Where are you on your AI journey?

Someone in marketing is using a generative tool to draft copy. IT might be piloting an AI-assisted helpdesk. Perhaps there's a data science project quietly running in the background. But activity isn't the same as strategy, and scattered pockets of enthusiasm rarely add up to a coherent, governed, value-generating approach.

This is where many businesses get stuck. They know AI matters. They may even be spending money on it. But they don't have a shared understanding across the business of:

• How mature their current AI use really is
• Where the governance, security and ethical gaps sit
• What ‘good’ looks like for an organisation of their size and sector
• What the sensible next step actually is, rather than the next shiny tool.

Without that clarity, investment tends to follow noise rather than need. Some businesses over-invest in flashy pilots that never scale. Others under-invest, hang back out of caution, and watch competitors quietly pull ahead on efficiency and customer experience.

Are you too close to the action?

It's tempting to think an organisation can simply judge this for itself. In practice, that's difficult for a few reasons.

• Bias from proximity: The people closest to AI initiatives, often the ones most excited about them, are usually the ones asked to assess progress.
• Fragmented visibility: Maturity spans awareness, governance, data readiness and how embedded AI is operationally, and few people hold visibility across all of it.
• No shared benchmark: Without an external reference point, 'good progress' is relative, ahead of last year doesn't mean ahead of where the sector expects you to be.
• Discomfort with the answer: Sometimes the honest answer is uncomfortable, for example that governance hasn't kept pace with experimentation, or that the business is further behind than leadership would like to admit internally. An independent, low-stakes way of surfacing this makes it much easier to act on.

A simple way to frame it

We think about AI maturity as a journey through three broad stages:

• Explore: building awareness, running early experiments, and starting to ask the right questions, but AI isn't yet governed or embedded in a structured way.
• Establish: AI use is becoming more deliberate. Governance, data foundations and a few core use cases are in place, and the organisation is starting to scale what works.
• Excel: AI is embedded into strategy and operations, governed properly, and delivering measurable value, with the organisation continuously refining its approach.

Neither of these stages is inherently ‘wrong’ to be at. A business genuinely early in its journey shouldn't be trying to leapfrog to advanced use cases before the fundamentals are in place. What matters is knowing, honestly, where you sit, and understanding what a sensible next step looks like from there.

Why it's worth getting an outside view

This is exactly why we built the AI maturity check: a quick, practical way for organisations to work out where they genuinely are on the Explore, Establish, Excel journey, rather than relying on a gut feel that might be shaped by whoever’s in the room.

It takes about two minutes. Seven quick questions cover how your organisation understands, governs and uses AI today. At the end, you'll see how your answers map across the journey, along with practical, prioritised next steps for wherever you land. Not a generic list, but recommendations suited to your actual stage.

Understanding where you stand is the easiest first step to making sure your AI investment goes somewhere useful.

Take the AI maturity check: 7 questions, and takes about 2 minutes.