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A practical AI readiness assessment framework — how to score which processes are worth automating, estimate ROI, and decide build vs buy before committing to a project.
An AI readiness assessment framework exists to answer one question honestly: which of the processes on your list are actually worth automating right now, given the data you have and the accuracy bar the business needs — not which ones sound most impressive in a roadmap slide. That framework has three inputs: a map of candidate processes, an audit of the data each one depends on, and a defined threshold for what 'good enough to ship' means before any engineering starts.
Most assessments fail by skipping straight to the third input — picking a use case because a vendor demo made it look easy — without first mapping the data landscape underneath it. A process that looks automatable in a slide deck often depends on data scattered across three systems that have never been reconciled, which is a data-engineering problem wearing an AI costume.
An AI readiness assessment checklist that actually holds up covers four things per candidate process: data availability and quality, process volume and variability, the cost of a wrong answer, and a realistic ROI estimate once implementation and review cost are netted out — not the gross benefit a vendor pitch quotes.
How to conduct an AI readiness assessment properly means running a build vs buy vs wait analysis before any of that scoring is finalised: a category the business assumed needed a custom build is often already served by an existing vendor at a fraction of the engineering cost, and that has to surface during the assessment, not six months into a stalled internal build.
A ranked list of opportunities scored on realistic ROI, not technical novelty — the AI readiness assessment framework should produce a prioritised list a leadership team can act on directly.
An honest build-vs-buy-vs-wait recommendation attached to each opportunity, made before any engineering commitment, not as a retrospective explanation for why a build stalled.
A numeric, pre-agreed threshold for 'good enough to ship' — without it, a pilot has no way to know it succeeded, and no way to know it should stop.
For a single business unit with two or three candidate processes, two to four weeks is typical — most of that time goes into the data-quality audit, not the scoring itself. A multi-unit assessment covering a dozen candidate processes runs closer to six to eight weeks.
This page summarises an engineering approach for orientation purposes and reflects our understanding as of the review date above. AI tooling, model capabilities and best practices move quickly; validate specifics against current vendor documentation and your own environment before committing to an architecture.
This is the technology. See how we build it into something production-grade.
A proof of concept tests whether a model can do a task at all. An AI readiness assessment is a portfolio-level exercise that ranks multiple candidate processes against each other on data quality, ROI and risk, before any model gets built — it answers 'which one, if any, is worth a PoC,' which a PoC itself can't answer.
Yes, in a narrower form. Even with a use case chosen, the data-quality audit and the build-vs-buy-vs-wait check still need to happen before committing engineering time — skipping them is exactly the pattern behind most AI pilots that never reach production.