AI Readiness Assessment
AI projects fail on data long before they fail on models
The common failure is not choosing the wrong model. It is discovering, halfway through a build, that the data is incomplete, inconsistent, or not captured at all. Readiness is knowable in advance, and checking is dramatically cheaper than finding out during implementation.
- Pressure to adopt AI without a defined use case
- Pilots that demonstrate well and never reach production
- Uncertainty about whether your data is usable
- Vendor claims impossible to evaluate internally
- No agreed measure of what success would be
What the assessment delivers
Readiness score
Maturity assessed across data, infrastructure, process and capability, so gaps are specific rather than general.
Data and technology audit
What data you hold, its quality, how it is accessed, and whether it can support the use cases under consideration.
Use case identification
Five to ten candidates found by examining your actual processes rather than working from a catalogue of AI applications.
Prioritisation by return
Candidates ranked by value, effort and risk, so the sequencing decision is evidenced.
Build, buy or wait analysis
For each candidate, the right approach — including where waiting is genuinely correct.
Implementation roadmap
An eighteen-month phased plan with dependencies, resourcing and decision points.
Business case
Cost and return modelled per use case with assumptions stated openly enough to be challenged.
Executive summary
A document a board can decide from without technical background.
What the assessment produces
5-10
Use cases ranked18 mo
Implementation roadmap3-6 wks
To completeEvidenced
Rather than assumedBuilt for
- Enterprises considering significant AI investment
- Organisations whose pilots have not reached production
- Companies with known data quality concerns
- Boards evaluating competing AI proposals
- Businesses choosing between building and buying
How the engagement runs
Three weeks for a focused review, six for a full assessment.
Book a CallBook a Call- Weeks 1-2
Identify candidates
Process analysis and interviews to find where cost, error and delay concentrate — the conditions under which AI has something to work with.
- Weeks 3-4
Validate feasibility
Data quality, volume, access and infrastructure tested against each candidate. This stage changes the answer more often than any other.
- Weeks 5-6
Prioritise and model
Ranking, business cases and a roadmap sequenced around dependencies and readiness gaps.
- After
Pilot, where warranted
If the assessment supports one, we scope the highest-value pilot with success metrics agreed in advance.
Frequently asked
Then that is the finding, along with what would need to change and what it would take. Discovering that in six weeks is considerably cheaper than discovering it six months into a build.
No. The roadmap is written to be executed by your team or another partner. Where you want us to build, that is a separate conversation on its own merits.
They overlap substantially. The assessment is the more formal, evidence-led version — better suited when a board needs documentation to approve significant spend. The consultancy engagement is lighter and more advisory.
Process owners and the people doing the work, alongside whoever owns the data. Feasibility is determined by operational reality, and that is not visible from the executive level.
Strategic consulting
Digital Transformation Strategy
A sequenced plan, not a slide deck.
Technology Roadmap Development
Decide what to keep, what to replace, and in what order.
Business Process Optimization
Fix the process before you automate it.
Tell us what you are trying to fix
A short call is usually enough to establish whether this is the right answer for your operation, and what it would take. No obligation and no pitch deck.
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