AI Fluency Assessment for Teams | Measure Human–AI Collaboration

Packaged Offering

What you get (in plain terms)

A team baseline of AI collaboration quality: how people frame, challenge, correct uncertainty, and curate durable value.
Coaching-grade insights: where judgment is strong, where it’s drifting, and what to fix next.
A repeatable loop (optional): re-measure after interventions to validate improvement—without making the system easy to game.

Outputs got cheaper. The differentiator is governed collaboration—and it must be measurable in ways that hold up under optimization.

Request a Pilot Read the Design Framework

We keep the assessment mechanics non-public to reduce gaming. You still get actionable feedback and clear next steps.

How it works (high-level)

Baseline: structured tasks that surface framing, challenge, correction, and curation behaviors.
Evidence: outcomes leave behind traces (constraints, alternatives, corrections, durable artifacts).
Results: bands + feedback (not brittle numeric scores that invite reverse-engineering).
Improve: targeted interventions and re-measurement to validate progress (optional).

Ideal for

Teams adopting AI across knowledge work
Hiring or internal mobility where AI signals are noisy
L&D leaders who need evidence beyond completion
Transformation leaders who want judgment to compound

Engagement options

Baseline

Team Baseline (2–3 weeks)

Establish where collaboration quality is strong vs drifting, and what to fix first.

Baseline measurement
Top risks + strengths
Prioritized improvement areas

Pilot

Pilot + Improvement Loop (4–8 weeks)

Baseline + targeted changes + re-measurement to validate improvement.

Baseline + interventions
Repeat measurement
Evidence-based progress readout

Program

Org Program (Quarterly)

A repeatable capability loop that makes judgment visible over time.

Periodic measurement
Trend insights
Governance coaching + enablement

Why this works when legacy signals fail

Most programs measure exposure (courses) or output polish (portfolios). In AI-shaped work, that’s no longer a reliable capability signal. We focus on governed collaboration—evidence that holds up under real constraints and optimization pressure.

See the doctrine stack (DF-001 → DF-005)

Request a pilot

If you’re exploring AI adoption and want a capability signal that’s harder to fake than tool usage, we can run a baseline and pilot loop with your team.

Request a Pilot Talk to us

We’ll align on scope, team size, and what decisions you want the assessment to support (hiring, enablement, governance, or all three).

FAQ

Is this another AI certificate or training course?

No. This is an assessment service that measures collaboration quality with AI (framing, challenge, correction, curation). Training can follow, but the core deliverable is evidence-based measurement and improvement insights.

Do you measure tool usage or prompt tricks?

No. We measure consequence governance in human–AI collaboration, independent of tools: scope discipline, risk handling, correction behavior, and durable value creation.

Will this reveal proprietary rubrics or thresholds?

No. Results are delivered as bands and evidence-based feedback—useful for improvement, not easy to reverse-engineer.

What do teams get at the end?

A baseline view of collaboration quality, prioritized improvement areas, and (optionally) a repeat measurement to validate progress—plus artifacts that make judgment discipline more visible in AI-shaped work.