How to choose a generative AI use case
Start with the business outcome, not the technology. Google Cloud’s current guidance recommends working backward from measurable goals such as employee efficiency or customer satisfaction. Define the user, the existing process, the bottleneck and the evidence that would justify changing it.
Generative AI is a good candidate when the work involves unstructured language or media, many acceptable outputs, and a human-readable interface. Deterministic calculations, fixed rules and stable integrations may be cheaper and safer with conventional software. A hybrid often works best.
1–2. Knowledge search and research synthesis
First, an internal knowledge assistant can retrieve approved policies, manuals and project documents, then answer questions with citations. The difficult work is permissions, document freshness and abstaining when no source supports an answer. Measure retrieval success and verified answer accuracy, not chat volume.
Second, a research assistant can map external sources, summarize long reports and produce claim tables. It should preserve links, dates and definitions so an analyst can verify the synthesis. The accepted deliverable is not ‘a long report’; it is a report whose important claims survive source review.
3–4. Customer service and employee assistance
A customer-service copilot can summarize case history, retrieve policy and draft a reply for an agent. Start in assistive mode and measure handling time, correction rate and customer outcome. Escalation rules matter because a warm, incorrect answer can be more damaging than a slower human response.
An employee assistant can answer HR, IT and operations questions or prepare routine documents. Scope the source collection carefully and separate guidance from transactions. Resetting access, changing payroll or approving leave should require authenticated systems and explicit authority, not only a convincing conversation.
5–6. Software development and data analysis
Coding assistants can explain repositories, draft tests, implement bounded changes and review patches. Value appears when they shorten accepted delivery, not when they generate more code. Require repository instructions, automated tests, security review and a reversible change set.
Data-analysis assistants can translate questions into queries, explain metrics and assemble reports. The semantic layer is critical: the system must know which table and definition represent revenue, active users or churn. Reconcile outputs to a trusted dashboard before enabling automated distribution.
7–8. Document operations and content production
Document workflows include extracting fields, comparing versions, classifying correspondence and drafting material from approved templates. Combine language models with structured output and deterministic validation. Sensitive contracts, applications and medical documents require privacy controls and expert review.
Content systems can generate campaign variants, product descriptions, localization drafts and creative concepts. Quality depends on brand evidence, rights-aware source assets and an editorial process. Measure accepted assets and production time; raw output count rewards volume rather than usefulness.
9–10. Product discovery and operational agents
Conversational product discovery can translate a shopper’s intent into filters, explanations and comparisons. Ground claims in catalog data, preserve price and availability timestamps, and test whether recommendations serve the user rather than only maximizing conversion.
Operational agents can coordinate multi-step work such as updating a case, drafting a response and scheduling a follow-up. Begin with recommendation mode, narrow permissions and approval before external or irreversible actions. Logs, idempotency and recovery are product requirements, not backend details.
A production scorecard
For every candidate, record the baseline time and quality, target user, required data, error cost, approval points and owner. Run a limited pilot on representative cases. Measure acceptance rate, correction time, latency, cost, user outcome and performance across relevant segments.
Promote the use case only when the complete system beats the existing process and the organization can monitor it. Generative AI creates value by improving a defined experience or decision. A broad mandate to ‘add AI’ creates demos; a narrow, evidenced workflow creates an operating capability.
Sources & further reading
Social-media activity is treated as a signal of attention, not proof. Product claims are attributed to the linked publisher or announcement.