AI maturity assessment
Structured interviews and system reviews across teams, scored against a maturity model covering strategy, data, skills, governance, and delivery. You see where you stand and what blocks progress.
AI strategy & readiness
Know which AI use cases are worth building
Know which AI use cases are worth building
before you fund them
A two to four week assessment maps your workflows, tests whether your data can support them, and ranks use cases by value and feasibility so engineering starts in the right place.
Book discovery call
Our clients
KnowlesSterne KesslerRepleniumABBClient fiveClient sixClient sevenClient eight
What the assessment
gives you
Process mapping
Step-by-step maps of the workflows people actually run, including handoffs, exceptions, and approvals. These maps show where automation helps and where it would add risk.
Use-case prioritization
Each candidate use case is scored on business value, feasibility, data availability, and risk. The result is a ranked list your leadership can debate with evidence.
Business cases and ROI model
Cost, effort, and expected return for the top use cases, with the assumptions written down so finance can challenge them and track them later.
Data readiness audit
A review of the data each priority use case needs: where it lives, its quality, access rules, and gaps. Gaps are sized so they can be planned rather than discovered mid-build.
Sequenced roadmap
A delivery plan that orders use cases by dependency and value, with team shape, timelines, and the governance decisions needed before each stage.
Have a list of AI ideas but no way to rank them?
Bring the list to a call and we will show you how we would score it.
Book discovery callDoes your AI plan
have these gaps?
Every team has a different priority
Requests arrive from every department with no shared way to compare value or effort.
Use cases picked by enthusiasm
The loudest idea gets funded, not the one with the clearest return.
Data problems found too late
Projects stall mid-build when the data turns out to be missing or locked away.
No baseline to measure against
Without current cost and time figures, nobody can prove the AI investment paid back.
How the assessment
runs in four weeks
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We interview leaders and the people who run key workflows, and review systems and data sources.
Deliverables: Stakeholder map, interview notes, system inventory
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Priority workflows are mapped step by step, with current time, cost, and error rates recorded as the baseline.
Deliverables: Workflow maps, baseline metrics
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Use cases are scored on value, feasibility, data readiness, and risk, and the top candidates get business cases.
Deliverables: Scored portfolio, business cases, data readiness findings
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We sequence the work, define team needs, and present the roadmap to leadership for decision.
Deliverables: Roadmap, readiness scorecard, executive briefing
Featured success stories
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Engineers who ship inside your systems,
not isolated AI demos
Talk to an FDE lead
Production from the first sprint
We build in your repositories and environments from day one, so there is no separate rebuild between proof of concept and release.
Senior pods, not rotating benches
Each pod is led by an engineer who has shipped AI systems before, drawn from tkxel’s team of around 150 AI professionals. The same people stay through delivery.
Governance built in, not bolted on
Autonomy matrices, audit trails, and human review are designed with your risk and security teams before release, backed by ISO 27001 controls.
Measured against your metrics
Every engagement starts with agreed business metrics and evaluation sets. We report against them throughout, and the pod exits when your team can run the system.
We've been recognized by the best, year after year
- Inc. 5000
- Top 15 Inspiring Workplaces
- Titan Business Platinum (AI & Automation)
- Financial Times
- Mogul People Leader
- Forbes Coaches Council
- ISO 27001
- ISO 20000
- ISO 9001
- CMMI DEV 3
Start with a clear, ranked AI roadmap
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Frequently asked questions
How long does the assessment take?
Most assessments take two to four weeks depending on how many departments and workflows are in scope. A single-department assessment can finish in two weeks; a company-wide review usually takes the full four.
Who from our side needs to be involved?
An executive sponsor, a few hours from each department lead, and time with the people who run the workflows day to day. We also need read access to the systems and data sources the priority use cases depend on.
Will you recommend building everything with tkxel?
No. The roadmap names the best route for each use case, which is sometimes an existing product licence or work your own team can do. Where an embedded pod is the right choice, we say so and explain why.
What if our data is not ready?
That is a common and useful finding. The data readiness audit sizes each gap, and the roadmap orders work so that data foundations come before the use cases that depend on them.
What happens after the assessment?
You own the roadmap and can act on it however you choose. Many clients move straight into an FDE pod for the top-ranked use case, using the business case and baseline metrics from the assessment.
Is the assessment fixed-price?
Yes. Scope and price are agreed before the work starts, based on the number of departments and workflows included.