Aware
AI is on the board's agenda, but unfunded.
Maturity model
Six stages of enterprise AI maturity.
Where your firm sits decides the next move: capture more value, or prove more control. Six stages, from first pilots to an architecture that absorbs each new model.
Experimenting
Pilots run and shadow AI spreads, with no operating read.
Deployed
AI is in production, but you cannot measure what value it has produced, so the value you hoped for never shows.
Reliable
The output is reliable enough to act on and the value is landing, captured ahead of a formal govern state.
Governed
Controls, independent evidence, and a review cadence the board and the regulator can rely on.
Adaptive
New frontier models and agent frameworks are plug-and-play because the trust and eval harness carries forward.
Stage by stage
The wrong move at the wrong stage is expensive.
Each stage has a different question, buyer, and capability need. The model stops the generic AI conversation and starts the specific one.
Aware
Is AI going to matter for us?
- Buyer: CEO
- What matters: Strategy framing, peer awareness, vendor education.
- Start with a neutral read of what is running and whether any value thesis is real.
Experimenting
What AI tools should we try, and are teams using them?
- Buyer: CIO / CAIO
- What matters: Discovery, tool inventory, shadow AI visibility, light ROI signal.
- Quick Audit finds approved tools, shadow AI, owners, spend, and obvious exposure.
Deployed
Are our AI investments producing value?
- Buyer: CIO + CFO
- What matters: Usage depth, ROI measurement, spend intelligence, board reporting.
- Discovery Call sizes the value workstream and decides what gets funded next.
Reliable
Are our AI systems behaving the way we need?
- Buyer: CIO + CISO
- What matters: Behavioral evaluation in production, baselines per use case, drift detection.
- Evals sets the production baseline and keeps drift, grounding, and policy visible.
Governed
Can we produce audit-ready evidence continuously?
- Buyer: CIO + CISO + Compliance
- What matters: Framework mapping, continuous evidence, audit-pack exports.
- Governance turns production traces into continuous framework-mapped evidence.
Adaptive
How do we make AI a durable operating advantage?
- Buyer: Board / CEO / CIO
- What matters: Benchmarking, agent-to-agent governance, platform consolidation.
- The end state is harness-portable: new models and agent frameworks inherit the eval and evidence layer.
How the model was built
Built from inside. Not from a report.
Every stage corresponds to a real CXO interaction: the CEO asking for the AI update, the CIO chasing a usage number, the CISO defending an agent incident, compliance facing a framework clock.
A common arc, not a strict gate.
Organizations do not move through AI maturity in a perfect line. A team can be Adaptive in one workflow and Deployed-but-blind in another. The operating read decides which door comes next: capture more value through Transformation, or prove more control through Governance.
Trustable, reliable AI in production
Start with the AI work that moves the number. Keep the proof built in.
Start with Strategy, Transformation, or Fluency; use Quick Audit when the first need is an independent read on what is already running.