AI adoption statistics tell leaders whether a tool is being used. They do not tell them whether the organization can rely on the resulting work. Capability assurance asks a harder question: can people and systems repeatedly deliver the required outcome, under realistic conditions, with appropriate oversight?
Define the work and the failure conditions
Begin with a consequential task, such as reviewing a customer case, interpreting a policy or preparing an operational recommendation. Specify the expected result, the acceptable error rate, the decisions reserved for a person and the situations that require escalation. Different roles will need different evidence of capability.
Test performance rather than attendance
Useful evidence can include observed task completion, review of real work samples, scenario-based judgment, exception handling and the ability to challenge an incorrect AI output. A training certificate or tool login is insufficient on its own.
Assess the whole operating system
Failures may arise from the model, data, workflow, unclear authority, poor instructions or missing human skills. Examine these together. If an employee knows the correct action but lacks permission to take it, more training will not resolve the constraint.
Build an assurance record
- Identify the business outcome and critical work.
- Document capability standards and non-negotiable controls.
- Gather representative performance evidence, including difficult cases.
- Record gaps, owners and corrective actions.
- Retest after changes and monitor performance in operation.
The appropriate level of assurance depends on the consequences of failure. High-risk work warrants stronger validation and oversight than routine low-risk assistance.
Related resources: AI workforce readiness assessment, Who Should Do the Work?, and CATALYST advisory services.
If you are evaluating a workforce capability or learning investment decision, describe the decision in a consultation enquiry.
By Ravinder Tulsiani, DBA.
