AI Readiness Checklist
A red flag in any one dimension can stall or sink an entire initiative. Work through each section before scoping your next AI engagement.
AI readiness checklist
Work through each dimension before scoping any AI engagement. A red flag in any one area can stall or sink the whole initiative.
The starting point. If there's no clear problem tied to business value, and no one senior enough to own it, everything else is irrelevant. GenAI enthusiasm is not a use case.
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Is there a named, specific use case — not just 'we want AI'?
The use case should reference a concrete problem, a measurable cost, and an owner.
Critical -
Does the use case have a senior sponsor with budget and authority?
Projects without exec sponsorship rarely survive prioritisation battles.
Critical -
Is there a clear definition of success — not just 'better'?
Think: time saved, error rate reduced, revenue influenced. Something you can measure in 3 months.
Important -
Is AI genuinely the right solution — or would a simpler tool do the job?
Challenge the assumption early. LLMs are expensive, complex, and often overkill.
Important -
Are the key business stakeholders aligned on scope and expectations?
Misaligned expectations are the most common cause of 'failure' that isn't actually a technical failure.
Watch
Probably the dimension most underestimated by clients. AI doesn't improve data quality — it amplifies whatever quality exists. Inaccessible or untrustworthy data kills more AI projects than bad models do.
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Is the relevant data actually accessible — not trapped in legacy systems or behind red tape?
Access issues often surface only mid-project. Map this before signing off on any timeline.
Critical -
Is data quality understood and documented?
Completeness, accuracy, freshness, consistency. If no one knows, assume the worst.
Critical -
Is there a clear owner for the data that will feed the AI system?
Ownerless data drifts. You need someone responsible for its ongoing quality.
Important -
Are there PII, sensitivity, or compliance constraints on the data?
GDPR, sector-specific regulation, internal classification policies. Surface these early.
Important -
Is there enough labelled or historical data to train, fine-tune, or evaluate the model?
For GenAI use cases this bar is lower — but not zero. Know what you have.
Watch -
Can the data be refreshed reliably once the system is live?
A model trained on stale data will degrade. Who refreshes it, how often, and at what cost?
Watch
A demo that works on a laptop is not a production system. The question is whether the underlying platform can host, integrate, and scale a solution that real users will depend on.
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Is there a cloud or on-prem environment capable of hosting the AI workload at scale?
Evaluate compute, storage, and latency requirements against what's available.
Critical -
Can the target system integrate with existing data sources and downstream tools via APIs?
Integration debt is where projects stall after the PoC. Check this before the PoC.
Important -
Is the current tech stack stable enough to build on — or is there significant underlying technical debt?
Building AI on a fragile foundation just shifts the problem downstream.
Important -
Are there existing ML/AI platform components that can be reused (vector stores, embedding pipelines, orchestration)?
Reuse reduces time-to-value and lowers maintenance burden.
Watch -
Is the architecture observable — can you monitor model behaviour and system health in production?
You can't manage what you can't see. Logging, tracing, and alerting must be designed in.
Watch
Governance isn't compliance theatre. It's the operational structure that lets you deploy AI without it becoming a liability. Most organisations skip this until something goes wrong.
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Is there a defined owner responsible for AI outputs in production — not just IT?
Business ownership of AI outputs is non-negotiable. IT can run the model; they can't own the decisions it informs.
Critical -
Is there a process for reviewing AI outputs before they affect customers, staff, or decisions?
Human-in-the-loop is not optional for high-stakes use cases. Define the review triggers now.
Critical -
Are the risks of this specific use case understood and accepted by the right people?
Hallucination risk, bias risk, regulatory risk. Has the relevant risk function signed off?
Important -
Does the organisation have an AI or data ethics policy — or at minimum, working principles?
If not, you'll need to establish guardrails for this project specifically.
Important -
Is there a process for handling failures or unexpected model behaviour once live?
What happens when it gets something wrong? Who decides, who escalates, who fixes it?
Watch -
Are there audit or explainability requirements for this use case?
Regulated industries and internal audit functions often require traceable decisions. Know this upfront.
Watch
Can the organisation build it, and — critically — sustain it after go-live? Many AI projects get delivered and then orphaned. Delivery capability includes the people and change management, not just the technical skills.
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Does the team have the technical skills to build and operate the solution — or is there a realistic plan to acquire them?
ML engineering, prompt engineering, platform ops. Know the gap before you scope.
Critical -
Is there a realistic plan for who owns and maintains the solution after go-live?
The most common failure mode: project team disbands, no one retrains the model, quality degrades silently.
Critical -
Has the end-user change management been planned — not just the technical rollout?
Adoption fails when users aren't brought along. Training, communication, and feedback loops must be designed.
Important -
Is the project scope realistic given available time, budget, and team capacity?
AI projects routinely underestimate data prep and integration work. Build in contingency.
Important -
Is there a plan for retraining or updating the model as data and requirements evolve?
Models degrade as the world changes. Drift detection and retraining cadence should be agreed upfront.
Watch -
Are there dependencies on third parties (model providers, data vendors) that introduce delivery or continuity risk?
API pricing changes, vendor lock-in, terms of service restrictions. Surface early.
Watch
About the AI Readiness Checklist
Who it is for
Use this checklist before you scope or fund an AI initiative. It is written for the person who has to say whether the organisation is ready: a CTO, a head of product, a head of data, or a consultant preparing an engagement.
It takes about fifteen minutes alone. It is more useful in a room with the business sponsor and the technical lead, because the disagreements are where the real gaps are.
What it measures
The 28 questions are grouped into five dimensions. A serious gap in any one of them can stall the whole initiative.
- Business priority. Whether there is a named use case, a senior sponsor with budget, and a definition of success you can measure.
- Data readiness. Whether the data is accessible, its quality is known, someone owns it, and it can be refreshed once the system is live.
- Architecture maturity. Whether the platform can host, integrate and observe a system that real users depend on.
- Governance maturity. Who owns AI outputs in production, how they are reviewed, and whether the risks have been accepted by the right people.
- Delivery capability. Whether the team can build the solution and, above all, keep it running after go-live.
How to read the result
Tick each question you can honestly answer yes to. The score is the share of questions ticked, shown per dimension and in total.
80% or more reads as ready to proceed. 55% to 79% means proceed with caution. 30% to 54% means significant gaps. Below 30% means not ready.
Look at the flags before the total. Each question is marked Critical, Important or Watch. One unticked Critical question matters more than a high overall score, so treat those as blockers to resolve before you commit a timeline.
Common questions
What is an AI readiness assessment?
It is a structured check of whether an organisation can take an AI use case to production: a real business problem, usable data, a platform that can host it, clear ownership and a team that can sustain it. It is done before scoping so that gaps surface while they are still cheap to fix.
What score do I need before starting an AI project?
There is no pass mark. A team at 60% with every Critical question ticked is in a better position than a team at 85% with no sponsor. Use the score to decide what to fix first, not whether to feel confident.
How is this different from the Data Readiness Checklist?
This checklist covers the whole organisation in five dimensions, with data as one of them. The Data Readiness Checklist goes deeper on the data alone, with 27 questions. Start here, and run the data checklist if the data dimension is your weakest.
Is it free?
Yes. The tool is free and runs in your browser with no account. The downloadable report asks for an email address, and your scores are sent with it so I can follow up if you want to talk them through.