Generative AI Leader Practice Question: Business Strategies for Generative AI Solutions
A company is considering using gen AI for customer support. Which two business strategies are most important for success?
⚠ Common exam trap
Google Cloud often tests the misconception that cost optimization (cheapest model) or speed-to-market (deploy without testing) are primary success factors, when in reality governance, safety, and continuous measurement are the foundational strategies for sustainable gen AI adoption.
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
✓
Measure customer satisfaction metrics
Measuring customer satisfaction metrics (A) is critical because it provides quantitative feedback on the generative AI system's performance, enabling iterative improvements to the model's responses and alignment with business goals. Without metrics like CSAT or NPS, the company cannot validate whether the AI is reducing resolution time or improving user experience, which are key ROI indicators for gen AI 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.
- ✓
Measure customer satisfaction metrics
Why this is correct
Metrics help evaluate success and guide improvements.
- ✗
Ignore data privacy
Why it's wrong here
Data privacy is legally required and builds customer trust.
- ✗
Deploy without testing
Why it's wrong here
Untested deployment can lead to customer dissatisfaction and reputational risk.
- ✓
Ensure human-in-the-loop for critical interactions
Why this is correct
Human oversight ensures quality and handles edge cases.
- ✗
Use the cheapest model
Why it's wrong here
Cost should not be the sole factor; model quality is critical.
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