Generative AI Leader Practice Question: Business Strategies for Generative AI Solutions
A retail chain's leadership wants to understand how generative AI could improve its customer service operations but is unsure where to start. They ask a cloud consultant to identify candidate use cases, estimate potential value, and flag which ones are realistically achievable with current technology. Which activity should the consultant perform first?
⚠ Common exam trap
The trap here is equating progress with building something, so the first instinct becomes fine-tuning a model or deploying a chatbot instead of first identifying and prioritizing which use cases deserve investment.
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
✓
Run a generative AI use case discovery workshop with business stakeholders to inventory candidate scenarios and prioritize them by value and feasibility.
The consultant should begin with structured use case discovery, because the organization needs an inventory of candidate scenarios ranked by business value and technical feasibility before committing to any build or procurement. This produces the prioritized shortlist that later pilots, model choices, and investment decisions can be based on, aligning generative AI work with measurable business outcomes.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✗
Fine-tune a foundation model on the retailer's historical customer service transcripts and measure its response quality.
Why it's wrong here
Fine-tuning is an implementation activity that presupposes a chosen use case, and it consumes data preparation and compute budget before anyone has agreed on what problem to solve. Leadership asked for candidate use cases and value estimates, not a trained model, so starting here would spend effort on an unvalidated scenario and skip the prioritization the organization actually needs.
- ✓
Run a generative AI use case discovery workshop with business stakeholders to inventory candidate scenarios and prioritize them by value and feasibility.
Why this is correct
Discovery workshops bring business and technical stakeholders together to enumerate candidate scenarios, then rank them by expected business value and technical feasibility. This directly answers leadership's request for candidate use cases, value estimates, and realism checks, and it produces a prioritized shortlist that later pilots can draw from, which is the standard first move in a generative AI business strategy engagement.
- ✗
Deploy a general-purpose chatbot on the public website and monitor engagement metrics for one quarter.
Why it's wrong here
Launching a chatbot without a defined use case, success criteria, or feasibility assessment generates operational risk and produces engagement data that cannot be attributed to a deliberate strategy. It also puts customer-facing quality at stake before the retailer has decided which scenarios matter, which is the opposite of the structured discovery leadership asked the consultant to lead.
- ✗
Migrate the retailer's contact center platform to a new SaaS vendor that advertises built-in generative AI features.
Why it's wrong here
Replacing the contact center platform is a large, irreversible commitment made before the organization understands which generative AI scenarios are valuable or feasible. It conflates a procurement decision with use case discovery, and it locks the retailer into one vendor's capabilities instead of producing the neutral, prioritized inventory of opportunities that leadership requested.
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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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