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Start with a business problem, not a search for something to automate with AI. Identify where results miss expectations or people spend time on repetitive work, define the outcome you want to change, then compare candidate workflows by impact, feasibility, readiness, and the effort required to capture the benefit. Test the strongest candidates with a bounded pilot before committing to a wider rollout.
1. Find a measurable problem before choosing AI
Ask teams where results miss expectations, approvals take too long, or repetitive tasks consume time that could be spent on higher-value work. These are useful discovery prompts, not proof that AI is the right solution. First describe the operational problem and its consequences; then ask whether an AI-enabled change could address it.
For each idea, write a concise use-case statement naming the activity and expected result. For example: “Help the service team classify incoming requests so cases reach the right queue sooner.” Check that the task happens often enough, and matters enough, to justify investment. Microsoft’s AI strategy guidance recommends grounding opportunities in business problems and expected outcomes.
Look across functions and levels
Leadership can establish priorities and make time for the work, while employees closest to a workflow can identify its bottlenecks and practical constraints. Invite input from the functions that own, use, or support the process; cross-functional workshops are particularly useful when a workflow spans teams. OpenAI’s use-case guide stresses leadership support and employee discovery, and AWS recommends cross-functional participation for supply-chain opportunities in its supply-chain best practices.
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2. Describe candidates consistently
Before comparing ideas, capture the same information for each one. This keeps an exciting concept from receiving an unfair advantage over a less flashy but better-understood operational need.
- Owner and users: the team responsible for the workflow and the people affected by a change.
- Workflow step: what happens today, where friction occurs, and what decision or action is involved.
- Baseline: current volume, time, cost, quality, error rate, delay, or other relevant measure.
- Desired outcome: the specific improvement the business wants, such as faster resolution, fewer errors, better forecasts, or lower operating cost.
- Frequency and scale: how often the task occurs and how many cases, users, or transactions it affects.
- Human role: what people will review, decide, approve, or handle when the system is uncertain.
- Dependencies: data sources, integrations, process changes, training, and staff time needed.
Distinguish individual productivity support from business automation. Productivity support helps people work within existing tools, such as drafting or meeting preparation. Business automation changes how an organization operates or delivers value, such as routing customer requests or forecasting demand. The categories imply different adoption and process-change questions; Microsoft’s business envisioning framework discusses both.
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3. Compare value with readiness and execution effort
Use a scorecard or a simple two-axis chart to organize judgment, not to manufacture precision. Gartner’s midsize-enterprise assessment, published November 11, 2025, frames value and feasibility as important selection considerations when IT budgets are limited. Microsoft groups strategic business impact against executional fit, while AWS includes value, feasibility, strategic alignment, and data readiness. No single score proves that a candidate will perform well in production.
| Dimension | Questions to ask |
|---|---|
| Outcome impact | Could this improve cost, revenue, quality, speed, risk, insight, or decision confidence? Which outcome matters most to the company now? |
| Strategic fit | Does the use case advance a stated company priority or strengthen an existing business capability? |
| User need and adoption | Do affected users want the change? Can the workflow realistically change, and who will be responsible for using or reviewing the result? |
| Technical and data feasibility | Are necessary data available, sufficiently ready and consistent, and accessible through workable integrations? |
| Effort and change burden | What build-or-buy choices, deployment work, staff capacity, training, process changes, and ongoing oversight are required? |
| Evidence quality | Is there a credible baseline, a measurable outcome, and a pilot that can answer a decision-relevant question? |
Gartner’s January 20, 2026 guidance identifies possible productivity impact through work quantity, work quality, work scope, insights, and decision confidence. It also advises tying initiatives to an existing business outcome and baseline. These are ways to frame potential impact, not promised results for any company. See Gartner’s guidance on near-term financial impact.
Rank #3
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4. Sort the shortlist into action paths
Impact and effort help distinguish work worth testing now from work that needs more preparation. Keep strategic importance separate from near-term readiness: a difficult candidate may deserve incubation rather than rejection, while an ambitious vision does not establish that a project is feasible.
| Candidate profile | Practical next step |
|---|---|
| High impact, low effort | Prioritize for a focused proof of concept or minimum viable implementation, subject to data, risk, and adoption checks. |
| High impact, high effort | Research or incubate: resolve major data, integration, process, or governance uncertainties before a full commitment. |
| Lower impact, low effort | Consider only if it supports a broader priority or offers a useful, low-risk learning opportunity. |
| Lower impact, high effort | Usually defer while capacity is limited; revisit if business priorities or feasibility change. |
Microsoft’s prioritization framework uses paths such as shelve, research, incubate, and accelerate to an MVP. Treat these as decisions to revisit, not permanent labels: capabilities, data readiness, costs, and organizational capacity can change. Its worked examples—including store operations assistance, a shopping application, and inventory management—illustrate how to compare ideas; they are not evidence of guaranteed returns. See Microsoft’s business envisioning framework.
Rank #4
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5. Run a bounded proof of concept
Choose the smallest test that can establish whether the proposed approach is technically workable and valuable in the actual workflow. Match its scope to the organization’s AI maturity, set success criteria and required resources in advance, and state what result would lead to stopping, revising, or proceeding.
- Set the question: name the uncertainty the pilot must resolve, such as whether a model can classify a defined type of request accurately enough for human review.
- Fix the baseline and measures: record the current process and choose outcome measures that reflect the business objective, not just model activity.
- Bound the test: specify users, workflow, data, duration, human oversight, and any cases the system must not handle autonomously.
- Agree decision criteria: decide in advance what evidence supports stopping, adjusting the workflow, extending the test, or planning a broader deployment.
- Review results with users and owners: include integration, quality, adoption, risk, and operating requirements alongside the measured outcome.
For an organization still building experience, an internal, non-customer-facing project can limit exposure while teams learn. Microsoft’s adoption planning guidance recommends proofs of concept to refine prioritization and implementation plans.
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6. Connect productivity gains to captured value
A pilot may show that a task takes less time, but time saved is not automatically a financial benefit. Determine how the organization will use the capacity: for example, to handle more work, improve service, avoid planned hiring, change staffing, increase revenue, or reduce spending. Include any process redesign, role changes, or vendor costs needed to realize the gain.
Compare pilot results with the baseline and the outcome that justified the project. Gartner’s 2026 guidance specifically asks how productivity gains will convert into financial benefit. If the project cannot identify a credible path from the measured improvement to a business result, treat that gap as an unresolved assumption rather than a realized return.
Questions to use in a prioritization meeting
- Which workflow has the most consequential, measurable friction?
- What result should improve, and what is the current baseline?
- Do we have usable data and a workable path to integrate it?
- Will affected users adopt the changed workflow, and what human review remains necessary?
- What is the smallest pilot that could prove or disprove the business case?
- If the pilot works, how will the organization capture the gain?
There is no universally best AI use case or universal financial threshold for proceeding. The strongest candidate for a particular mid-market company is the one that addresses a meaningful outcome, is feasible enough to test responsibly, and has a credible path from evidence to value.
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