AI Fluency

Make the workforce fluent where the work changed.

We make AI trustable and reliable in production. AI Fluency lifts the people doing the work: role-specific tooling reaches the populations that need it most, hands-on training maps to real workflows, and every manager gets a fluency score they 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.

Leaders and managers know what AI is in scope, what is out, and what the first workflow targets are. The evidence: a single shared map of approved tools and assigned populations.

Tools land in the right roles, and seats convert to first real use. The evidence: per-role activation rate, not blanket license counts.

AI is woven into priority workflows with patterns the team can repeat. The evidence: depth of use and workflow coverage, not prompt volume.

Output quality lifts, and managers validate the impact in the work product. The evidence: manager-rated quality lift, role by role.

Patterns spread laterally, libraries get reused, and the curve bends without new cost. The evidence: cross-team reuse of role libraries.

Experienced AI users work differently.

Anthropic's March 2026 Economic Index found that longer-tenure Claude users bring more complex work, collaborate more, and have higher conversation success. That is the public signal behind our fluency scoring: measure the curve, not just the seats.

A user who has worked with AI for six months has different habits than a user who just received access. The read tells that difference apart by role.

The strongest signal is iterative use: asking, checking, revising, and validating inside real work, not one-shot delegation.

High-value workflows need the right model class and review path. Teams should know which tasks deserve stronger reasoning and tighter proof.

Read the TrustEvals field note: AI Fluency Is a Learning Curve. Source: Anthropic Economic Index, March 2026.

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 read.

Portfolio-wide reads, deal-memo and IC tooling, board-ready AI summaries. Scored on time-to-decision and quality of the read, not seats activated.

Tooling rationalization, role-mapped license allocation, manager-level rollout playbooks. Scored on per-role activation and workflow coverage.

Finance-team patterns for close, FP&A, and reporting, paired with manager-validated quality checks. Scored on cycle time and audit-ready output quality.

Approved-tool footprint, sanctioned patterns, and fluency telemetry that flags shadow use early. Scored on coverage of sanctioned use and time-to-detect drift.

Tooling, training, and telemetry compound fluency.

AI Fluency turns role-specific tools, workflow training, and adoption telemetry into manager-visible capability.

Tools matched to leadership, ops, finance, and risk roles. No blanket-license rollout.

Hands-on training and pattern libraries

Workflow patterns, manager enablement, and quarterly refreshes as the tooling changes.

Fluency telemetry and scoring

Per-role and per-manager dashboards show who is getting value and who is stuck.

Continuous, framework-agnostic evidence

Production behavior, policy, drift, and proof feed the same trace pipeline the rest of the work runs on.

AI Fluency lifts the people doing the work.

The read sets the operating picture. AI Transformation captures workflow upside. AI Governance proves what is running holds. AI Fluency gives teams the tools, practice, and confidence to work inside those guardrails.

The workstream works because it sits next to capture and risk, not apart from them.

Start with a discovery call.

A discovery call sizes the fluency work against the workflows that changed: approved AI inventory, shadow AI exposure, value signals, evidence gaps, owners, and the next workstream. If you want the independent read first, a quick audit lands it in two weeks.

Questions buyers actually ask.

How is AI Fluency different from the AI training programs we have seen? Training is one of three workstreams. The other two are role-specific tooling rolled out to the populations that need it most, and fluency telemetry with a score every manager can read. Training without those is what fails to stick.

Can we run AI Fluency without doing AI Transformation first? 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 customers run them in sequence, fed by the same read.

What does fluency scoring actually measure? 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".

Do you do AI product evaluation for our software product? 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.

Deployed is not the same as working.

Scope

Signals

Materiality

Opinion

Next moves