problem, or as the most valuable piece of intelligence I've received all week?

4. If my sceptics are not the obstacle, but the only people in the room telling you the truth, what does that reveal about my AI investments?

I reckon AI sceptics are doing companies a favour. We just don’t know it yet.

I know every data point has to be verified, accurately represented, and from a high-quality source. (Sorry, Reddit.) It was something drilled into me both during my studies and when I first started working. And yet, after fifteen years of research experience, being sceptical and thoughtful about my sources, I was fooled by Claude.

This can happen to anyone.

And it’s the smaller version of a much bigger story: what AI is doing to truth, and trust, at work. Three challenges. Three small things you can do about them tomorrow morning.

I shared this earlier today at The New Normal, a community Paul Armstrong has built, on a panel with Susan Walsh (The Classification Guru) and James Ball (Pulitzer-winning investigative journalist and author).

Many people in the audience said they found it useful, so I’m sharing it here too. Do check Susan, James and Paul out, they’re worth your time.

THE FUTURE OF TRUTH

We all gathered at the event to talk about the future of truth.

Yet we have no common understanding of what TRUTH actually is.

Is it facts? Is it beliefs? Or something else entirely? And why does it even matter for success in the world of work?

Before we get to the future of truth we need to look to the past.

In ancient Greece, the philosopher Parmenides, writing around 500 BC, used the word aletheia. Literally translated, aletheia means not concealing. Revealing. Bringing into the open. For Parmenides and the Greeks who came after him, truth was something that came out of hiding. It was about disclosure.

Fast forward a thousand years and you find a very different idea. By the time we get to the medieval times, truth has become something more like faithfulness: being faithful to your beliefs, to your word, to a doctrine, to a sovereign.

And now skip forward again to 2016, when Oxford Dictionaries declared the word of the year to be “post-truth”. Use of the word had jumped 2,000% in twelve months. Oxford defined it as a state in which objective facts matter less than appeals to emotion and personal belief.

Two things are clear from this brief history scan:

1. That the very notion of truth has evolved over time. We don’t have a shared definition of what truth is. It could be about accuracy and “facts”. It could be about authenticity. Or it could be about being transparent and not hiding anything.

2. We might not have a shared notion of truth but what we DO SHARE is a belief that it matters to us. The sheer weight of resources, time and thinking that humanity has poured into asking that question across philosophy, science, religion and law is itself the evidence of how much we care about it.

We care about it because truthfulness is what allows us to build trust over time – which is the fundamental building block of our relationships. Trust is what allows us to succeed at work, with each other, in our lives.

When truthfulness goes, so does trust.

TRUTH MATTERS FOR TRUST AND TRUST MATTERS FOR SUCCESS AT WORK.

Look at what happens when trust breaks down and the productivity impact.

The Edelman Trust Barometer is an annual survey of trust that has been going for 25 years. They survey 30,000 people across 28 countries. In their most recent survey they looked at the crisis of trust and what that looks like in the workplace.

Think about this:

• 42% said they would rather switch departments than report to a manager with different values.

• A third would put less effort into helping a team leader who held different political beliefs. That’s the productivity impact.

TRUTH AT WORK

When we say “truth” at work, we usually mean one of three different things:

1. Truth = accuracy. Does the output match the facts? Is the data right?

2. Truth = sincerity. Does the person mean what they say? Are they standing behind it?

3. Truth = transparency. Can I see where this came from? Can I trace it back?

Now enter AI.

As AI enters the workplace it alters how truth and trust are built and maintained. And it’s bringing a new set of challenges.

1. Truth ≠ accuracy

AI challenges the relationship between truth and accuracy.

It does this through hallucinations and workslop.

Not all AI is the same but generative AI is famous for its hallucinations. This matters for accuracy and for the quality of our work.

Last October, Deloitte Australia had to refund the Australian government after delivering a A$440,000 report on the country’s welfare-penalties system. The report contained multiple fabricated citations to books and papers that didn’t exist. The irony - Deloitte announced the refund the same day it announced a global partnership to roll out Claude to its half-a-million employees.

And then there is workslop - which unfortunately we’re all becoming more familiar with.

Our baseline assumption is that we send our best-quality work to our colleagues. Yet, more than half of us now admit to sending AI-generated content that we haven’t reviewed.

If that quality baseline collapses, how do we know what's real?

2. Truth ≠ sincerity

AI challenges the relationship between truth and sincerity.

Early in my career I worked as an economist for ArcelorMittal, in their shipping division. The first thing I learnt was the phrase “my word is my bond.” This is not just a saying. It governs how the Baltic Exchange, the world’s oldest shipping market, has functioned for centuries.

Historically most negotiations between brokers and charterers were done initially in person at Lloyd’s coffee house, and more recently by phone, before being put in writing. If your paper trail did not match what you had verbally agreed, your reputation in the market - and your ability to do business - was undermined.

I think about this example precisely because shipping is not an industry known for leading on AI adoption. Yet it relied on a trust mechanism that allowed complex, high-value transactions to move quickly. My point is not that older industries “did trust better” but that trust used to be designed into everyday work through visible interactions. AI in our workplaces removes many of the signals that once enabled trust.

The other angle that matters here is how management is perceived. A study last August by the University of Florida and USC asked employees to rate managers’ messages. When the manager used minimal AI assistance, 83% of employees rated them sincere. When the manager used heavy AI assistance, that dropped to 40-52%.

This has important implications for anyone that manages other people.

3. Truth ≠ transparency

The third way that AI is challenging truth in the workplace is transparency.

At work - and especially for regulated industries - we expect that our colleagues are able to show us how they arrived at their output and that traceability element is essential.

It turns out that AI is making it harder to do that because we won’t admit to it.

A KPMG study of 48,000 employees found that 53% present AI-generated content as their own.

What is happening here is our emotions are coming into play.

Some people fear being seen as less competent if they had to use AI to deliver their work.

Others see it as a threat to their identity – if they’re known for a particular expertise then admitting to AI usage threatens that.

For another group of people it might be simply not knowing what the rules of the game are because companies haven’t figured out the governance yet.

There are two sides to this and this is where it gets a bit trickier. The challenge of non-disclosure so far comes from us as humans and our emotional responses. But it also comes from the evolution of the technology itself.

Right now we’re debating disclosure as if AI is something you pick up and put down. But AI is becoming a feature, not a product. It’s already in our search, our meeting summaries and our spreadsheets. At some point the question “did you use AI for this?” becomes like asking “did you use spell-check?”

WHAT NOW?

We have seen how AI is challenging the relationship between truth and accuracy, truth and sincerity and truth and transparency.

AI is a powerful set of technologies but it’s the way that we use it that will shape the future of work.

What I thought would be helpful is to share with you some of the emerging findings from the research for my forthcoming book “AI:Q the new leadership imperative for the Human+AI era” – and it is based on 200 senior leaders taking the AI-Q diagnostic and 50 interviews.

Here are three of the areas where high AI-Q leaders are different from the rest, and what we can learn from it.

1. PROTECT YOUR THINKING

Critical thinking kept emerging as one of the defining capabilities for leadership in the AI era. Not as a personality trait but as a discipline. The willingness to be the friction. The muscle to slow down when everything around you is engineered to speed up.

Here is what I’d ask you to do.

Build a verification habit before you build an AI habit. For every output that will leave your desk under your name, ask these questions:

• Does this make sense?

• Where did it come from?

• What’s missing?

• What biases and assumptions are baked in?

Your most important contribution in the AI era is not using AI faster than your colleagues. It is being the person in the room who interrogates what it produces.

2. “KNOW” PEOPLE

Building relationships with others will increasingly be what you uniquely bring to the workplace.

Two specific actions.

First, have the conversation with your team about what AI means for their roles. One of the questions I asked in the survey was for leaders to rate their confidence in leading their teams through AI-driven change. Yet almost a quarter of leaders who described themselves as confident had not actually had a conversation with their teams about that very change.

Second, align with your peers on what counts as a win. The same AI initiative looks like a success to one function and a failure to another. Get explicit about what matters before you measure outcomes, not after. This emerged as one of the key reasons why some companies are struggling to capture the ROI of their AI investments and it’s not showing up in their metrics.

One story I came across that I thought really showed the power of relationships and how they’re becoming even more important was that of Ferrari. In July 2024, an executive at Ferrari received WhatsApp messages and a phone call. The voice on the other end was the CEO Benedetto Vigna’s perfectly cloned, southern-Italian accent intact. The “CEO” needed help rushing a confidential currency-hedge transaction.

The executive asked one question: “What was the title of the book you lent me last week?”

The scammer hung up and Ferrari avoided a costly scam.

3. FIND YOUR RED LINES

Decide where you will use AI and where you will not. Be deliberate, write it down, and communicate it to the teams you work with.

A practical exercise. Sort your work into three categories.

Where you will use AI freely.

Where you will use AI but always review personally.

Where you will not use AI at all.

Then share that list with your team this quarter, so they know how to read the work that comes out under your name.

The CEO of a global publishing house told me he had worked with his marketing team to train an AI agent to write speeches and emails in his voice and tone. But he never let the agent or the team send anything out without personally editing every line. His red line was simple: nothing goes out under his name without his sign-off. Authenticity of voice was non-negotiable, even when AI could replicate it.

When I looked at the practices distinguishing high AI-Q leaders from the rest, this was the single biggest behavioural gap. Not who used AI most but who had decided most clearly where they would not use it.

We started off with a brief history of truth. But the future of truth is still being written, today, by you and me and the actions we take. Every small action holds power.

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Similarly the debate around AI literacy tends to go two ways:

1. the education system and how we teach our kids about AI

2. coding skills and prompt hacks, often shared by tech-bros and LinkedIn influencers.

I see AI literacy as much more than these two areas. Those have their place—but are not sufficiently representative of what our AI literacy needs are.

I see AI literacy as equipping leaders and teams to understand how AI could change what their business does and how it does it. It means being able to spot where new competitors might come from, often outside your industry. And it means thinking ahead about how AI might open up new markets and products for your organization, or demand new and creative ways of working.

In other words it is about leadership. Yet our understanding of AI literacy for leaders remains under-explored and under-developed. Read on to discover why this gap matters and how you can build your own AI literacy as a leader.

The signals of change

Over the past few weeks I’ve come across three very different examples that reveal how AI is already disrupting how leadership works, how authority is structured, and how strategy is shaped.

To be clear, these aren’t examples of “good” or “bad” AI literacy. They're signals that the current definition and approach to AI literacy isn’t suitable for leaders.

1. WPP’s CEO resigns under pressure from AI-driven competitors

   
   Mark Read recently stepped down as CEO of WPP. The company’s revenue and share price have come under pressure — not just from rival ad firms, but from entirely new competitors like Meta, which offer AI-generated campaign solutions at scale.
   

   What does this show? That the traditional playbook of competing with similar peers is crumbling. leaders now face disruption from platforms, models, and actors with entirely different capabilities.

2. Dubai gives AI a formal seat in government

   
   Starting January 2026, the UAE will introduce a National Artificial Intelligence System as an advisory member of its Cabinet. It will analyse data, assess the impact of new laws, and propose updates.

   
   What does this show? That decision-making is being reshaped. the idea of a single leader at the top is evolving into something more distributed — with AI as a co-pilot in governance and policy.

3. Accenture reorganises into ‘reinvention services’

   
   Accenture, one of the world’s largest consulting firms, recently merged its five major business units, Strategy, Consulting, Song, Technology, and Operations, into one integrated division called “Reinvention Services”.

   What does this show? That leadership today isn’t about overseeing discrete parts of a business. it’s about orchestrating transformation in more complex environments and that requires different capabilities, mindsets, and literacies than traditionally valued.

Thinking about all of these together, they illustrate how the impact of AI goes beyond being just an operational or technology tool. It’s reshaping what it means to govern, lead, and compete.

So if you’re a CEO, business leader, strategist, or board member wondering what ‘AI-ready leadership’ really looks like, then read on - this post is for you!

Why does this change matter for you?

The working definition of AI literacy is far too narrow. It’s being treated as a technical or tactical skill.

But as these example show, the real shifts are strategic and structural. They’re about how leaders anticipate change, build trust, and rethink value creation.

If you’re a senior leader and you’re thinking of AI as someone else’s problem, something for IT to handle, or for new grads to adapt to, you’re introducing a massive blind spot.

What can you do to prepare and become an AI-literate leader?

The question that follows is how to future-proof yourself and act today to prepare for these changes. What does AI literacy really look like for a business leader? This is a question that is so crucial for the future of business, yet so woefully underexplored.

My current thinking, based on my research so far and conversations with nearly 100 leaders over the past year, is that there are two ways to frame what needs to change and how you can take practical steps to build your AI literacy.

The first is what I call MAP: “Mindset-Skills-Processes”

• Mindset: stop assuming AI disruption is for other people in your organisation. If you’re only focused on junior roles, you’re missing where the real risks and opportunities are.

• Abilities: yes, you need foundational knowledge of AI. but beyond that, can you lead under uncertainty? Can you read the signals of change early enough?

• Processes: how do you make decisions? How do you test assumptions? How often do you revisit what your strategy is based on? Leadership in the AI era means constantly adjusting your processes to be future-fit.

I have yet to find a good acronym for the second one (!) but the three dynamics that AI-literate leaders confidently understand are:

• Technology: understand the tools, their limits, and where they’re heading. You don’t need to code, but you do need to grasp the trajectory of what’s being made possible. Are you fluent enough to spot the real opportunities?

• People: This is about trust, ethical leadership, and communicating change, as well as demonstrating your value as a leader and the ways you interact with other members of the leadership team. Are you building the culture and confidence to lead with, not just through, AI?

• Direction: Being AI-literate means understanding how your organisation’s positioning, priorities, and possibilities are shifting. Are you asking the right strategic questions? Are you alert to signals beyond your usual field of vision?

I’ll be exploring both of these framings in future pieces because I strongly believe that we need better answers to a simple but urgent question:

What does it really mean to be an AI-literate leader?

So join me on the next newsletter of The Athenian Intelligence where I will be breaking down each of those two framings, along with practical steps for each one.

AI literacy matters….

…For the future of global prosperity

Do you ever think about switching on a light when you walk into a room? Or just pulling out your phone and using the internet to search for something?

Chances are you don’t.

Why?

Because it’s so commonplace that you don’t need to think about it, it’s become so engrained into your routine and daily life that you don’t even give it a second thought.

But that is not the case for everyone in the world.

Electricity has been around for 100 years yet 750 million people still do not have access to it. The internet has been around for 30 years and still 2.6 billion people do not have access to it.

And even among the remaining world population that has access to it, that does not mean they can reap the benefits, because they may not have the literacy and skills to use it. The OECD 2023 Digital Skills and Digital Inclusion report estimates that around 30% of Americans and 42% of Europeans lack basic digital skills, and this is contributing to a growing digital divide: the gap between those who can access and effectively use technology, and those who can’t.

My mum has access to google maps on her phone for example, but she still does not use it to get around because she doesn’t know how to, and it doesn’t feel familiar. This matters because it limits where she can go, and crucially her confidence to try out new places.

Those two innovations, or as economists would call electricity and the internet “general purpose technologies”, have become so important for human progress and our quality of life that they are part of the United Nations Sustainable Development Goals.

I believe that as AI becomes more embedded into the fabric of our everyday lives, it will follow a similar path. The risk is that it will create a new divide in our world, between countries, and even within countries between those that understand and use the technology, and those that do not.

AI literacy matters….

…For our future economies

Did you know Microsoft signed a 20-year deal to purchase power from a Pennsylvania nuclear plant that will reopen in 2028, providing energy for its power-hungry data centres for artificial intelligence (AI)? Microsoft also admitted this year that energy use related to its data centres was endangering its moonshot target of being carbon negative by 2030.

Or that Meta, a company known for the motto “move fast and break things” that in many ways encapsulates all that is wrong with the tech industry’s approach to innovation, said about the free access of their AI to the NHS: “It is in the long run indirectly in our interest to see this ecosystem of Llama-based innovation because it then makes it much easier for us to reincorporate innovations that are out there into our own products.”

Or that the so-called Magnificent Seven now account for 38% of all US R&D spending by publicly listed US firms, growing from 12% in 2012.

The roll out of AI is shifting power dynamics and concentrating it in a handful of companies.

How can we shape AI for good if we don’t even know how it works or where it is being used? How can we hold the tech giants accountable when we are not even aware of their true power?

AI literacy matters….

For the future of business

Businesses are putting big money into AI. Will their investments in the technology pay off without the associated investments into people?

Enterprise AI spending reached $13.8 billion this year, 6x since 2023. And companies are spending $1 trillion on capital for AI projects, such as data centers, chips and other infrastructure.

Beyond the corporate world, governments are rushing to explore how AI can help them solve the backlog of issues that built up during the pandemic and they’ve been unable to cope with. Meta ran its first “hackathon” in Europe asking more than 200 programmers to devise ways to use its Llama AI system in UK public services. In October 2024, Microsoft agreed a five-year deal with Whitehall departments to supply its AI Copilot technology to civil servants.

Those are major transformation projects, yet we know that 7 out of 10 business transformation projects fail. And that AI projects have twice the failure rate of other IT projects.

One of the main root causes of failure is a misalignment between the leadership expectations of outcomes and the reality that people and employees experience on the ground. Research by Accenture on the future of work uncovered such gaps between CxOs and employees. For example, all CxOs that they surveyed—100%—anticipate changes to their workforce, e.g., growing or reducing headcount and implementing plans to reskill. Yet only one in three leaders believe they have the technology expertise or feel they can tell a compelling transformation narrative to lead the change that’s needed.

Leaders need to start taking seriously, and address, worries about the impact of AI on people’s jobs. Think about this: while almost 60% of workers worry about AI eliminating their jobs, less than one-third of C-suite leaders feel job displacement is a worry for their people. Clearly this is a blind spot.

Source: Accenture

It might be that regulation is what forces companies to start taking AI literacy more seriously. The EU AI Act came into force in February 2025 and states that “providers and deployers of AI systems shall take measures to ensure, to their best extent, a sufficient level of AI literacy of their staff and other persons dealing with the operation and use of AI systems on their behalf, taking into account their technical knowledge, experience, education and training and the context the AI systems are to be used in, and considering the persons or groups of persons on whom the AI systems are to be used.”

AI literacy matters….

For your future job

My 5 year old son will need to master skills to thrive in the workplace of the future, skills that I cannot even conceive of.

As our world becomes more complex it becomes harder than before to imagine the jobs of the future. Would my parents for example have thought of prompt engineering when I was 5 years old? Or “social media influencer”? Highly unlikely for many reasons, not least that at the time there was an ‘AI winter’! But joking aside, the point is that new jobs will emerge.

Research by MIT economist David Autor using the US Census Bureau data over the past 100 years has found that 40% of the jobs that we have today didn’t exist in 1940. “We estimate that about six out of 10 jobs people are doing at present didn’t exist in 1940,” says Autor. Interestingly, many new jobs are created by technology. For instance, “Engineers of computer applications” was first codified in 1970, “Circuit layout designers” in 1990, and “Solar photovoltaic electrician” made its debut in 2018. But not all: Some come from consumer demand. “Hypnotherapists” was codified in 1980, and “Conference planners” in 1990. “Technician, fingernail” became a category in 2000. In the future there will be demand for health care services jobs for an aging population.

The creation of new jobs will also come with the obsolescence of existing jobs. For example, can you guess what happened in 1950 that lead to the number of elevator assistants plummeting in the US? Otis launched the “Autotronic” automatic elevator.

Which jobs that we have today will disappear or decline? It can be hard to know, but AI literacy will be a key tool for empowerment and individual resilience.

The path forward: Collective AI understanding

We don’t know how AI will evolve.

One response to that uncertainty is to do nothing. Wait and see what happens and decide how to proceed when the need arises.

Another more forward-looking response might be to arm ourselves with knowledge and understanding of what the AI future holds, enabling us to be better prepared.

My vision is to move beyond a world where people are split into AI-optimists and AI-pessimists, into a world united around the importance of AI literacy. AI literacy isn't about becoming a computer scientist, but the need for curiosity on the technology reshaping our world. And this is why curiosity is the future builder.

Share this with others that you think need to know about the problems we face around AI literacy and what it means for our world, our economies, our businesses and our jobs. Join me next week to imagine what that world looks like and how we can illustrate that vision.

[The audio version is AI-generated using NotebookLM].

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Let’s take a step back.

We have a problem.

We live in a world where people are split into AI-optimists and AI-pessimists, instead of a world where we are united around the importance of AI literacy.

For example, if I mention the word “robot” do you think of “The Terminator” or “Wall-e”?

If you get asked at work to use a new AI technology* being rolled out, is your instinctive initial thought “I will lose my job” or “These tools are useless”?

Chances are that your mind goes to one extreme or the other. A bit like this Dall-e image!

Source: Dall-e

Asking the wrong question

Over the past year, I’ve had the opportunity to speak to people from diverse backgrounds while building my own business. A common thread emerged: When I speak to technologists, they believe that AI will solve our world’s greatest problems. They see it as a panacea. In Greek mythology, Panacea was the goddess of universal remedy and healing.

Consider the associated headlines: “An inside look at AI’s potential to achieve the SDGs” says Devex. “AI versus cancer”: the Cambridge researchers using machine intelligence to beat disease. AI crucial to tackling environmental challenges, say experts at Turing and Met Office event. Forbes says: “5 Ways To Harness AI And End Poverty Forever.” The message is one: AI will help us solve wicked problems: those complex, multidimensional challenges that we have struggled to address so far.

Yet, outside of the tech world, among the greater majority of people I speak to, AI is either seen as a threat to jobs or dismissed as overhyped. This fear or misunderstanding poses a risk that we will miss out on the opportunity that AI can bring to our world.

As Frank Herbert wrote in Dune, “Fear is the mind-killer.” It clouds judgment, stifles innovation, and prevents us from seeing AI for what it really is: not an unstoppable force for good or evil, but a tool we need to—and can—learn to wield wisely.

At some point in the exchange, the person I’m speaking with will inevitably turn to me and ask “What do you think? Are you optimistic or pessimistic about the future of AI?”

My belief is that this is the wrong issue to focus on, the wrong question to ask in the first place. We should not be divided into AI-optimists and AI-pessimists. The real issue at stake is whether we are sufficiently knowledgeable about AI to be empowered to shape our future.

The evidence suggests that we are not sufficiently knowledgeable, and in fact, an AI literacy gap is looming.   

US-based research found that only 30% of respondents can correctly identify common uses of AI, and 44% do not believe that they interact with AI regularly (both are indicators of awareness). Worryingly, gaps widen among underrepresented communities. Only 15% of Black and 23% of Hispanic Americans have a high level of awareness of AI in their daily lives (compared to 34% and 40% for White and Asian Americans), according to the same research.

Other indicators tell a similar story. A study by the Reuters Institute at the University of Oxford examined public awareness and use of generative AI across six countries. In Argentina 59% of those they asked had never heard of ChatGPT.

Source: Reuters Institute

Some promising steps are underway. But grand scale announcements are not sufficient to close the gap given how quickly the pace of AI technology is evolving. For example, in March 2024 the IndiaAI mission was announced. This includes a FutureSkills program, which will focus on expanding the reach of AI education by increasing the accessibility of graduate and post-graduate AI programs, while also establishing Data and AI Labs that will not just be spread across major Indian cities but also smaller towns and cities to impart foundational-level courses in Data and AI.

What are some of the factors driving the AI literacy gap?

Factor 1: Fear.

Many people fear AI. In a YouGov public opinion poll conducted in the US at the start of 2024, the predominant feelings reported in association with AI were negative or fear-related.

And fear is not unfounded. Humans are biologically programmed to fear change, and arguably it is what has driven the survival of our species, so far. Our amygdala are exceptionally good at detecting changes in our environment and flooding our body with hormones that enable us to respond, whether that is fight or flight. So it is unsurprising that we may fear AI, as it portends significant change ahead and a change that we do not yet understand and are not prepared for.   

Factor 2: Doubt.

The second factor driving an AI literacy gap is doubt about its potential.

Some people believe AI is just hype and they just don’t need to worry about it. Just another fad, a bit like the metaverse, that will pass. In November 2024 I was in a leaders workshop, with people across the public, private and non-profit sector. One of the questions I got was “Are the use cases there? Is AI really having an impact?”

This is justified given the constant barrage of hyperbolic claims we see in the headlines, where a natural response might be doubt. Jeffrey Funk, is a retired professor, now an independent consultant, who calls himself “an AI hype buster”. Ha believes the potential of LLMs, like OpenAI's ChatGPT, is overhyped, and this is one of the themes in his book released in 2024: Unicorns, Hype and Bubbles: A Guide to Spotting, Avoiding, and Exploiting Investment Bubbles in Tech. In response to this I would highlight some data points. For example, while sales of VR headsets remain sluggish, ChatGPT was the fastest growing app of all time, reaching 100 million users within 2 months.

We’re also seeing a rapid acceleration in AI innovation and intellectual property development. By 2022, the number of AI patents granted globally skyrocketed to over 60,000, according to research by Stanford’s Institute for Human Centred Artificial Intelligence (chart compiled by Goldman Sachs).

Factor 3: Irrelevance.

Others believe AI is not relevant to them. Some dismiss it as futuristic or unlikely. Yet if history has taught us anything is that profound changes are the accumulation of a series of small steps. The adoption of AI in our world will not be “an event”. It will be a series of mundane changes made by companies that will change our future.

All of these three factors—fear, doubt and irrelevance—come together to exacerbate the gap between technological innovation and our response as a society. The pace of technological innovation is outstripping our ability to make sense of it, and master it.

Others believe AI is not relevant to them. Some dismiss it as futuristic or unlikely. Yet if history has taught us anything is that profound changes are the accumulation of a series of small steps.

The adoption of AI in our world will not be “an event”.

It will be a series of mundane changes made by companies that will change our future.

Next week will analyse why AI literacy matters for the future of our economies, societies and everyday lives.

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[**Side note: AI is an umbrella term for a group of interrelated technologies. The National AI Strategy published in September 2021 describes AI as "machines that perform tasks normally performed by human intelligence, especially when the machines learn from data how to do those tasks". In general, AI behaves in a way that mimics some human cognitive processes, using data to inform decisions. While the field of AI continues to develop rapidly, it has already become a part of our daily lives.]

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The journey to “The Athenian Intelligence”

Leaving the bubble

One year ago I made a life changing decision.

For 15 years I lived in a bubble. A wonderful bubble, full of amazing people, and I loved it. That bubble was the thought leadership team at Accenture. My colleagues and I could debate endlessly about trends, the hottest technological innovation, the latest economic policy, the most recent geopolitical development. My friends would talk about getting the Monday blues, not wanting to go to the office, or how dull their jobs are.

Not me!

I’d luckily found myself in that magic middle of the Venn diagram where the circles of “jobs you enjoy”, “jobs you get paid for” and “jobs you’re good at” all intersected.

But one year ago I decided to leave that bubble. I felt that there was more that I needed to try, more for me to learn about who I am, and more to discover on what I can offer to the world (the photo below is of me in front of The Academy in Athens, during my bubble phase).

Being wrong

Have you found that when you tell people that you want to start something new, everyone gives you advice? Start a podcast! Launch a newsletter! Write a book! All worthy suggestions, but none that I could take seriously without having found an idea that I passionately felt could anchor my thinking.

In March 2024 I founded a strategic foresight consultancy. I had lots of experience in using these tools to help the executive teams at large corporates to think about the future of their company and had enjoyed seeing the impact of my work—so it was a great place to start.

When you set up a business you realise you need to widen your network beyond familiar circles. That meant attending many different events.

I found myself in new settings. Lawyer roundtables. Regulation expert panels. Futurists breakfasts. Founders meet-ups.

I went from talking to execs at large corporates to directors of medium-sized businesses, or founders and entrepreneurs. From interacting with mostly consultants, researchers and technologists, to interacting with engineers, designers, creatives, policy experts.

I used to think that one of those connections might lead to a project.

But I was wrong.

Realizing the spark

One of my favourite poets, C.P. Cavafy wrote:

“When you set sail for Ithaca,
wish for the road to be long,
full of adventures, full of knowledge.”

So what I realize with the benefit of hindsight is that the value of all that networking lied elsewhere.

The value was in being exposed to such a large range of perspectives.

Every person I met was an opportunity to listen to a new viewpoint. Every project and each speaking engagement I researched was feeding me different angles. Every conversation I had was like little tetris blocks in my mind constructing a new idea.

The most interesting insights came from unexpected places. Drinks with friends, coffee with my mum, get-togethers with my neighbours. This topic would always come up when I mentioned I was researching technology trends: AI. But the questions that kept surfacing were of a very different tone from what I was used to.

Here are some questions I got that stuck in my mind:

• “What do you think? Will the robots take over my job?”

• “If AI is so powerful, why has it not already found the cure to all disease?”

• “I tried chatGPT once, it gave me a lot of incorrect answers and have not used it since.”

• “I really don’t see how AI can help my business, it seems like consulting b***s***.”

• “Why bother learning about AI, look what happened with the metaverse!”

People want to talk about AI—not in an abstract, technological sense, but in a deeply personal, “how will my day to day change” way.

I had assumed the message was already clear to everyone. At Accenture I worked on a report in 2016, “Why AI is the future of growth”, estimating the potential economic benefits for GDP and profitability, and since then AI has been a recurring topic in my work. But breaking out of my bubble showed me how much people are still trying to grasp it.

And that realization sparked an idea.

Finding the purpose

There is an emerging disconnect, a new digital divide, as a result of AI becoming more prevalent in our lives. That disconnect will become increasingly visible in all areas of our economy, society and lives.

The only way to prevent it is for every individual, every team, every leader to improve their own AI literacy. This is not just about not being left behind, but also about you being empowered to shape the technology’s future, and your own, through your actions.

“The Athenian Intelligence” is my effort to bridge that gap. It’s a play on my name, sure, but I also want to reflect the spirit of Athenian values, namely helping you to become better informed so that you can be empowered to engage in public debate. The Athenians saw this empowerment as a way to a stronger city. I see this newsletter as a space where we can do the same for our future.

Understanding the divide

You might question why I say there is an emerging disconnect, and it will be explored in detail in future posts, but let’s pause for a moment on that.

Economists consider AI a General Purpose Technology.

That means that its impact on the world is so profound that it will change all economic fundamentals and bring new waves of productivity and growth. Historically, the invention of the steam engine, electricity and the internet are all general purpose technologies. These innovations enabled us to exponentially improve our standards of living.

Yet they also create new “haves” and “have nots”.

Electricity has been around for more than 100 years but more than 700 million people still do not have access to it. The Internet has been around for more than 30 years and still 2.6 billion people globally do not have access. These inequalities have largely been across countries, creating divisions along geographic boundaries between the Global North and the Global South.

But the nature of AI means that the AI divide will be different and more pervasive—not based on geographic lines but based on a range of factors, such as access to computing power and technical expertise, and this will create a more complex divide, that exists predominantly within countries.

Extending the invitation

The main thread through all of those questions that I’ve been getting is a lack of confidence in being able to talk about the topic. The lack of confidence comes from a lack of AI literacy.

My goal for this newsletter is to be a space where you can come and build your literacy and confidence about the latest technology trends, and particularly Artificial Intelligence. Where we can question different aspects of the technology together and make AI accessible to more and more people.

Technology is not only for technologists. Economics is not just for economists.

In true Athenian spirit, knowledge is power.

The more we learn about those the fields that are reshaping the world around us, the more empowered we are to change our future through our actions.

My ambition is to help you boost your AI literacy and build that confidence. To explore thorny issues together, and questions that you might not feel comfortable to ask in a business setting. To make the complex simple.

If you are looking for the latest flops of compute power that LLMs are trained on, or “3 chatGPT prompts that will change your life” then this newsletter is likely not the best fit for you. Feel free to get in touch and I can recommend others that would be suitable. But if you’re interested in understanding the bigger picture, then welcome!

I invite you to subscribe and join me on this journey of empowerment.

I’ll be here once a week. In each weekly issue of The Athenian Intelligence, we'll dive deep into the latest AI trends, developments, and their implications. We'll work to make these complex topics accessible and empowering for readers of all backgrounds. I’ve decided for now to leave the newsletter free so that as many people engage with it as possible.

Please share with those that you think would value a little wisdom on the future. And don't hesitate to submit your own questions - dialogue is core to my mission.

*Sneak preview*: Next week’s article is about the “6 misconceptions of AI” and we will unpack them together, one at time.