Why Realistic Work Simulations Reveal What AI Skills Quizzes Miss

Executive takeaway: no assessment format answers every question. Knowledge tests are efficient for checking concepts; realistic work simulations are better suited to observing how those concepts become behavior across a complete AI-enabled task.

A multiple-choice question can ask whether a person knows that generative AI may fabricate a source. It cannot show whether that person notices a fabricated source inside a persuasive recommendation, traces it back to the evidence, changes the conclusion and communicates the remaining uncertainty.

That gap matters because workplace AI use is a system of interactions among a person, a task, source material, organizational constraints and an AI tool. NIST's sociotechnical evaluation work explicitly emphasizes assessing AI in context, including what happens when people interact with it in realistic settings.[1]

What simulations make observable

A well-designed simulation gives a participant an assignment, workplace materials, a limited amount of time, familiar tools and an accountable deliverable. It can then reveal behaviors that are difficult to infer from self-report or isolated questions:

• Whether the participant clarifies the audience and decision before generating content.

• Which sources they prioritize when documents conflict or contain distracting detail.

• How they divide work between human judgment and AI assistance.

• Whether they test calculations, citations and recommendations against source evidence.

• How they handle uncertainty, sensitive information and operational consequences.

• Whether the final deliverable is useful to the person who must act on it.

A realistic example

Imagine an operations lead reviewing a proposed workflow automation. The case includes process data, a policy excerpt, stakeholder email and a vendor summary. Some figures use different reporting windows; one stakeholder asks for speed while another flags a control risk.

A quiz could test the definitions of hallucination, privacy or human oversight. The simulation can show whether the participant reconciles the periods, finds the policy constraint, gives AI enough context, challenges an unsupported saving estimate, proposes a proportionate control and writes a decision memo that makes the trade-off clear.

Why this matters for productivity and implementation risk

Organizations often want AI adoption to improve useful output without importing hidden errors, confidentiality problems or poorly governed decisions. Foundation training can help people use tools safely and effectively,[2] but an organization also needs evidence about application: where people succeed, where verification breaks down, and which task families need stronger support.

Simulation evidence can inform targeted development and implementation planning. It may show, for example, that a group prompts confidently but rarely checks source support, or that participants analyze well but fail to adapt the result for a decision-maker. Those are more actionable signals than a single undifferentiated readiness score.

The limitations are part of the result

A simulation is still a sample of behavior. Performance can vary by scenario, domain knowledge, accessibility needs, time limit and tool familiarity. Telemetry can show that an action occurred without fully revealing the participant's reasoning. NIST's risk framework likewise calls for context-aware measurement and documentation of uncertainty.[3] A strong report therefore describes the assessment context, uses only available evidence and expresses confidence limits.

2Prune does not claim that one simulation predicts universal workplace productivity or replaces human judgment. The advantage is narrower and more defensible: a realistic scenario creates direct evidence of applied AI fluency that a knowledge-only test is not designed to capture.

Design the test around the work

The strongest scenario is not the one with the most documents or traps. It is the one whose decisions, evidence and risks resemble the work the organization needs to understand. The deliverable should have a credible audience, the evidence should reward careful navigation, and the AI tool should be useful without making verification optional.

Read next: What is AI fluency?, and how 2Prune thinks about AI assessment methodology. If you want to assess workforce AI readiness through realistic work, request a 2Prune assessment and we will help define the scenario, materials and evidence that matter.