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Generative AI Leader Rolling out a generative AI tool to employees Practice Question
A company is rolling out a generative AI tool to employees. To ensure successful adoption, they plan to provide training and identify early adopters. Which change management practice is MOST critical early in the rollout?
Answer choices
Why each option matters
Answer the question above first, then reveal the full breakdown to understand why each option is right or wrong.
Correct answer & explanation
✓
Identify and train AI champions who can support their teams
Identifying AI champions who can advocate and help peers is crucial for organic adoption. Training all employees at once is less effective. Monitoring usage is important but later. Setting up a help desk is reactive.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✗
Set up a dedicated help desk for AI tool issues
Why it's wrong here
A help desk addresses incidents after users hit problems; it neither identifies early adopters nor delivers the training the stem specifies for early rollout. It is tempting because support reduces friction, but it is reactive infrastructure that becomes valuable once adoption is underway, not the critical early practice.
- ✗
Conduct mandatory training for all employees before launch
Why it's wrong here
Mandatory pre-launch training for everyone front-loads content before employees have context or questions, and it does not identify the early adopters the stem calls for. It is tempting because training is a recognised adoption lever, but it belongs once champions are identified and use cases are concrete.
- ✓
Identify and train AI champions who can support their teams
Why this is correct
Identifying and training AI champions directly satisfies the stem's need to build peer support during early rollout. Champions are influential early adopters who model usage, answer colleagues' questions and reduce resistance, embedding the tool within existing team workflows far faster than centralised training alone achieves.
- ✗
Monitor usage metrics for the first month
Why it's wrong here
Usage metrics describe what already happened, so monitoring for a month yields lagging data rather than the early adopters and training the rollout needs. It is tempting because measurement underpins change management, but it is a later feedback activity, not the critical early practice of building advocacy.
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