AI MATURITY ASSESSMENT
Enterprise AI maturity assessment
We assess your AI maturity across seven dimensions and turn the findings into a scorecard, priority gaps and a practical investment plan.
- A scored baseline across seven dimensions, evidenced not asserted
- A data-readiness deep-dive to expose gaps before delivery
- A board-ready readout leadership can align and fund against
If several of these land, you are planning on assumptions.
Scattered readiness answers look like debate on a status call. On a funding decision, they look like risk. An assessment replaces the assumptions with evidence: a scored view of where you actually are, and the specific work between you and the next rung.
Nobody agrees how ready you are
Different leaders give wildly different answers to a simple question: how AI-ready are we.
Pilots stall at an invisible barrier
Initiatives keep dying at the same point, and no one can name the constraint that stops them.
You are asked to scale with no baseline
Leadership wants AI at scale, but you have no evidence you can, only optimism.
Data problems surface late, every time
The data estate becomes a surprise in delivery, never a plan at the start.
Talent and governance gaps go unmeasured
They are discussed anecdotally in meetings, never quantified against a standard.
The board wants a view you cannot produce
A board or investor has asked for an AI readiness picture you currently cannot show.
We place you on a five-stage progression, then show what it takes to climb.
The value is not the stage. It is seeing the specific rung, and the specific work that moves you up one.
Self-assessment can overstate maturity when ratings are not tied to evidence. Naming the current rung clearly is where the assessment earns its keep.
A low but honest score beats an inflated one. It tells you exactly where to invest before you scale.
Seven factors that actually predict whether AI succeeds.
Data readiness gets particular scrutiny because gaps in access, quality and ownership can stop an otherwise sound use case. Each dimension is scored against the ladder, producing a scorecard that shows your peaks, your drags, and where investment moves the needle fastest.
Strategy and vision
A funded AI ambition tied to real business outcomes, not a slogan.
Data readiness
Quality, access, governance, and volume for the use cases you actually want. The silent blocker to production AI, scrutinized hardest.
Talent and skills
In-house capability to build, run, and govern AI over time.
Operating model and MLOps
The engineering to get models to production and keep them there.
Governance and risk
Controls, accountability, and regulatory alignment you can evidence.
Culture and change
Whether the organisation actually adopts what gets built.
Realised value
Whether deployed AI has moved a metric that leadership cares about.
Five steps to a baseline that holds up under challenge.
Frame
Align on the business outcomes AI is meant to serve, so maturity is measured against your goals, not a generic template.
Gather
Structured interviews, artefact review, and a data-estate inspection across all seven dimensions.
Score
Plot each dimension on the ladder, evidencing every rating so the baseline is defensible under challenge.
Diagnose
Identify the constraints that hold you back, and the two or three that matter most right now.
Recommend
A prioritised set of moves to climb the next rung, sequenced by impact and effort.
Artifacts leadership can act on, not a slide to admire.
Turn the baseline into an investment decision.
Evidence-backed gaps. Explain each maturity rating using the systems, processes and interviews assessed.
Prioritised action. Separate immediate constraints from longer-term improvements and identify dependencies.
A way to reassess. Keep the scoring criteria and baseline so progress can be checked using the same method.
An assessment is the front door to strategy.
Use Case Discovery and Validation
Findings feed a scored, validated backlog of the few use cases worth funding.
Roadmap and Blueprint
A costed, phased plan on a reference architecture leadership can fund.
AI Strategy and Consulting
The full engagement the assessment opens, from baseline to funded roadmap.
AI at Scale
The operating model that turns a good score into a repeatable capability.
What leadership asks before it commits.
Typically a few weeks for the seven-dimension assessment, including structured interviews, artefact review and a data-estate inspection. The two-week AI Readiness Assessment is a faster five-dimension decision baseline; this service is the deeper maturity diagnostic.
No. We evidence every score against artefacts and the data estate itself, so the baseline holds up when a sceptical executive challenges it.
That is the point. A low but honest score is more valuable than an inflated one, it tells you exactly where to invest before scaling.
Yes. The assessment is built to hand directly into roadmap and delivery, so the recommendations get executed rather than shelved.
Find out where you actually stand, and what it takes to climb the next rung.
Across strategy, data, talent, and governance, scored and evidenced, so the number holds up in the room where budgets are set.