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Assessing readiness for AI
AI readiness assessment
An AI readiness assessment checks whether an organisation can support a proposed AI workflow before money is spent building it.
predeploy assesses readiness against a specific use case, not in the abstract, so the result is a clear list of what is ready, what is missing and what to fix first.
Get a readiness verdict for a named use case, with gaps ranked by how much they block progress.
NIST describes its AI risk framework as voluntary guidance for design, use and evaluation. NIST: AI Risk Management Framework ↗
ICO guidance covers AI accountability, transparency and fairness; the guidance is currently under review. ICO: AI and data protection guidance ↗
New to the topic? Read the background on artificial intelligence.
Assessing readiness for AI
what does an AI readiness assessment cover?
Strategy, data, technology, governance, skills and ownership.
Strategy: whether the proposed workflow connects to a business outcome someone owns.
Data and technology: whether the information needed is accessible, accurate and permitted for this use.
Governance: whether rules exist for data, review and accountability, guided by references such as the NIST AI Risk Management Framework.
Skills and ownership: whether people have the time and capability to review and run the workflow.
Strategy and ownership.
Data and technology.
Governance and skills.
Assessing readiness for AI
why assess readiness against a specific use case?
An AI readiness assessment should test a specific use case, because readiness depends on what you are trying to do.
An organisation can be ready for an internal drafting assistant and unready for an automated customer decision.
Generic maturity scores hide those differences.
Assessing against a named use case produces actions rather than a score; AI use cases for business helps choose one.
Assessing readiness for AI
how is an AI readiness assessment carried out?
Through interviews, document review and a look at the data itself.
Interviews with the process owner, data owner and security owner reveal how the work happens and what constraints apply.
Document review covers existing policies, data agreements and system access.
A sample of the actual data shows whether it is complete and usable, which interviews alone cannot confirm.
| Check | What to establish |
|---|---|
| 1 | Interview the owners. |
| 2 | Review policies and access. |
| 3 | Inspect real data. |
Assessing readiness for AI
what should the assessment produce?
An AI readiness assessment should produce a verdict, ranked gaps and a recommended next step.
The verdict says whether the use case can proceed, proceed with conditions or should wait.
Gaps are ranked by how much they block progress, so effort goes where it matters.
The next step may be a fix, a proof of concept or AI implementation consulting.
For a self-check before an assessment, use our AI readiness checklist.
Assessing readiness for AI
who carries out an AI readiness assessment?
An independent consultant working with your owners.
An AI readiness consultant leads the assessment and writes the findings.
Your process, data and security owners provide the evidence and review the verdict.
Data protection questions should reflect the ICO guidance on AI and data protection; contact predeploy to arrange an assessment.
Consultant leads.
Owners provide evidence.
Owners review the verdict.
Make the decision concrete
What does the current task cost in time?
Choose a task connected to ai readiness assessment and enter its current workload. This gives you a baseline to discuss before buying training or commissioning a build.
A baseline is useful even if you decide to keep the process manual. Measure the work that matters, then compare quality and review effort as well as time.
Assess the workflow before changing it, or plan business AI training if the gap is how people use existing tools.
Your current workload
Estimate how much time the existing task takes before discussing ai readiness assessment.
16 hours per week
People × minutes × weekly tasks ÷ 60. A workload baseline from your inputs, not a prediction of AI time savings. Include review and support time when comparing a proposed workflow.
A useful AI investment leaves your team able to do the work and judge the result.
Continue with the right question
Where should you go next?
Use the overview for context, then follow the pages that match your delivery decision.
See our ai consultancy service or read the ai implementation consulting overview.
FAQ
Questions, answered
Evidence and further reading
Which sources support the guidance?
Source links support the specific background described here. The practical checklists and illustrative examples are predeploy’s guidance.
NIST describes its AI risk framework as voluntary guidance for design, use and evaluation. NIST: AI Risk Management Framework ↗
ICO guidance covers AI accountability, transparency and fairness; the guidance is currently under review. ICO: AI and data protection guidance ↗
Background reading on the broader field of artificial intelligence. Wikipedia: artificial intelligence ↗
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