GCP GenAI Leader case studies
GCP GenAI Leader · Put your knowledge to work in context.
The Google Cloud Generative AI Leader credential is a foundational, non-coding certification for people who need to reason about generative AI in a business context rather than build models themselves. It suits product managers, solutions consultants, sales and marketing leads, and IT managers who are expected to spot opportunities, evaluate trade-offs, and govern adoption without writing Python. The GCP GenAI Leader case studies are 5 cases and about 3,648 words.
1Contact Center Triage: Separating Model Limits from Engineering Mistakes
A regional utility's contact center handles 42,000 support tickets a month. A two-person innovation team built a prototype on a large Gemini Pro model: every request pastes the full 60-page billing policy manual into the prompt, sets temperature to 1.0, and asks for a ticket category plus a drafted reply.
704 words, about 4 min2Retail Knowledge Assistant: Managed Search Versus a Custom Retrieval Pipeline
A retail chain with 1.2 million product and operations documents in eight languages wants an internal assistant for store managers and a separate customer-facing answer bot. Roughly 30% of the corpus is scanned PDFs inherited from acquired regional brands.
661 words, about 3 min3Contract Extraction: Choosing Between Prompting, Retrieval, Tuning, and Distillation
A legal operations team wants structured extraction - parties, term, termination notice period, governing law - from 40-page commercial contracts. A generic prompt on a large Gemini Pro model yields 62% field-level F1.
761 words, about 4 min4Insurer's Pilot to Platform: Governance Without a Bottleneck
An insurer with 4,000 employees ran a three-month generative AI pilot with three teams. Results are mixed: 12% weekly active users, two teams report meaningful time savings, but legal discovered engineers pasting claims data into a public chatbot, finance found one team's Vertex AI spend grew eightfold in a single week with no alerting, and departments are now procuring their own tools independently.
752 words, about 4 min5Bank Assistant: Over-Refusal, Fabricated Policy, and a Request to Automate Credit Decisions
A bank deploys an internal assistant for 900 loan officers and underwriters, built on Gemini with Vertex AI safety filters left at default settings. Within two weeks, support tickets show legitimate requests being refused - 'summarize why this application was declined', 'list the fraud indicators in this file' - while a red-team exercise got the model to produce a plausible but invented internal policy number.
770 words, about 4 min