Digital Economy Dispatch #297 -- What is AI For?

Something shifted over the summer. In conversation after conversation, with boards, digital leaders, and public sector teams, I’ve noticed the questions getting harder and considerably more useful. Less "which AI tools and models should we be using" and more "where is the value for our AI investment coming from, and how would we know". After several years of experimentation, budgets are being examined properly for the first time, and a plain question is being asked out loud: what should we use AI for?

It’s the right question. However, many of the leaders asking it are starting from perspectives that were set for them by someone else (such as tool vendors, external commentators, and online influencers) and they are determining what counts as a good answer.

Two metaphors dominate current views of where AI’s value lies. The first is AI as a telescope, bringing the future into view: market movements, demand signals, and user scenarios previously too faint or too far to resolve, can now be more reliably observed. The second is AI as a microscope: deeper insight into today's problems to improve decision making and operational practices by revealing the fine structure of processes, analysing behaviours, and connecting siloed data.

Both are reasonable. Both are being oversold. And I have started to think the largest opportunity sits somewhere neither of them points, in a third framing that nobody is selling because it cannot be so easily packaged: AI as a mirror, showing an organisation to itself in a light it has not previously had.

The benefits of better optics

Let me be fair to the optics before taking them apart, because organisations clearly do suffer from limited reach and limited resolution, and better instruments are worth having.

The trouble is what the optical framing conceals. A telescope and a microscope both assume a competent observer who simply lacks equipment. The organisation is treated as a fixed, functioning thing that needs better optics bolted on. Buy the instrument, see more, decide better. Nothing about the organisation itself has to change.

That assumption deserves more scrutiny than it usually gets, and each instrument breaks in a way that tells you something important.

Telescopes show you old light. What reaches the lens left its source long ago, and everything forward-looking is inference stacked on top of a historical record. Models trained on the past are, quite literally, instruments for observing what has already happened at a considerable distance. That is useful. It is not prediction, whatever we’d all like to believe. Telescopes also look through atmosphere, and in this analogy the atmosphere is your own technical debt and dysfunctional data estate, distorting the signal before it ever reaches the optics.

Microscopes fail more subtly. They magnify the sample you have selected and prepared. Yet, process mining reveals what your systems logged, not what your people did. The gap between those two is where most of the interesting behaviour lives, and no amount of additional resolution will surface something that was never captured in the first place.

The instrument you never ordered

But a third framing offers a very different perspective: AI as a mirror.

The telescope and the microscope are instruments you choose to deploy. The mirror is a by-product you can’t avoid. Every serious AI deployment runs an unintended audit of how well an organisation understands itself, and the results arrive whether or not anyone commissioned them. Most organisations receiving those results read them as a historical technology effect and file them away.

Where "AI as a mirror" is considered, it often takes far too narrow a view, as an argument about societal bias or lack of diversity in data collection. Such important arguments have been made well by others, from Buolamwini and Gebru's audit of commercial facial analysis systems to Kate Crawford's Atlas of AI, and I am not repeating them here. What interests me is broader and, for anyone running an organisation, far more actionable: what AI reveals about institutional self-knowledge.

Start with specification. The inability to write a usable requirement is routinely diagnosed as a procurement skills gap. It is rarely that. It is the discovery that nobody ever had to articulate what a given process was actually for, because it was held together by tacit knowledge and long-serving staff. Ask a machine to do it, and the vagueness becomes visible immediately. The requirements document turns into an accidental self-portrait, and it is not usually a flattering one.

Then data readiness. "Our data isn't ready" gets reported upward as a technical finding. It is nothing of the sort. It is a decade of deferred ownership decisions surfacing at once, under a deadline, in front of a vendor. The data was never the problem. The absence of anyone accountable for it was.

Consider also what stalls pilots. In my experience, they stop scaling less often because the technology underperformed than because nobody can name who owns the decision to proceed. The NAO's 2024 review of AI in government put the same point in more measured language, warning of risks to value for money where government had not established which department held overall ownership and accountability for delivering AI adoption, nor set out who was responsible for contributing to it. The pilot holds a mirror up to the governance structure and the reflection comes back blank. That is a finding about the organisation, not about the model.

Which brings us back to where we started. Ask a leadership team what should be automated with AI and too often there is little response. That pause is not caution. It is the absence of any shared account of what the work is for. An organisation that cannot answer a question about what this work is for has no way of answering what is AI for, and no instrument will supply the missing answer.

Consider, for example, Klarna's much-discussed AI reversal. Having announced in February 2024 that its OpenAI-powered assistant was handling two-thirds of customer service chats and doing the equivalent work of 700 full-time agents, the company changed course by May 2025 and began recruiting human agents again. It reads differently through the AI mirror lens. Less a misjudgement about technology, more a company discovering it had never properly articulated what its service did for customers in the first place.

The face in the mirror

To be fair, not every disappointment with AI use is a process issue. Plenty of AI projects fail for straightforward technical reasons, and an AI mirror lens that explains everything risks explaining away real limitations in the tools.

However, the organisations getting the most out of AI do one thing differently at the outset. They treat early AI work as explicitly diagnostic rather than as deployment, and they say so in the business case for why they invested in it.

That sounds like semantics. It is not, because it changes what counts as success. If the stated deliverable of the first project is a map of where specification, ownership, and decision rights were missing, then an AI pilot that fails to scale has still returned its investment. At the very least, the organisation has bought a survey of its own foundations at a fraction of what a transformation programme would charge for the same information, and considerably more honestly.

The alternative is what I see most organisations are doing now: running the diagnostic accidentally, reading the result as technology failure, and commissioning yet another AI pilot to prove the first one wrong.

So, next time the board asks what AI is for, there is a better answer available: to tell us more about ourselves. Three questions will test whether your organisation is ready to give it. Could you write a requirements document for your most important process without asking anyone who currently carries it out? Can you name the person who owns the decision to scale your most advanced AI pilot? And when your last AI project failed to deliver the value you’d hoped, did anyone ask what it had just revealed, rather than only what had gone wrong?

The lens through which to view your AI adoption was always going to show you something. The only real choice is whether you want to look.