Generative AI Leader Practice Question: Business Strategies for Generative AI Solutions
A utility company's board approves a generative AI program and asks the program lead to present a plan showing how investment will be governed and how value will be tracked from pilot through production. The lead wants a framework that ties each initiative to a business owner, defines stage gates for continued funding, and specifies which metrics justify scaling. Which approach best meets the board's expectation?
⚠ Common exam trap
The trap here is answering a funding governance question with a technical or policy control such as model standardization or an acceptable use policy, neither of which defines ownership, stage gates, or scale-up metrics.
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
✓
Create a value realization framework that links each generative AI initiative to a business owner, defines stage-gate criteria for continued investment, and specifies scale-up metrics.
The board is asking for investment governance and value tracking across the program lifecycle. A value realization framework supplies exactly that: a named business owner per initiative, stage gates that decide whether funding continues after each phase, and pre-agreed metrics that must be met before scaling. This makes continued investment evidence-based rather than momentum-driven.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✗
Adopt an AI acceptable use policy that prohibits employees from entering confidential data into public generative AI tools.
Why it's wrong here
An acceptable use policy is valuable risk control, but it governs employee behavior rather than the funding lifecycle of the program. It does not assign business owners, define stage gates, or identify scale-up metrics, so presenting it as the program plan would not answer the board's request for how investment is governed and value is tracked.
- ✗
Standardize on a single foundation model and require every initiative to use it to simplify procurement and support.
Why it's wrong here
Model standardization can reduce operational complexity, but it says nothing about who owns each initiative, when funding should continue or stop, or which metrics justify scaling. The board asked for investment governance and value tracking, so a procurement simplification alone would leave the core questions unanswered and could force unsuitable use cases onto one model.
- ✓
Create a value realization framework that links each generative AI initiative to a business owner, defines stage-gate criteria for continued investment, and specifies scale-up metrics.
Why this is correct
The board asked for governance of investment and tracking of value across the lifecycle. A value realization framework assigns accountability, sets explicit stage gates that determine whether funding continues after each phase, and names the metrics that must be met before scaling, which is precisely the structure needed to govern a multi-initiative generative AI program from pilot to production.
- ✗
Build a centralized generative AI center of excellence that provides prompt libraries and engineering support to all business units.
Why it's wrong here
A center of excellence can accelerate delivery and share reusable assets, but by itself it does not establish business ownership per initiative, stage-gate funding decisions, or the metrics that trigger scaling. The board wants accountability and measurable value progression, which a support organization alone does not provide.
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Last reviewed September 2026 · checked against the official Google Cloud exam blueprint
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