Beyond productivity: what should your business become because AI exists?

AI has made work faster: research, drafting, summarising, analysis, the everyday tasks that fill a calendar. That is real progress. But it mostly happens inside the business as it already is, and a faster version of today’s business is not the same as a better one.

This is the gap I see in almost every leadership conversation about AI. The next advantage won’t come from more personal productivity. It will come from redesigning the business itself: how it creates value for customers, how it scales and where its margin comes from.

Where AI use is today

Productivity is showing up. Commercial value mostly isn’t. UK government research across 3,500 businesses found that three in four AI adopters report higher productivity, but only one in eight report higher revenue.

Source: DSIT, AI Adoption Research

The same research found that the most commonly cited barrier isn’t cost or skills. For 71% of businesses, it’s a lack of identified need: they haven’t found a clear use case.

That tells us how most organisations have used AI so far: as tools for individuals. Chat and ideation. Summarising documents. Automating tasks. Useful, but personal. The benefit sits with the person using the tool, not in the way the business works.

The bigger challenge

The real risk isn’t falling behind on AI tools. It’s being outcompeted by a business designed differently.

In the UK, caution is understandable. Firms are moving at the speed their market asks for. But the UK isn’t the only market that matters. A 2025 global study by the University of Melbourne and KPMG found several major economies combining higher workplace use of AI with far higher public acceptance of it. Your competitors, suppliers and future customers increasingly operate there too.

A new entrant doesn’t have to preserve your products, processes, structures or economics. It can start with the customer’s problem and build around what AI now makes possible: a different customer experience, expertise embedded in systems, fewer hand-offs, lower marginal cost and new commercial models.

You have advantages it can’t easily copy: your customers, their trust, your expertise and your data. But they won’t be enough if the basis of competition changes faster than your business does. There is still time to respond from a position of strength, but that window is narrowing.

Stop asking “What is our AI strategy?”

When leadership teams sense this shift, the reflex is to ask one of two questions: what is our AI strategy, and where are our AI use cases?

Both start with the technology. Where can we use it? What can we automate? Which use cases should we prioritise? That keeps the thinking inside today’s business. It’s also why so many firms struggle to find a use case at all: they are looking for problems a technology might solve.

Steve Jobs put the principle simply in 1997:

“You’ve got to start with the customer experience and work backwards to the technology.”

Steve Jobs, Apple Worldwide Developers Conference, 1997

It’s business 101, yet with AI the rule seems to have been forgotten. AI strategy, technology choices and use cases should follow from the business you decide to build. They shouldn’t define it.

Think like a start-up

An AI-native start-up wouldn’t ask either question. It would ask: what problem are we solving for the customer, and what is the best business we could build to solve it now that AI exists?

That is the discipline leadership teams need to borrow. Good start-ups are obsessed with the customer’s problem: the job they are trying to get done, the friction in how they do it today and how they judge success. They design the proposition, the experience and the operating model around that job, with AI built in from the start rather than bolted on later.

Established businesses can do the same, but it means stepping outside today’s products, processes and constraints long enough to see the job clearly.

One way I have done this with leadership teams throughout my career is to ask them to design the competitor that could beat them. Then we bring their own business back in and compare: where the competitor would create more value, and where their existing advantages still win.

It’s an exercise straight from the consultant’s playbook, but a tried and tested one. Today it helps a leadership team see what an AI-native competitor would do to beat them, and moves the conversation beyond what AI means for personal productivity.

Outcome, capability, action

Once you know the customer’s job you are helping them get done, and the business you need to become, the route to the P&L runs in one direction:

1. Outcome. Start with the customer and business outcomes that must change, and the evidence that would show they have.
2. Capability. Identify the organisational capabilities that drive those outcomes, and which of them hold you back today.
3. Action. Choose the right mix of change across people, process, data, technology, AI and governance to build those capabilities.

Then measure whether the capability improved and whether the outcome followed.

Every AI idea has to earn its place in that chain: which outcome it serves, which capability it strengthens and how you would know it worked. Ideas that can’t make that connection may still be useful, but they aren’t strategic priorities. This is how AI moves from a productivity tool to real impact on revenue, margin and growth, and how the thinking moves beyond “I have a great idea” to something that makes a commercial difference.

Beyond personal productivity

AI compresses the distance between need, understanding, decision and action. That changes what customers expect, what employees can do and how quickly a business can sense and respond. I call the environment AI is creating around us one of Accelerated Experience, or AX.

Most businesses weren’t designed for it. Their structures, processes, roles and decision rights were built for slower loops between signal and action. When the environment changes and a business doesn’t adapt, there is only one outcome.

The step leadership teams now need to take is from individual use to enterprise impact:

That is what it takes to build the scalable business an AX world demands.

The question for your leadership team

The question is no longer “Can we use AI?” It is “What should this business become because AI exists?”

That is a business redesign question, not a technology one. Answering it starts with the customer, runs through outcomes and capabilities, and ends in action your team owns.