AI Use Case Prioritization: How to Find Opportunities Worth Piloting

AI use case prioritization is a management decision before it is a technology decision. The question is not where AI could be used. It is which business problem deserves attention, whether AI is appropriate, and what evidence would justify a pilot. A disciplined process helps leaders avoid attractive demonstrations that never translate into useful changes in how work gets done.

At Thinking Oak, our recommendation is to start with a small portfolio of well-defined opportunities, not an organization-wide request for AI ideas. The objective is a defensible shortlist: each opportunity has an accountable owner, a measurable outcome, and a realistic path to testing.

Find opportunities in workflows, not wish lists

Asking employees how they would use AI often produces broad answers: write faster, summarize meetings, or improve customer service. These are starting points, not decision-ready use cases.

Instead, select three to five workflows connected to current business priorities. Examples include preparing customer proposals, resolving service requests, reviewing supplier documentation, and assembling monthly management reports.

For each workflow, combine three discovery methods:

• Interview the process owner about cost, delays, quality issues, and the decisions that matter.
• Observe someone doing the work, including handoffs, rework, and unofficial workarounds.
• Review representative work samples and available operational data to test whether the reported problem is recurring and material.

Ask specific questions: Where do people search for information? What gets copied between systems? Which drafts require repeated corrections? Where does work wait for judgment or approval?

Also ask whether AI is necessary. A clearer template, better knowledge organization, or a conventional workflow rule may solve the problem more reliably. Discovery should identify the best intervention, not manufacture an AI justification.

Turn each opportunity into a comparable brief

Before scoring, describe every candidate in the same format. Otherwise, a polished presentation can outrank a less exciting but more valuable problem.

A one-page opportunity brief should capture:

• Business outcome: the specific delay, cost, quality issue, or service problem to address.
• Workflow boundary: the trigger, inputs, proposed assistance, human review, and final output.
• Users and owner: who performs the work and who is accountable for its outcome.
• Baseline: current volume, handling time, error rate, or another relevant measure.
• Information requirements: source material, access restrictions, quality concerns, and ownership.
• Pilot hypothesis: the change to test, with explicit limits and success criteria.

For example, replace “AI for proposals” with “Test whether account managers can prepare a first proposal draft from approved service descriptions with less drafting time, without increasing reviewer corrections.” That statement is narrow enough to investigate and measurable enough to challenge.

Use an AI use case prioritization scorecard

A scorecard makes assumptions visible. It should support executive judgment rather than create the appearance of mathematical certainty.

Score each dimension from one to five, with five always representing the more favorable condition. Set weights before reviewing candidates. A practical starting point is:

• Business value, 30%: relevance to a business priority and plausible magnitude of benefit.
• Workflow suitability, 20%: a bounded task with accessible inputs and outputs people can evaluate.
• Information readiness, 20%: relevant material is available, sufficiently reliable, and appropriately accessible.
• Adoption readiness, 15%: an engaged owner, willing users, and capacity to change the workflow.
• Pilot feasibility, 15%: a useful test can be run within agreed time, cost, and operating constraints.

Define scoring anchors. For information readiness, a one might mean sources are unknown or inaccessible; a three, identified sources with unresolved gaps; a five, representative material with confirmed access and ownership.

Calculate the weighted score, but show evidence confidence separately as high, medium, or low. An opportunity supported only by interviews should not look as certain as one supported by observed work and baseline data.

Treat risk as a gate, not merely another weighted factor. High potential value must not cancel out unresolved access restrictions or consequences of an incorrect output. Candidates involving sensitive information or consequential decisions need appropriate review before a pilot proceeds.

Build a shortlist, not a league table

Review the scores with business owners and the relevant technology, security, privacy, or legal stakeholders. Challenge assumptions rather than debate decimal places.

Place candidates into four groups:

• Pilot-ready: sufficient evidence, accountable ownership, and manageable boundaries.
• Discovery needed: promising value, but important assumptions remain untested.
• Prerequisite work: information, process, access, or ownership gaps must be addressed first.
• Not now: limited value, excessive complexity, or a better non-AI alternative.

Select a small number of pilots that the organization can support. Avoid choosing several that depend on the same unavailable specialists or compete for the same users. Portfolio capacity matters as much as individual scores.

Make the first decision reversible

For each selected opportunity, agree on a pilot charter: scope, duration, participants, approved information, review responsibilities, baseline, success measures, and stop conditions.

Measure the whole workflow. Faster drafting is not a meaningful improvement if verification and correction consume the time saved. Include review effort, output quality, user acceptance, and exception handling alongside speed.

Only then consider implementation capabilities, which might include Microsoft 365 Copilot, Copilot Studio, or Power Platform, depending on the task and constraints.

The executive decision at the end is not automatically to expand. It is to proceed toward implementation readiness, revise the concept, resolve prerequisites, or stop. Good discovery makes all four outcomes useful: it directs investment toward evidence and keeps enthusiasm from becoming an obligation.