Someone out there is trying to eat your lunch.

PLUS: why three of five AI investments get more valuable with age, not less.

From the aibl team

In our lead this week, we spent some time with Informa’s Group Chief Commercial AI Officer Amin Mrini, who spends much of his time on how workflows should be redesigned around AI. He observed how many companies are starting with what seems easy, focusing on efficiency at the expense of more impactful redesigns of how they do business.

Of course we’ve seen the same thing. But recently we’ve also seen a new wrinkle in the cycle. Some companies are taking a step back because they don’t want to misstep.

It’s not something AI leaders are vocal about. But we’ve had enough conversations about the phases of AI development in mid-market companies to have noticed a retrenchment.

For some companies, roughly 45% based on our research, their first real attempt at AI was a failure. That’s oversimplifying, since nearly all were upskilled and learned valuable lessons in the process. But in terms of their financial KPIs, the ROI wasn’t there. This has led to a difficult political problem. The demand for results is still there, but the initial experience leaves AI-focused leaders on the back foot.

The response from some has been to go smaller, focusing on efficiency plays and measuring indirect metrics like AI usage by employees and time saved on specific tasks.

While it’s easy to sympathise with these businesses, we firmly believe this is the wrong approach at just the wrong time.

Efficiency and productivity gains are positive effects, but they’re not central to how AI can and should be affecting the organisation.

It’s something Amin speaks about eloquently. In our research section this week, we distil some of the ways to think about AI investment, most of which go to a single thesis: AI will inevitably change your market and your model, so it’s incumbent on you to learn, prepare, and act.

AI’s Bigger Prize Is Growth, Not Cost-Cutting

Amin Mrini is Group Chief Commercial AI Officer at Informa, the B2B information services group. He spent close to a decade in strategy, starting in management consulting, before moving into product and digital leadership. Half his job, by his own account, is redesigning workflows around AI.

Most companies are pointing AI at what they already do, in the hope of doing it cheaper. “I think this idea of doing things for cheaper is incredibly short-termist and mono-dimensional as a way of trying to unlock AI value.”

He allows that most will pass through a cost phase first, because saving is the easier thing to measure. In his formulation, a cost saving is banked once, a new product compounds. He wants the productivity equation turned around. Revenue or ARR per head, rather than the number of heads needed to produce each dollar of profit.

“Being protective and thinking about costs only is a defence strategy that will ultimately affect your market share and your growth potential long term. Someone out there is trying to eat your lunch and is thinking new products, new services, new demand, new ways of distributing content, information, products, you name it.”

It matches what aibl sees across mid-market organisations. The firms making progress aren’t the ones with the largest AI budgets. They’re the ones asking what each person can now produce.

Watch the full interview:

aibl Research: Why AI Investments Compound

One of the more insightful pieces I’ve read on AI strategy recently is from Baba Prasad, a professor of leadership at Brown University’s School of Professional Studies in the US. In a piece for HBR he identifies five types of AI investment, with an analysis of their financial measures and impact.

I won’t parrot Professor Prasad’s findings, which you should read in full, but I do want to share the main insight I gained: that three of his five types of AI approaches result in compounding assets, not depreciating investments.

Conventional enterprise software is worth the most on the day it goes live and declines from there. But what we can build with AI (data flywheels, deep integration into proprietary workflows, and organisational capability) all run the other way. They’re worth more after two years of use than after two months, because the accumulating asset is proprietary data, embedded processes, and human fluency rather than the tool itself.

Product spotlight of the week

This week the aibl team has been tracking MindGym, a behavioural science firm that has been adding AI tooling to its manager development work. Their pitch is that training stalls between the workshop and the moment it was meant to prepare you for.

Lio is an AI conversation coach that runs alongside five live sessions covering feedback, accountability, and career conversations. It stays available between them, with nudges after each session.

Their Memberships subscription gives on-demand access to 100+ workshops, delivered by MindGym facilitators or by yours once trained. Pricing is tailored to the organisation, so aibl readers wanting a number will need to go direct.

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