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AI readiness self-assessment

Twelve questions across workflows, data, systems, governance and people. See your readiness by area and what to work on first.

Workflow and value

We can name one recurring workflow that costs real time or causes errors.

We know roughly how often it happens and how long it takes.

We agree what a better result would look like and how we would measure it.

Data

The information the workflow needs is in systems or files we can access.

That information is reasonably consistent and up to date.

Systems and integration

We know which tools the work passes through (ERP, CRM, email, files).

Those tools can be connected through APIs, integrations or exports.

Security and governance

We have rules for which company data can be used with AI tools.

We know which decisions must stay with a person.

People and ownership

One person is accountable for the workflow and its results.

The people who do the work are willing to test and give feedback.

Leadership will give the project time and a budget if the case is clear.

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How the numbers work

  • Each answer scores from 0 (not yet) to 3 (yes). An area score is its total as a share of the maximum.
  • The overall score weights every question equally. 70% and above suggests a focused first project; below 40% suggests starting with an assessment.
  • The questions follow the checks a good AI readiness assessment makes: a named workflow, reachable data, connectable systems, agreed guardrails and an accountable owner.
Read the full guide

Go deeper

Guide · 9 min read

What happens in an AI readiness assessmentAn AI readiness assessment is a structured review of specific workflows, the data and systems behind them and the people who run them, to decide where AI or automation will help, what must be fixed first and in what order to act. A good one ends with named, prioritized opportunities and a roadmap, not a maturity score or a deck of industry trends.Read

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