How do I measure AI readiness progress?

Cherry Seed

Measure two things: how deep your data goes and how capable your team is. Data depth is the months of clean, joinable history you have accumulated, the share of events you actually capture, and whether records stitch to real customers. Capability is how routinely your team asks questions of that data and acts on the answers. Gartner found only about 48% of AI projects reach production, and shallow or fragmented data is a recurring reason. Readiness is visible long before you deploy a model, in the quality and reach of the data you are already collecting.

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The mistake is treating AI readiness as a yes-or-no switch that flips when you buy a tool. It is better read as a gradient with two axes you can check at any time. The first is data depth: are you capturing 95% or more of your events server-side, do those events join to a stable customer identity, and how many months of consistent history sit in your warehouse. Thin or broken data caps everything downstream no matter how good the model is.

The second axis is organisational capability. Does the team query its own data without waiting on an analyst, does it interpret results sensibly, and does it close the loop by changing something based on what it learns. A store with two years of clean history and a team that runs weekly queries is far more ready than one that bought an AI product last week.

Gartner's finding that fewer than half of AI projects reach production points to the same gap: the work that determines success happens in the data and the people before the model arrives. Track depth and capability over time, and readiness stops being a guess. You will see it accumulating, quarter by quarter, in dashboards you can already build today.

Cherry Seed

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