Most AI programs report completion. CLOs need a clearer signal: can people use AI safely, critically, and effectively in real work?
QLM measures AI readiness through scenario tasks and visual team reports. The goal is practical: find strengths, spot risk areas, and decide what training to reinforce next.
Can employees apply AI to real work without skipping judgment?
Do they catch unsupported claims, weak evidence, and overconfident AI output?
Do they understand privacy, policy, escalation, and acceptable use?
The free cognitive beta returns a directional profile. Enterprise pilots add team views, role comparisons, and validation as data accumulates.
| Dimension | What It Measures |
|---|---|
| D1 Analytical | Breaking down complex problems, evaluating evidence, identifying logical flaws |
| D2 Quantitative | Working with numbers, models, and data under uncertainty |
| D3 Verbal | Interpreting nuance, constructing arguments, communicating clearly |
| D4 Spatial | Visualizing systems, relationships, and spatial structures |
| D5 Inference | Drawing valid conclusions from incomplete information |
| D6 Collaboration | Coordinating, delegating, and integrating perspectives |
| D7 Operational | Prioritizing, sequencing, and executing under constraints |
Asking for useful, bounded AI help.
Checking sources, assumptions, and outputs.
Knowing when AI is useful and when it is not.
Following policy, privacy rules, and escalation paths.
An employee receives a fluent AI summary that cites a clause not present in the source document. The assessment asks what they should do before sharing it with a customer.
Directional strengths, watch areas, AI judgment signal, and recommended next learning focus.
Skill gaps by team, role, and department. Clear follow-up priorities in plain language.
Which groups are ready, where risk is concentrated, and what training or policy reinforcement comes next.
QLM is in pilot stage. Public beta results are directional, not hiring recommendations or clinical claims.
We are seeking pilot partners to validate the assessment, improve the question bank, and calibrate team benchmarks with real workforce data.