"The story of the human race is the story of men and women selling themselves short."
— Abraham Maslow
Maslow's hierarchy wasn't really a theory about human needs. It was a theory about realising our potential. It asked a simple question: what becomes possible when constraints are progressively removed?
Applying that to organisations, I think we have an opportunity to make AI a story about removing the constraints that cause us to sell ourselves short and unlock what wasn't possible before.
Most conversations about AI focus on productivity. How much faster can we write documents? Summarise meetings? Analyse data? Create presentations? Generate code? These are all worthwhile questions, and the productivity gains are real. But the more I observe teams using AI, the more I think we're focusing on the most obvious benefit rather than the most important one.
The real value of AI isn't that it helps people work faster. It's that it unlocks potential that wasn't previously reachable — in individuals, in teams, and eventually in the organisation itself.
A few years ago, if a Product Manager wanted to turn an idea into a working prototype, they would usually need a designer and an engineer. If a marketer wanted a dashboard, they often needed help from an analytics team. Today, those same people can often solve these problems themselves. They haven't suddenly become engineers, designers or analysts. But they have become capable of doing things that previously sat outside their reach. That's not a productivity story. That's a potential story.
I've started thinking about AI adoption through the lens of Maslow's hierarchy of needs. Not because the analogy is perfect, but because it captures something important: each stage creates the conditions for the next. You don't move directly from using AI to summarise meetings to transforming the way your organisation operates. You climb. And the view from the top looks nothing like the base.
The foundation of the pyramid is productivity. This is where most organisations begin, and for many it's where most of the conversation remains today. People use AI to accelerate tasks they already know how to perform. Product Managers synthesise customer interviews more quickly. Engineers generate code faster. Analysts accelerate research and reporting. Marketers create content in a fraction of the time.
The nature of the work remains largely unchanged. AI simply reduces the effort required to complete it. This creates value — but it's important to recognise what kind of value it is. The person is doing the same work as before. They're just doing it faster. Productivity is the entry point, not the destination.
The second stage is where things become more interesting. At this level, AI allows people to do things they previously depended on specialists to do for them. A Product Manager creates a prototype instead of waiting for engineering capacity. A marketer builds a dashboard instead of relying on an analytics team. A finance analyst automates recurring processes that previously required technical support.
This is fundamentally different from productivity. The person is no longer simply doing existing work more efficiently. Their range of capability has expanded. Problems that were previously outside their reach are now within it. Potential that existed but couldn't be expressed — because of skill constraints, resource constraints, or dependency on others — is now reachable. When people talk about AI as a force multiplier, this is what they mean.
As capability expands across individuals, it begins to change the behaviour of teams — and not just product teams. This is where the shift becomes visible across the business. A marketing team builds its own dashboard instead of raising a request to analytics. A finance team automates its own reporting instead of waiting for engineering support. An operations team designs and deploys a workflow tool instead of managing the process through spreadsheets and email chains.
The dependency that used to flow toward product and engineering teams begins to reduce. Business teams become increasingly self-sufficient — not because they have hired technical specialists, but because the capability to solve their own problems is now within reach of the people who understand those problems best.
This matters for more than efficiency. When the people closest to a problem can also act on it, the quality of the solution tends to improve. Context doesn't get lost in translation. Iteration happens faster. The question shifts from "Can we get this on the roadmap?" to "Can we solve this ourselves?" That shift — quiet as it might seem — is the beginning of a different kind of organisation.
This is the level most organisations haven't reached yet — and the one I think will matter most. At some point, execution ceases to be the primary constraint. The ability to create solutions becomes increasingly accessible. What remains difficult — and increasingly differentiating — is knowing what to build in the first place.
A new question starts to surface across the organisation: just because we can, should we? When execution is difficult and expensive, that question rarely gets asked because the constraint answers it for you. As AI lowers the cost of execution, the constraint disappears. What replaces it is judgement.
The ability to identify worthwhile problems, validate assumptions, learn from users and focus effort where it creates value becomes increasingly important. Not everyone becomes a Product Manager. But more people across the organisation begin reasoning in terms of problems, outcomes, experiments and evidence rather than tasks, outputs and delivery. This is potential operating at its most powerful — not just individual skills being unlocked, but collective intelligence being sharpened.
The final stage is where the organisation itself begins to change. Capability is now distributed. Teams solve many of their own problems. Product thinking exists beyond product teams. Decision-making happens closer to where information and context actually live. The organisation is no longer structured around managing dependencies — it is structured around pursuing outcomes.
Dependencies shrink. Feedback loops shorten. Learning accelerates. The organisation doesn't just move faster — it becomes more intelligent over time, because more of its people are closer to the problems, generating insights, and acting on them without the friction that previously slowed everything down. AI is no longer a tool being used by individuals. It has become part of the operating model.
Most organisations are still at the base of the pyramid. That's understandable. Productivity gains are immediate, visible and easy to measure. They show up in reports, justify budgets and are a reasonable place to start.
But if AI continues on its current trajectory, productivity may ultimately be remembered as its smallest benefit. The organisations that merely adopt AI will work faster. The organisations that are transformed by AI will unlock potential that previously couldn't be expressed — first in individuals, then in teams, and eventually across the organisation itself.
Team potential, compounded.
Organisational potential, realised.
Viewed through that lens, AI maturity isn't really a story about technology. It's a story about unlocking potential — first in individuals, then in teams, and ultimately in organisations becoming what they are capable of becoming.
