AI isn’t just changing how organisations work. It’s exposing what they believe about value.
A few weeks ago, I came across a thought provoking video from brand strategist Eugene Healey and brand thinker Jasmine Bina that got me thinking, exactly the intent of the video. You can watch it here.
Healey’s argument was sharp and, once you hear it, very hard to unhear. He said that what most brands really need is a complete audit of the ontological ground they stand on, the foundational beliefs that a brand requires its audience to hold in order for any of it to make sense.
I kept thinking about it. Not just in relation to brands, but in relation to organisations as a whole. Because what Healey is really describing isn’t just a branding problem. It’s a belief problem. And AI, more than any technology before it, is exposing how many of the beliefs organisations are built on have quietly stopped being true.
Let me explain
Every organisation is built on assumptions about how the world works. Not strategy. Not tactics. Deeper than that. Assumptions about where value comes from, what expertise means, who holds knowledge, and why people need you.
For most of the last century, those assumptions were stable enough that nobody thought to question them. Information was scarce. Expertise was rare. Content was expensive to produce. Authority came from knowing more than the people around you.
Generative AI has disrupted all of that, not gradually, but all at once. And organisations that haven’t noticed are still making decisions based on a version of reality that no longer exists.
To make sense of this, I did what every good non-intellectual would do and asked the smarter people around me to share their insights. And here’s what we uncovered together.
The expertise illusion
The most fundamental belief to break is one that most organisations have never had to question: that expertise comes from holding more information than everyone else.
As Dr Lollie Mancey puts it:
“One belief that is breaking is that expertise comes from holding more information than everyone else. For years organisations created value by controlling knowledge and hiring the smartest analysts in the room. Generative AI changes that because information and pattern recognition are now widely accessible. The advantage now comes from judgement: asking better questions, understanding context, and making thoughtful decisions about how knowledge is used.”
This isn’t just a shift in skills. It’s a shift in what organisations fundamentally are.
“Organisations are no longer just processors of knowledge, they are meaning-making systems. In a world where intelligence is abundant, advantage comes from context. That includes understanding customers, culture and what is really happening on the ground. Expertise becomes the ability to hold that context, apply it with nuance and take responsibility for what happens next.”
The organisations that understood this early have stopped hiring for knowledge and started hiring for judgement. The ones that haven’t are still measuring the wrong thing.
The democratisation of everything
If Dr Lollie’s argument changes what expertise means, Hugo MC Pinto‘s changes what organisations are for.
“If the internet democratised information, GenAI democratised the creation of capital.”
That single line is worth sitting with. Because if the creation of value – real, economic value – is no longer confined to the people, teams, or institutions that historically controlled it, then organisations built on that control face something more serious than a strategic challenge. They face an identity crisis.
Pinto goes further. He argues that organisations with solid data foundations will find that every member of staff becomes a potential entrepreneur, a transformation consultant.
The barriers that once protected established players; proprietary systems, institutional knowledge, access to tools are crumbling. And the organisations that will benefit most are the ones that have invested in the infrastructure to let that happen: clean data, accessible systems, and people who know how to use them. (see previous article for more)
The implication is uncomfortable. It isn’t enough to adopt AI tools. If your organisation is still structured around controlling access to value, the tools will simply make that structure more visible and more fragile.
What’s actually happening inside organisations?
Mike Weston has worked with enough organisations to understand the gap between how leaders talk about AI and how their businesses actually behave. And what he sees, consistently, are three outdated assumptions still quietly running the show.
The first assumption: “Value comes from what we know.” Most businesses still behave as though their competitive advantage sits in accumulated knowledge; proprietary data, institutional expertise, hard-won process know-how. But as Weston puts it,
“AI is compressing the gap between knowing and doing so fast that knowledge alone isn’t a moat any more. The organisations that are pulling ahead are the ones asking: if everyone can access roughly the same intelligence, what’s actually ours?”
The second assumption is that efficiency is the goal. “The default instinct is to point AI at cost: automate this, speed that up, reduce headcount. But efficiency only matters if you’re doing the right things in the first place. AI is an indiscriminate amplifier, it doesn’t care what it makes louder. If your operating model has structural drag in it, AI just scales that drag faster.”
The third is perhaps the most seductive: that the human layer is the creative layer. The narrative that AI handles the mundane and humans handle the creative, strategic, relational work. “That’s already breaking down,” says Weston. “What I’m seeing is that the genuinely valuable human contribution isn’t creativity in the abstract, it’s judgement. Specifically, the ability to decide what matters, what to prioritise, and what to stop doing.”
And then the thread that ties all three together: “Most organisations are still treating AI as a tool question — ‘what can it do for us?’ — when it’s actually an identity question: ‘what are we, if not the things AI can now do?'”
What hasn’t changed?
Dave Birss cuts through the noise in the way he always does, by refocusing back at people.
“It’s easy to get lost in the tech-bro hype cycle. The amount of news coming out of the AI world can be dizzying and can lead to business leaders being frozen in the headlights, feeling as if they’re being left behind. But they’re missing the point.”
His argument is both simple and quietly radical: humans haven’t changed much in 40,000 years. Our brains operate at the same speed, have the same memory capacity and carry many of the same limitations. The opportunity isn’t to use humans to oversee AI. It’s to use AI to amplify what humans are actually good at.
“Using AI to amplify human capabilities is better than using humans to oversee AI capabilities.”
That reframing matters. Because the organisations that are struggling most with AI aren’t necessarily the ones with the worst technology. They’re the ones that have forgotten to ask what their people are actually for.
The cost of getting this wrong
The previous sections discuss the strategic problems, now Toju Duke reminds us that it’s also a risk problem, and the consequences are measurable.
“Organisations need to be nimble and agile, adapting to changing consumer preferences and new technological demands. Most change management processes across businesses are outdated and unsuitable for the new digital age. Failure to observe changing customer behaviours and adapt quickly will result in major challenges, including loss of market share, customer loyalty, customer trust, investor confidence, and revenue and profitability.”
This is the part that tends to land differently on senior leaders. It’s one thing to argue that expertise is changing, or that value creation is being democratised. It’s another to say: if your organisation continues to operate on beliefs that no longer hold, the financial and reputational consequences are real, documented, and already happening to companies around you.
Governance and ethics aren’t constraints on AI adoption. They’re the scaffolding that makes it sustainable. And organisations that treat them as optional are, as Toju’s work consistently shows, building on ground that won’t hold.
The question worth asking
This isn’t an article about what AI can do. There are plenty of those. This is an article about something deeper: the beliefs that underpin how organisations create value, earn trust, and justify their own existence. Most of those beliefs were formed long before generative AI existed. Some of them were never written down. And many of them, right now, are quietly unravelling.
Eugene Healey’s provocation stays with me because it doesn’t ask you to adopt a new strategy. It asks you to audit the ground you’re standing on.
So here is the question worth taking into your next leadership conversation:
What has to be true for your organisation to make sense? And is it still true in the age of AI?
Helena McAleer is the co-founder of TheGenAIAcademy.com . She connects organisations implementing AI with real-world experts who know how to deliver results the right way – and yes, she still uses the em dash!
Typically I give a list of resources, further reading, courses and workshops to do, but this article is designed more to get you thinking about how AI could shape the future of brands, so for this one, I will leave you with your thoughts and no homework.
