Questions to ask about AI training impact

What change is the programme meant to produce?

Define the work before selecting a metric. Is the aim to improve source checking, task decomposition, safe data handling, spreadsheet verification, manager review, or delivery of a specific artifact? Name the population, workflow, expected behavior, evidence source, timeframe, and decision the evaluation will support.

Ask whether the programme taught knowledge, supported practice, changed a workflow, or introduced a tool. Each intervention creates a different causal path. Attendance can establish exposure to training. A knowledge check can show recall. Neither alone shows transfer to a realistic task.

The OECD Artificial Intelligence and the Future of Skills programme emphasizes bounded skill and capability measures when comparing AI with workplace requirements. Its work is aimed at policy measurement, not a ready-made corporate training rubric, but it supports defining the capability under study instead of relying on a broad “AI skilled” label.

Sources: [1]

Did participants have a fair opportunity to apply it?

Ask whether participants encountered the target task after training, had approved tool access, could use appropriate data, received manager support, and understood local policy. A lack of observed behavior may reflect missing opportunity or blocked workflow rather than missing capability.

Record the transfer conditions: task frequency, tool availability, permissions, workload, incentives, review expectations, and changes to the surrounding process. Compare groups or periods only when these conditions are sufficiently similar, or disclose the differences.

The NIST AI RMF Measure guidance calls for selecting methods and metrics appropriate to mapped risks and documenting what is not measured. This supports explicit evidence gaps and mixed methods; it does not validate a particular training programme.

Sources: [2]

What evidence demonstrates changed behavior?

Prefer evidence close to the intended performance: a realistic work sample, observed workflow, reviewed deliverable, error-recovery exercise, or structured manager observation tied to specific anchors. Define the behavior before examining results. Examples include checking a cited source, independently recalculating a material figure, refusing an unsupported instruction, escalating sensitive data, or revising a conclusion after contradictory evidence.

Use activity data carefully. A rise in weekly AI use may show adoption but not whether work improved. A confidence survey may capture perceived readiness but can move independently of observed performance. A final deliverable may look strong while hiding a fragile process. Combine sources only when each has a stated role in the evidence chain.

Worked artifact: a synthetic post-training exercise

Imagine a synthetic finance cohort trained to verify AI-generated variance explanations. The impact evidence chain defines the target behavior as tracing each material driver to the approved ledger extract, checking period and currency, and marking unresolved causes. Participants complete a controlled exercise before training and a structurally similar exercise afterward.

In the follow-up, more participants catch a currency mismatch, but several still accept an unsupported causal explanation. The evaluator records both findings. Managers also report that production access to the approved exchange-rate table is inconsistent, creating a transfer barrier. The programme owner responds with a focused verification practice and an access fix rather than claiming that the full capability is established.

The artifact contains the task specification, scoring anchors, opportunity checks, observed actions, missing evidence, manager context, and next test. The scenario is synthetic. It does not establish a Two Prune benchmark, programme outcome, or causal effect.

What comparison supports the conclusion?

Ask whether the evaluation uses a baseline, a comparison group, repeated tasks, or only a post-course snapshot. Check task equivalence, prior experience, attrition, coaching, and who was observed. Report absolute counts and uncertainty as well as averages. If the design cannot separate training from other changes, describe association or observed change rather than causation.

Also ask whether improvements persist. A near-term assessment may show learning under controlled conditions. Transfer requires later evidence in relevant work, and organizational results may take longer or depend on process and management changes outside the course.

What decision follows from the evidence?

Specify in advance whether results will revise content, target practice, change manager support, fix access, continue a pilot, or trigger deeper evaluation. Document evidence that would reverse the decision. Avoid converting a development measure into an employment judgment that it was not designed to support.

These questions create a defensible path from training to task-bounded evidence. They do not establish universal programme effectiveness, predict future performance, or replace human interpretation. Two Prune can provide structured evidence from realistic simulations with confidence limitations; it does not make employment decisions.