Generative AI Leader Practice Question: Business Strategies for Generative AI Solutions
A logistics firm wants every generative AI proposal to be judged on whether it reduces cost per shipment. Leadership asks the AI team to define the metric before any project starts. Which practice does this illustrate?
⚠ Common exam trap
The trap here is mistaking a technical or governance activity for a business value definition, when value must be expressed in an operational outcome the business already tracks.
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
✓
Establishing a measurable business outcome tied to an existing operational key performance indicator.
Value-driven generative AI programs start by naming the business outcome and linking it to a metric the organization already measures, such as cost per shipment. That anchor lets leadership compare proposals, size expected returns, and confirm impact after launch. Model benchmarks, egress savings, and governance approvals are supporting concerns rather than definitions of business value.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✗
Selecting a foundation model based on its published benchmark scores.
Why it's wrong here
Benchmark scores describe general model capability, not the financial or operational impact of a specific deployment. A model can top a leaderboard and still fail to lower cost per shipment if it is expensive, slow, or poorly matched to the routing task, so this choice addresses model selection rather than business value definition.
- ✗
Adopting a responsible AI review board to approve all generative AI use cases.
Why it's wrong here
A review board governs risk and compliance, which is valuable but orthogonal to proving economic benefit. Approval does not tell the firm whether a proposal reduced cost per shipment, so relying on governance alone leaves the value question unanswered and the portfolio unrankable.
- ✗
Choosing a deployment region that minimizes network egress charges.
Why it's wrong here
Regional placement affects infrastructure spend, which is only one input into cost per shipment and often a minor one. Optimizing egress does not establish whether the generative AI capability itself creates value, and it cannot serve as the shared success criterion leadership asked the team to define.
- ✓
Establishing a measurable business outcome tied to an existing operational key performance indicator.
Why this is correct
Tying the generative AI effort to cost per shipment connects the initiative to an operational metric the business already tracks and trusts. That makes value demonstrable, comparable across proposals, and defensible to finance, which is exactly what defining the metric before work begins is meant to achieve.
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Written and reviewed by Johnson Ajibi, MSc IT Security
Senior Network & Security Engineer · founder of Courseiva
Last reviewed September 2026 · checked against the official Google Cloud exam blueprint
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