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Research and business strategy

Use case assessment

Assess a technology or operational use case, including capability, solution landscape, evidence of value, user implications, design options, and data readiness. Use for AI or technology research, use-case analysis, vendor landscape, feasibility assessment, advisory research, a point of view, or an executive briefing. Produces a structured cited analysis; it does not select, purchase, deploy, or configure a solution.

Works with the context you provideVersion 3.0.0Historical revision

# Use-case assessment

Use this method to assess whether a proposed technology or operating change is worth pursuing and what would have to be true for it to work. Choose the audience before researching: client deliverable, internal knowledge, thought-leadership seed, or executive briefing.

Scope

If the primary question is who to target, use the ideal-customer-profile method. This method may consume a completed targeting decision, but does not create a competing one. It can assess options, but it does not make an unapproved vendor selection or implementation change.

Method

  1. Define the use case, decision, users, workflow, existing systems, operating or regulatory constraints, geography, and audience.
  2. Build a research plan that states the questions for capability, solution landscape, user impact, value evidence, design paths, data readiness, and other material constraints. Use supplied evidence first and name assumptions that need public research.
  3. Research independent sections in parallel when the runtime permits; otherwise keep the same source protocol serially. Capture URLs, publication dates, and confidence as evidence is gathered.
  4. Assess actual capability boundaries: what the technology can do, failure modes, human oversight, integration requirements, security or privacy implications, and what has not been demonstrated in the stated context.
  5. Compare solution paths, including build, buy, adapt, and defer where relevant. Separate vendor claims from attributable customer or independent evidence.
  6. Analyze value using measured outcomes where available. Keep ROI, labor savings, accuracy, and adoption assumptions visible; do not turn a case study into a forecast.
  7. Assess data readiness only when data quality, access, ownership, governance, or integration materially affects feasibility.
  8. Synthesize a recommendation, constraints, decision criteria, and the smallest next validation action. Write the executive summary last so it only compresses supported findings.

Output

```markdown # [Use case] assessment ## Executive summary ## Decision and scope ## Technology capability assessment ## Solution landscape ## User and workflow implications ## Value and evidence ## Design paths ## Data readiness ## Risks, constraints, and next validation ## Glossary ## Sources ```

Quality rules

  • Cite every non-trivial external claim inline and include a source list with a descriptor.
  • Use plain language for the selected audience and define jargon on first use.
  • Treat evidence of possibility as distinct from evidence of viability in this organization or workflow.
  • Do not publish, procure, deploy, configure, or write a host record; return a proposed handoff for those actions.