2-week AI Audit
AI AuditVisibility, risk, outcomes.AI TransformationCapture AI value across workflows.AI GovernanceContinuous evidence for live AI.AI FluencyWorkforce that compounds.All servicesThe full service map.
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By industry
Private equityPortfolio-wide value capture.Regulated financeThe finance umbrella.Banks & capital marketsRegulator-defensible under SR 11-7.FintechShip trusted AI fast.Asset & wealthFor advisors and ops.REITs / real estateAI-driven NOI uplift.InsuranceUnderwriting + claims, instrumented.
Guides, templates, frameworks
Field notesEditorial reads on where AI work stalls.GuidesFinance AI systems, controls, and evidence.FrameworksLevers, maturity models, and control maps.TemplatesAudit memos and reusable eval artifacts.ScorecardsFast reads on adoption, strategy, and governance.Case studiesProof from AI work in finance.Golden datasetsReusable test sets for AI evaluation.NL-to-SQL evalsAudit-grade evidence for finance data agents.AI Audit resourcesInventory, Shadow AI, and board evidence.All resourcesBrowse the complete editorial library.
Featured field note
AI Fluency Is a Learning Curve.
What Anthropic's Economic Index means for workforce fluency.
Read more →

Make the workforce fluent.

For finance teams, 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.

TrustEvals service brief for finance AI teams.
What workforce fluency looks like

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.

Stage 1. Awareness
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.
Stage 2. Activation
Tools land in the right roles and seats convert to first real use. The evidence: per-role activation rate, not blanket license counts.
Stage 3. Integration
AI is woven into priority workflows with patterns the team can repeat. The evidence: depth of use and workflow coverage, not prompt volume.
Stage 4. Mastery
Output quality lifts and managers validate the impact in the work product. The evidence: manager-rated quality lift, role by role.
Stage 5. Compounding
Patterns spread laterally, libraries get reused, and the curve bends without new cost. The evidence: cross-team reuse of role libraries.
External learning-curve evidence

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.

Tenure matters
A user who has worked with AI for six months has different habits than a user who just received access. The Audit reads that difference by role.
Collaboration matters
The strongest signal is iterative use: asking, checking, revising, and validating inside real workflow, not one-shot delegation.
Model-task fit matters
High-value workflows need the right model class and review path. Finance teams should know which tasks deserve stronger reasoning and tighter proof.
Source

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

Per-role fluency tracks

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.

CEO and Operating Partner
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.
CIO and CAIO
Tooling rationalization, role-mapped license allocation, manager-level rollout playbooks. Scored on per-role activation and workflow coverage.
CFO
Finance-team patterns for close, FP&A, and reporting, paired with manager-validated quality checks. Scored on cycle time and audit-ready output quality.
CISO
Approved-tool footprint, sanctioned patterns, and a fluency telemetry that flags shadow use early. Scored on coverage of sanctioned use and time-to-detect drift.
How fluency compounds

Tooling, training, and telemetry compound fluency.

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

Role-specific tooling rollout
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.
Adoption telemetry and fluency scoring
Per-role and per-manager dashboards show who is getting value and who is stuck.
▸ Entry diagnostic
AI AUDIT
two-week cross-cutting first read
WORKSTREAM 01
AI TRANSFORMATION
process delta on priority workflows
WORKSTREAM 02
AI GOVERNANCE
continuous, framework-agnostic evidence
WORKSTREAM 03
AI FLUENCY
every employee meaningfully better
MEASUREMENT LAYER
EVALS ACROSS THE WORK
production behavior, policy, drift, and proof for every workstream
Where this sits

AI Fluency lifts the people doing the work.

The AI Audit sets the operating read. AI Transformation captures workflow upside. AI Governance contains risk. 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 the 2-week AI Audit.

Leave with the operating read: AI value, AI risk, fluency gaps, owners, and the next funded workstream.

Common questions

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.