Assumptions and limits
This model is deliberately narrow. It values time and direct cost only. It does not price quality improvement, customer satisfaction, risk reduction or revenue lift, because those need evidence from your own operation.
Adoption is the most common source of error. A system that works in a pilot rarely hits its full share on day one. For planning, assume a ramp and test the conservative case.
The model also ignores integration difficulty. Two systems with the same name can differ enormously in effort. List each integration and its owner before you commit budget.
Finally, the figures are not a quote. They are a planning tool. Use them to frame a conversation, then get a scoped proposal based on your actual systems and workflows.
Questions about AI Readiness Assessment
What does AI readiness actually measure?
It measures whether the conditions for a working AI project exist: clean data, accessible systems, governance, team skills and a clear workflow.
Readiness is not a general maturity score. It is a project-level question. A business can be ready for one workflow and completely unready for another.
Why weight data highest?
AI systems produce poor output from poor input, so data quality limits every downstream result.
If records are inconsistent or unavailable, fix that before investing in models or agents. It is the cheapest way to avoid a stalled project.
What if governance is weak?
Weak governance does not stop a pilot, but it does limit what can be deployed safely.
Start with read-only workflows, keep a human in the loop for decisions and log every action. Then build the governance layer in parallel with the pilot.
How is this different from a maturity model?
A maturity model describes an organization. A readiness assessment produces a project brief.
The output here is a first workflow with a named owner, an acceptance test and a clear boundary. That is what makes it actionable rather than descriptive.
What should I do with a low score?
Do not abandon AI. Pick one narrow workflow with high clarity and low risk, and use it to build the missing capability.
A low overall score often hides one strong dimension. Find that strength, start there, and let the project generate the evidence and momentum for the next one.