Make the workforce fluent.
For finance. Role-specific tooling rolled out to the populations that need it most, hands-on training mapped to real workflows, and a fluency score every manager can read.
AI Fluency is the measurable capability of an employee to use AI to do their actual job better. Not the count of training sessions completed, not seats activated, not prompts written.
Five stages from awareness to compounding.
Workforce fluency is a curve, not an event. Each stage has its own bottleneck, its own intervention, and its own telemetry proof.
One curve. Five role tracks.
Fluency is owned at the role, not at the company. Each track has its own tooling, its own pattern library, and its own scoring rubric, fed by the same AI Audit.
Tooling. Training. Telemetry.
Three workstreams produce fluency. The right tools deployed to the right roles, hands-on training and pattern libraries that map to real workflows, and telemetry that tells managers who is getting value and who is stuck.
The workforce-side workstream, anchored on the AI Audit.
AI Fluency is one of three workstreams that flow from the AI Audit. AI Transformation captures the upside in the workflow. AI Governance contains the risk in the surface. AI Fluency lifts the people who do the work.
The workstream only works when it sits next to capture and risk. People need real workflows to practice on and a safe operating surface to work inside.
Book the AI Audit.
Thirty minutes to size the discovery surface: employees, devices, SaaS admin access, developer tooling, internal agents, Shadow AI exposure, and the outcome read you need at the end.
Questions buyers actually ask.
Training is one of three workstreams. The other two are role-specific tooling rolled out to the populations that need it most, and adoption telemetry with a fluency score every manager can read. Training without those is what fails to stick.
Yes. The engagement compresses if you already have a defined transformation workflow running, because the fluency curve has a concrete workflow to compound on. Most finance customers run them in sequence, anchored on the same AI Audit.
Depth of use, workflow integration, output quality, and manager-validated impact, scored per role and per team. It is the answer to 'are people getting better at their job because of AI', not 'who logged in'.
Yes, but it is a separate Evals engagement. Eval pipelines, red-teaming, model comparison, and prompt optimization for AI product companies live at /services/evals as the measurement layer across the work.