Your AI strategy is already public. Whether you meant it to be or not.

Every boardroom in the FTSE 350 is asking the same question: “How mature is our approach to AI?” The better question to ask is “Who can see how mature we actually are?”.

The public answer to your organisational AI maturity is already out in the open. Annual reports, job listings, press releases, the technology running your channels, and more are all sharing signals of your AI maturity. 

And anyone prepared to do the deep dive into it can make their own assessment of where you are at. So we did. 

That’s the AI.M.E, our AI Maturity Evaluation Index: all 350 companies, judged on what the evidence shows rather than what they might say.

Why maturity matters

AI isn’t just changing what companies do. It’s changing what it means to compete at all. The businesses that come out ahead won’t be the ones with the most pilots or the biggest GenAI budget. They’ll be the ones who rebuilt their strategy, their data, their products, their content, and their people around a genuinely different mode of operating – what we call Organisational Intelligence. That’s why AI maturity matters. It isn’t a technology score. It’s a measure of relevance – how close you are to the kind of organisation that thrives in what comes next and where you sit today against the market. 

A maturity score built on evidence, not just the story

We score AI maturity as a single organisational capability built from five pillars. Strategy, Data & Insight, Product & Service, Content, and People, all combined into one single number: 

1. Strategy is about whether AI genuinely shapes decisions and budgets, not just something mentioned in a slide deck.
2. Data & Insight looks at how clean, connected and trustworthy the underlying data really is.
3. Content asks whether the knowledge behind it is structured enough for a system to use, not just written down somewhere.
4. Product & Service checks whether AI is actually built into what’s sold, rather than bolted on afterwards.
5. People comes down to whether staff are properly trained and confidently using AI.

A good overall score means there is disclosed capital committed to AI, AI tools and systems live, with governance properly considered. NatWest’s public £1.2bn commitment to technology, data and AI, sitting alongside published responsible-AI principles, is what that looks like in practice. 

A bad score doesn’t mean a company has no maturity; it means it has less visible evidence of maturity than we could find. For example, one low-scoring company in the FTSE 350 doesn’t mention AI anywhere in its annual report, and we’ve seen nothing else with that company to justify otherwise.

The average A.I.M.E. score of 41 out of 100 feels right to us: it represents a market still moving from experimenting with automation to genuinely adopting AI into its core strategy and operations. And the leading companies are pulling clear with demonstrable proof of AI maturity rather than lofty ambition statements, with only five points separating first and tenth. 

Almost nobody is bluffing. 

We went in expecting to find companies claiming more AI progress than they’d actually delivered. 

But across the FTSE350, the gap between what a company says about AI and what we evidenced is close to zero. We saw almost no bold claims without anything to back it up. And those that scored lower in our index with less evidence of a commitment to AI also resisted the temptation to tell a misleading story about their current state.

The biggest issue we saw was almost a hesitancy to share and celebrate where successes are. For example, Bunzl and DCC both operate genuine internal AI programmes, board-level governance, working models in production, that go almost unmentioned on earnings calls or in investor disclosure.

We also saw scores between each pillar vary significantly. This indicated to us that organisations might be concentrating on a few specific areas as they learn and adapt on their journey to becoming more mature in their approach to AI.

Governance is arriving after the systems it exists to govern

We found several examples of companies running AI systems on safety-or trust-critical use cases, but without corresponding AI policies regarding risk and no Board-level ownership of AI. 

While we believe it’s important for clients to pilot, test and learn in the new AI world and that this needs to be supported by appropriate governance practices. AI systems are already in organisations; the governance is arriving late, shaped by what has been done rather than a considered assessment of what the organisation needs.

Augmenting people with AI 

Where companies disclose numbers on people, the picture currently reads as augmentation rather than replacement. For example, half of NatWest’s 12.9 million assistant conversations resolve through AI agents, routing the harder cases towards staff rather than away from them. 

Companies have strong reputational reasons not to disclose AI-related job cuts, so we believe this pillar may understate the talent displacement that may come. However, the wider shape of the People pillar fits: investment in AI skills and development tracks to overall higher maturity scores, but the whole pillar sits far lower than others. Even the strongest 10% only hits a score of 55/100.

Data and Product don’t lie

The Data and Product pillars are the best predictors of where a company’s AI maturity really sits; tracking overall maturity more closely than other pillars. 

Shell processing roughly 20 billion sensor readings a week through around 11,000 models is a clear picture of AI being integral to enhancing a service. Admiral turns driving telematics into a £100m-a-year underwriting gain with fewer mispriced and fraudulent payouts. RHI Magnesita’s lakehouse feeds models that forecast refractory wear six months out at better than 80% accuracy. 

Product has the widest spread of any pillar, with the score ranging from 86 down to five, with wild variance between Product scores and other pillar scores. Oxford Nanopore is a great example, scoring 80 on the Product pillar, but only ranking 28th overall. 

The truth is out there

Almost every company in the FTSE 350 is doing something with AI – from experimentation to full integration. We have taken the time to define the component parts of AI maturity and scored each company in the FTSE 350 here. 

The proof points of your current AI maturity are already public. But whether scoring high or low, each company is on its own unique journey. Hopefully, our AI.M.E helps you to define where to go next to realise maximum value from the AI opportunity.