An AI use case is ready to compare when you can describe the work, the intended improvement and the evidence behind it. “AI for operations” is an area to explore. “Prepare a draft quote from an approved request for an estimator to review” is a candidate you can assess.
A practical prioritisation framework should help a leadership team decide what to investigate, test or defer. Start with comparable descriptions, apply non-negotiable gates, then weigh value against the work and uncertainty involved.
Describe every use case in the same way
Create a short record for each candidate:
- Business problem and the outcome that needs to improve.
- Process owner and people affected.
- Trigger, inputs, proposed AI task and finished output.
- Current volume, handling time, quality and exception rate.
- Systems, information sources and access needed.
- Human review, escalation and fallback arrangements.
- Simpler alternatives worth comparing.
- Evidence available, assumptions and unresolved questions.
Keep the boundary narrow enough to test. A tool that drafts a quote and a system that commits to pricing without review have different operating consequences. They should not share one assessment simply because both involve quotations.
If the current workflow is unclear, map it before estimating an improvement. Record whether each baseline comes from system data, observation or a person's estimate.
Check the gates before ranking
Some issues need a decision before a candidate can enter a delivery shortlist:
- Ownership: someone can approve the process change and remain accountable for its outcome.
- Information use: the intended data access and use can be approved, with restrictions understood.
- Acceptable operation: foreseeable failures, affected people, review requirements and recovery arrangements have been assessed by the appropriate owners.
- Evaluation: there is a credible way to judge whether the proposed output and workflow are good enough.
A failed gate does not automatically mean abandoning the idea. It may mean funding discovery, obtaining an access decision or changing the scope. Label it accordingly. A high potential benefit should not conceal an unresolved prerequisite.
NIST's AI Risk Management Framework treats context, risk measurement and risk response as connected activities. A prioritisation worksheet can support that discussion, but cannot certify that a system is safe or compliant. NIST AI RMF Core.
Compare evidence across six dimensions
For candidates that clear the relevant gates, compare these questions side by side:
- Business value: which agreed objective improves, by how much, and for whom?
- Process fit: does the proposed task address a material constraint in the end-to-end workflow?
- Information readiness: are representative, permitted inputs and reliable reference material available?
- Delivery effort: what integration, testing, support and ongoing operating work is required?
- Adoption readiness: can the people affected use and review the changed workflow in practice?
- Time to useful evidence: what would it take to test the most important assumption?
For each answer, record the evidence, its confidence and the next uncertainty to resolve. Microsoft also includes business impact, technical feasibility and user desirability in its use-case planning guidance. Those dimensions are useful prompts; they do not remove the need for organisation-specific judgement. Microsoft business planning guidance.
If your portfolio needs scores, define the scale and any weights before assessing candidates. Keep the underlying explanations visible. Two ideas can have the same total while one depends on unavailable data and the other is ready for a controlled test. Do not let arithmetic make that distinction disappear.
Estimate net value rather than gross time savings
Start with a simple time model:
Eligible monthly cases × expected use rate × net minutes saved per used case ÷ 60 = potential monthly hours released.
“Net minutes saved” must include checking, corrections and exception handling. Work that shifts to another team still counts. Treat the estimate as a hypothesis until measured.
For example, a fictional facilities-services company receives 240 eligible quote requests a month. Preparing and checking a quote currently takes 18 minutes. The team hypothesises that an assisted workflow, including review and correction, would take 11 minutes and be used for 75% of eligible requests.
The estimate is 240 × 0.75 × 7 ÷ 60 = 21 hours a month. These are invented planning assumptions, not observed results or industry benchmarks.
The estimate excludes implementation effort, software charges, support and training. It is also released capacity, not automatically cash savings. Before assigning a financial value, explain how the time would be redeployed or an actual cost avoided. Assess quality or service benefits separately, using measures that can be tested.
Choose between the next tests
Suppose the same fictional company has three ideas:
- Draft quotes for estimator review. Approved price information exists, and historic requests can support an offline test. The main unknown is review effort.
- Answer every customer enquiry automatically. Requests span several teams, reference material conflicts, and an incorrect commitment could affect a customer. The first task is to narrow scope and resolve content ownership.
- Route complete requests to a named team. The routing rules are already explicit. Compare a conventional workflow change before assuming AI is needed.
The quote-drafting idea is a reasonable first test in this scenario because its key uncertainty can be investigated within a bounded workflow. It has not yet earned a full rollout. The other ideas each have a useful next action rather than simply receiving a lower score and being forgotten.
Turn the shortlist into a decision
For the chosen test, name the owner, permitted inputs, operating boundary and review requirements. State the baseline, quality criteria, unacceptable failures and the evidence needed to continue, revise or stop. Include representative difficult cases; do not select only examples that make the demonstration look successful.
Assign the remaining opportunities an action and a review condition: investigate an uncertainty, resolve a dependency, test a simpler alternative or defer until a business need changes. This creates a usable delivery sequence while preserving promising ideas.
Hyperius helps organisations shape opportunities into practical use cases, pilots and adoption plans. Discuss your priority AI use case if you need an evidence-backed way to decide what to test first.