What an AI readiness assessment really tests
An AI readiness assessment establishes whether the organisation can deploy AI successfully, which is mostly a question about data, skills and process rather than technology. It covers data quality and accessibility, the state of the underlying processes, technical capability, governance, and whether anyone owns the outcome once a model is live.
Why so many AI pilots stall in practice
Rarely because the model does not work. More often because the data feeding it was never reliable enough to trust in production, the process it was meant to improve was undefined, or nobody was accountable for the output once the pilot team moved on. Assessing this first is considerably cheaper than discovering it in month nine.
How we prioritise AI use cases
Against two axes that matter: value to the business and feasibility given the data you actually hold. Most long lists collapse quickly under the second test. What survives is sequenced so the first deployment builds capability the second one needs, rather than being whichever idea had the loudest sponsor. Return is modelled on stated assumptions so it can be revisited rather than defended.
