The problem this solves
Ambition and capability drift apart fast. Leadership wants AI in every process, but the data is fragmented, nobody owns prompt or workflow quality, tooling is a patchwork of personal subscriptions, and the one person who understands the stack becomes a bottleneck. Projects then stall not on model quality but on missing plumbing: access, skills, ownership, and process discipline.
How we work
We assess capability across five dimensions: data foundations, tooling and licensing, team skills, process maturity, and governance. The current-state picture is evidence-based: what your team can demonstrably do today, not what the org chart implies.
Then we define the target maturity your strategy actually requires, which is rarely the maximum. A mid-market B2B company does not need a machine learning platform team; it needs reliable data, a handful of well-run AI workflows, and clear ownership. Overshooting the target wastes as much as undershooting it.
The gap plan closes the engagement: what to train, hire, buy, or consolidate, sequenced so that foundational gaps close before the capabilities that depend on them.
Deliverables
- Capability assessment across data, tooling, skills, process, and governance
- Maturity model with current and target state
- Gap analysis with prioritized actions
- Skills and roles recommendation
- Tooling consolidation recommendation
- Sequenced capability-building plan
What buyers ask before scoping.
How is this different from the AI readiness assessments in your diagnostics phase?
The assessments diagnose: they tell you where you stand. This module decides: it sets the target maturity and the route to it. If you have run the readiness assessments, their findings become the current-state input here and the work moves faster.
Do you benchmark us against other companies?
We give a qualitative, honest read based on what we see across implementations, but we do not invent industry percentile scores. The useful anchor is not how you compare to an abstract peer; it is whether your capability supports the AI work your strategy requires. That is what the maturity model measures against.
We are a small team. Is a maturity strategy overkill?
The opposite, usually. For a smaller company, maturity strategy means a few decisive choices: which data to fix, which two or three capabilities to build, who owns them. Getting those wrong costs a small team proportionally more than it costs an enterprise. The deliverable is scaled accordingly: pages, not binders.
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