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Generative AI Leader Practice Question: Evaluate the ROI of deploying a GenAI tool for…
A company wants to evaluate the ROI of deploying a GenAI tool for customer support. They plan to measure productivity gains. Which metric is most directly tied to productivity improvement?
⚠ Common exam trap
Google often tests the distinction between efficiency metrics (like AHT) and effectiveness or automation metrics (like deflection or CSAT), trapping candidates who confuse 'productivity' with 'customer satisfaction' or 'automation rate'.
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
✓
Average handling time per ticket
Average handling time (AHT) per ticket is the most direct metric for productivity improvement because it quantifies the time an agent spends resolving a customer issue. A GenAI tool that generates response drafts or retrieves knowledge base articles reduces the agent's wrap-up and research time, directly lowering AHT. This is a standard contact center metric (e.g., measured in seconds or minutes) that maps to cost savings and throughput gains.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✗
Total token consumption
Why it's wrong here
Token consumption measures model usage and cost, not the productivity improvement the ROI evaluation requires. It is tempting because tokens drive GenAI operating expense, making them the right metric when forecasting inference cost or capacity planning rather than measuring agent output.
- ✓
Average handling time per ticket
Why this is correct
Average handling time per ticket directly quantifies the time saved per support interaction, making it the clearest proxy for productivity gains. Reduced handling time translates into more tickets resolved per agent, which is the productivity improvement the ROI evaluation requires.
- ✗
Ticket deflection rate
Why it's wrong here
Ticket deflection rate measures self-service containment, not the productivity gain of agents using the GenAI tool. It is tempting because deflection genuinely reduces support workload, making it the right metric when evaluating chatbot containment or reducing inbound contact volume.
- ✗
Customer satisfaction score (CSAT)
Why it's wrong here
CSAT measures customer sentiment, not output per agent, so it cannot quantify productivity gains from the GenAI tool. It is tempting because satisfaction is a genuine support KPI, and it would be the right choice when evaluating service quality or customer experience impact rather than efficiency.
Visual reference
About these practice questions
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JA
Written by Johnson Ajibi, MSc IT Security
Senior Network & Security Engineer · founder of Courseiva
This Generative AI Leader practice question is part of Courseiva's free Google Cloud certification practice question bank. Courseiva provides original exam-style practice questions with explanations, topic-based practice, mock exams, readiness tracking, and study analytics to help learners prepare for the Generative AI Leader exam.