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Generative AI Leader Practice Question: A company runs a pilot for a GenAI-powered…
A company runs a pilot for a GenAI-powered internal knowledge base assistant. They want to measure adoption. Which metric is BEST for this purpose?
⚠ Common exam trap
Test-takers frequently confuse adoption with engagement or satisfaction, picking metrics like follow-up questions or survey scores, but Google specifically tests that adoption is about the proportion of the target population using the system, not how deeply or happily they use it.
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
✓
Number of unique users per week divided by total employees
The best metric for measuring adoption because it directly captures the breadth of usage across the organization. Adoption is defined as the proportion of the target user base that actively uses the system, and dividing unique weekly users by total employees provides a clear percentage of uptake, which is the standard measure for adoption in enterprise GenAI deployments.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✗
Number of follow-up questions asked per session
Why it's wrong here
Follow-up questions measure engagement depth, not whether users actually adopted the assistant; a pilot could show many follow-ups from a handful of testers. It is tempting because it indicates user interest, and it would suit evaluating conversational quality or satisfaction rather than breadth of adoption across the organisation.
- ✗
User satisfaction score from surveys
Why it's wrong here
Survey satisfaction measures how users feel about responses, not whether they use the assistant, so it cannot quantify adoption. It would be correct for evaluating perceived quality or experience, whereas adoption requires usage telemetry such as active users, query volume or repeat sessions.
- ✗
Average response time of the assistant
Why it's wrong here
Response time measures performance, not adoption; a fast assistant nobody uses still scores well. It is tempting because latency is easy to instrument and matters for user satisfaction, and it would be the right metric when the goal is assessing the assistant's responsiveness or infrastructure capacity rather than uptake.
- ✓
Number of unique users per week divided by total employees
Why this is correct
Weekly active users divided by total employees yields an adoption rate normalised to headcount, showing what proportion of staff actually use the assistant. This satisfies the requirement to measure adoption, unlike raw query counts or satisfaction scores.
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