Your AI works. Your business doesn’t.

PLUS: 91% confident, 10% scaled. Two surveys, one uncomfortable gap.

From the aibl team

Every leader I speak to can tell you what they want AI to do. Fewer can break down how their function actually works today: who makes which decisions, what data feeds them, and where it comes from. Before she’ll let a team near a tool, Anca Pintilie asks them to describe how work actually happens in their function. Most can’t. The business has reorganised since anyone last checked, and no chart has caught up.

Her line has stuck with me: “The AI is almost never the thing that’s broken.” At Amazon, she spent months mapping six teams before anyone touched a tool, and found 300 to 400 people running 13 different operating models that nobody had noticed. Each one had just drifted to fit whatever the business needed at the time.

We also dig into two surveys this week: one covering UK and Irish mid-market firms, the other the US and Canadian middle market. Klarus found 91% confident in their internal AI expertise, yet only 10% had scaled all their initiatives beyond the pilot stage. RSM found 97% satisfied with the value AI is delivering, yet only 36% had fully embedded it across core processes.

Confidence and satisfaction are running well ahead of capability. In the UK and Ireland, firms blame stalled projects on gaps in expertise and governance. In the US and Canada, budgets are being cut in many of the same areas needed to close that gap.

Anca’s interview and the full survey breakdown are below.

The AI is almost never the thing that’s broken

Anca Pintilie spent six years running marketing and brand for one of Amazon’s largest operations in EMEA, most recently leading the effort to bring six regional teams onto one operating model. She’d previously worked at Revolut, Oracle, and in agency roles.

Whatever hasn’t been dealt with falls out of the bag.

Ask Anca what she checks before a team buys AI, and she doesn’t start with the tool. “Are they able to describe how work actually happens within their function?” That means understanding how decisions get made, which ones trigger spend, what data feeds them, where that data comes from, and whether it all has “the same refresh rate.”

Businesses reorganise so often, she says, that any chart is obsolete before people settle into their new roles. Unless the operating model has been mapped in the past month, whatever’s on the slide is fiction, tidied up for management.

She reaches for the TikTok bag-check videos to describe what happens next: someone empties their bag on camera, and out comes whatever they haven’t dealt with in three weeks. Buy or build the AI before doing the work of finding out, and that’s what gets automated, in public, at scale.

Watch the full interview:

aibl Research: Confidence Isn’t a Strategy

Two surveys came out this month: one on UK and Irish mid-market firms, the other on the US and Canadian middle market. Different sides of the Atlantic. The numbers come out almost the same.

The UK numbers, from Klarus: nearly three-quarters of mid-market firms (73%) have partially or fully deployed AI, and 91% say they’re confident in their own internal expertise across the board. Yet only 10% have successfully scaled all their initiatives beyond the pilot stage, leaving 90% with at least some projects still stuck in what Klarus calls pilot purgatory.

Ask people why projects stall and the top two answers, tied at 48% each, are lack of AI expertise and concerns over governance, ethics, security, and privacy. The same areas they had just said they felt confident about.

Product spotlight of the week

This week the aibl team has been tracking Kantar Marketplace, an automated market research platform built for marketers who want decision-quality feedback in hours rather than the usual multi-week agency turnaround.

Its solutions run on Kantar’s Meaningful, Different, and Salient framework, aimed at linking creative and product choices to actual sales and brand impact. From one platform, teams can test ads and concepts, screen ideas, evaluate packaging, track brand health, or optimise media mix, drawing on access to over 150 million consumers across 80-plus countries.

Every project can run self-serve or with Kantar’s own expert service layered in, and teams can pay per project or commit to volume upfront. Pricing isn’t published either way, so the first step is a conversation with Kantar’s team rather than a straight sign-up.

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