Generative AI Leader Adopt GenAI for internal knowledge management Practice Question

Answer analysis

Option-by-option breakdown

For each option: why learners choose it and why it is or isn't the right answer here.

• ✓

  Pilot with a small group, identify AI champions, collect feedback, and iteratively expand

  Why this is correct

  A phased pilot with named AI champions, structured feedback capture and iterative expansion matches the stated plan of starting small, learning, then scaling. This incremental, people-led approach reduces adoption risk and builds internal advocacy before wider rollout.

• ✗

  Conduct mandatory training for all employees before the rollout

  Why it's wrong here

  Mandatory training for everyone before any rollout inverts the pilot-first sequence, committing the whole workforce before feedback is gathered. Universal training is appropriate once the solution is proven and scaling, not while a small team is still validating use cases and refining the approach.

• ✗

  Deploy the solution to the entire organization at once with a communication campaign

  Why it's wrong here

  A single organisation-wide launch contradicts the stated pilot-then-expand plan, removing the feedback loop that would inform wider rollout. Big-bang deployment suits stable, well-understood tools with minimal behaviour change, not an unproven GenAI pilot whose value and risks are still being validated.

• ✗

  Focus solely on the technical deployment without change management activities

  Why it's wrong here

  Skipping change management entirely removes the feedback loops and adoption support the phased pilot-to-expansion strategy depends on, so user resistance and low uptake go unaddressed. It is tempting when delivery pressure dominates, since pure technical deployment is legitimate for short-lived, single-team proofs of concept with no wider rollout planned.

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