Generative AI Leader Practice Question: Business Strategies for Generative AI Solutions
A financial services firm is evaluating generative AI use cases and must present a business case to its risk committee. The committee requires that each proposed use case have a measurable benefit and a clear owner before funding. Which action best aligns with a generative AI value-assessment practice?
⚠ Common exam trap
The trap here is treating experimentation volume or technical ambition as a substitute for a defined business metric and accountable owner.
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
✓
Score each use case on expected business impact, implementation feasibility, and risk, and assign a named business owner.
The committee's requirements map to a prioritized business case: quantify expected impact, assess feasibility, account for risk, and name an accountable owner. Scoring proposals on those dimensions produces comparable evidence for funding decisions. Model size, submission order, and undirected pilots do not demonstrate measurable benefit or ownership, so they fail the stated governance criteria.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✗
Select use cases based on which departments submitted requests first to ensure fair allocation of resources.
Why it's wrong here
First-come ordering is not a value criterion and can fund low-impact work while delaying high-value opportunities. The risk committee asked for measurable benefit and clear ownership, which this approach does not provide. Prioritization should consider expected impact, feasibility, and risk rather than submission order.
- ✗
Rank use cases by the size of the model they require, prioritizing the largest models for maximum capability.
Why it's wrong here
Model size is an implementation detail, not a business value measure. Prioritizing larger models can increase cost and latency without improving outcomes for a given task, and it gives the risk committee no evidence of benefit or accountability. A value assessment should tie each use case to a business metric and an accountable owner.
- ✗
Fund all proposed use cases at a small scale and let usage metrics determine which ones receive more investment later.
Why it's wrong here
Spreading funding thinly across every proposal delays value and consumes scarce engineering and governance capacity. It also defers the ownership and benefit definition the committee explicitly requested. While iterative experimentation is valuable, it should follow an initial prioritization that identifies which use cases deserve even a pilot.
- ✓
Score each use case on expected business impact, implementation feasibility, and risk, and assign a named business owner.
Why this is correct
A structured scoring model that weighs impact, feasibility, and risk gives the committee comparable evidence across proposals, while a named owner establishes accountability for outcomes. This combination directly satisfies the requirement for measurable benefit and clear ownership, and it supports transparent trade-offs when funding is limited.
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Senior Network & Security Engineer · founder of Courseiva
Last reviewed September 2026 · checked against the official Google Cloud exam blueprint
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