Take every idea raised in an AI discovery programme, group them by theme, and plot two things per theme: how many ideas it attracted, and how ready the data behind it is. In every programme we have run the result is the same, and it is the single most useful chart a steering group can look at: appetite and readiness point in opposite directions.
What the chart shows
Market, competitor and supplier intelligence drew the largest field of ideas in our most recent full programme, raised independently by R&D, procurement, sales and marketing. It also sat furthest from ready, because there was no market-data subscription or structured process behind it, only public sources and individual effort. Forecasting, demand and inventory were similarly rich in ambition and light in ready data: the history existed, but across several systems with different conventions. Knowledge and documentation, the theme every function wants, depended on documentation that was sparse, scattered or out of date.
At the other end sat the themes the business could act on immediately: content and creative tooling, a productivity assistant, quality and test-data analysis, spend and purchase-order anomaly detection. Fewer ideas, less excitement, and the data already in hand.
Across 250 ideas, roughly a third were supported by data that was ready today.
Why it happens
Appetite follows pain. The themes that attract the most ideas are the ones where people feel the absence most: nobody knows what competitors are doing, nobody can forecast, nobody can find the document. But the reason nobody can do those things today is usually the same reason AI cannot do them tomorrow: the data was never collected, or was collected into silos. The pain and the data gap are the same fact seen from two sides.
Readiness follows systems. The themes that are ready are the ones where a system of record already exists and already holds the data: the ERP for spend, the test system for quality, the licensed productivity suite for drafting and summarising. They are less exciting precisely because the hard part, capturing the data, was done years ago for other reasons.
What a steering group should do with it
Fund the unglamorous work with the glamorous justification. Master data, traceability, a trusted knowledge foundation: these never win a vote on their own. They win when the chart shows that every high-appetite theme depends on them. The data-foundation work is the ground the ambitious use cases stand on, and the chart is the argument for starting it now.
Start where the data is, and say why. The ready-now themes deliver visible value in the first quarter, which keeps the programme credible while the foundations are built. The steering group should be able to explain to the people who raised the exciting ideas why theirs are second, and the chart does that in one picture.
Put the high-appetite themes on a pathway, not on hold. Market intelligence enters a product-led scan or a research step; forecasting enters a data-foundation step; knowledge enters a documentation audit. None of them is parked. Each is sequenced.
Revisit quarterly. Readiness moves. Data that was absent becomes in hand once the foundation lands; a theme that was strategic becomes nearly ready. The chart is a living view, not a verdict.
The uncomfortable conversation
The chart tells a leadership team that its instincts about where to start are usually wrong, and that the boring work is the important work. That is an easier message to deliver with the evidence on one page than in a paragraph, which is why we draw it in every programme and put it in front of the steering group before the roadmap.
The data positions behind the chart are explained in why data readiness decides your AI roadmap, and the audit that measures them is under data mining and requirements.