AI readiness needs evidence, not completion rates.
QLM measures whether employees can use AI safely and effectively in real work scenarios: verify outputs, protect data, escalate risk, and choose the right tool for the task.
Seven practical AI competency signals.
Prompting and task framing
Can the employee ask for bounded, useful AI assistance without outsourcing judgment?
Output verification
Do they check sources, assumptions, and unsupported claims before acting?
Privacy and policy
Do they avoid sensitive-data exposure and follow escalation paths?
Tool choice
Can they tell when AI is useful, risky, unnecessary, or insufficient?
Hallucination detection
Can they identify fluent but false reasoning and missing evidence?
Responsible judgment
Do they account for fairness, accountability, misuse, and human review?
Small cohort. Clear decision.
1. Pick a role
Choose the team and AI workflows that matter most.
2. Run scenarios
Employees complete short role-relevant AI judgment tasks.
3. Read the map
Leaders see profiles, heatmaps, and recommended actions.
4. Retest gaps
Assign reinforcement and measure whether readiness improves.