generative AI use cases: Identify generative AI use cases by mapping language, knowledge, content and interaction tasks to measurable business problems. Look for high effort, delay, inconsistency or unmet demand, then test suitability, information readiness, review and risk.
Contents
- Direct answer
- Observe work before discussing models
- Find the real bottleneck
- Use eight selection tests
- Compare with simpler options
- Balance the portfolio
- Checklist
- CEO perspective
- FAQs
- References
generative AI use cases: the direct answer
Identify generative AI use cases by mapping language, knowledge, content and interaction tasks to measurable business problems. Look for high effort, delay, inconsistency or unmet demand, then test suitability, information readiness, review and risk.
Observe work before discussing models
Interview employees and follow documents, questions, decisions and handoffs. Candidate areas include drafting, summarization, search, translation, coding help, service assistance and knowledge retrieval. Ask what happens before and after the task.
Record the intended use, baseline, owner, permitted information, evaluation method, main risks and next review date. This short decision record prevents assumptions from disappearing when a demonstration becomes a live workflow.
Find the real bottleneck
Saving ten minutes in drafting creates little value if review, approval or system entry causes the delay. Measure the complete workflow. State the baseline, frequency, problem owner and expected improvement.
Use eight selection tests
Review strategic fit, problem size, frequency, information readiness, evaluation method, integration effort, user readiness and potential harm. Strong first cases have bounded output, available reviewers and evidence that can be gathered quickly.
Test the difficult cases, not only the average one. Include unclear instructions, incomplete information, unusual users and periods of high demand. Leaders need to know how the service fails and how people recover before broad release.
Compare with simpler options
A process change, search improvement or standard automation may solve the problem at lower cost and risk. Generative AI should earn its place against credible alternatives, not only against the current frustration.
Balance the portfolio
Include near-term productivity cases and a smaller number of strategic workflow tests. OECD research found only about 29% of surveyed SME users applying generative AI in core activities. Higher-value core use may be possible, but needs stronger evidence and control.
Before the next investment, compare evidence from real work with the original claim. Review value, adoption, full cost, output quality, human checking, employee experience and incidents. A strong result in one area does not cancel a serious weakness elsewhere.
Operational ownership matters after launch. Name the person who can pause the service, approve a material change, respond to an incident and decide whether continuing cost remains justified. Document model or vendor changes, because yesterday’s evaluation may no longer describe today’s service.
Executive checklist
- Observe the current workflow.
- Measure the real bottleneck.
- Score fit, readiness and risk.
- Confirm reviewers and evidence.
- Compare non-AI alternatives.
- Select a balanced portfolio.
A perspective from Praevion Consulting Inc.
“A useful generative AI use case begins with a stubborn piece of work, not a clever prompt. The value appears only when the full workflow becomes better.”
Mehrzad Verdizadegan,
CEO, Praevion Consulting Inc.
Related guidance
Frequently asked questions
What makes a good first use case?
A clear owner, measurable problem, bounded output, available reviewers and manageable risk.
Should customer-facing cases come first?
Usually only after the organization proves evaluation, support and control in lower-consequence work.
How many use cases should be piloted?
Choose only as many as the organization can support, measure and decide on properly.
Executive takeaway
Translate this issue into a named business outcome, accountable owner, evidence threshold and review cycle. Advance to scale only when value, adoption, operational readiness and risk evidence support the next investment decision.
To discuss your needs, contact Praevion Consulting Inc..
