The Tool Desk
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Start with a measurable problem, not an AI idea
Ask where results miss expectations, where approvals or handoffs slow work, and where people spend time on repetitive tasks. These prompts help uncover friction without assuming AI is the answer. Microsoft’s AI strategy guidance recommends turning a problem into a concise use-case statement that names the activity and intended result, and checking that the task happens often enough to justify investment.
Describe the problem in business terms first: for example, “Customer requests are routed manually, delaying responses,” rather than “We need an AI chatbot.” The first statement leaves room to compare AI with process changes, rules-based automation, or doing nothing. It also gives the team a result to measure.
Find opportunities across the organization
Executives can set priorities and make exploration legitimate, but employees closest to the work often know where the friction is. Invite people from affected functions, IT, operations, finance, risk, and customer-facing teams to surface and refine ideas. OpenAI’s use-case guide recommends leadership support alongside employee discovery and cautions that complex projects can slow early progress. For supply-chain opportunities, AWS similarly recommends cross-functional workshops in its supply-chain best practices.
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Keep the first inventory broad, but do not confuse idea collection with approval. Each idea still needs a defined workflow, a plausible outcome, and a way to test whether AI improves it.
Describe each candidate consistently
Use a one-page record or shared spreadsheet so the shortlist can be compared fairly. For each proposed use case, capture:
- Owner and users: the team that owns the process and the people whose work would change.
- Workflow step: where the task begins and what happens before and after it.
- Problem and baseline: the current delay, cost, error rate, volume, or other relevant measure, if available.
- Desired outcome: the change that would matter to the business, such as faster handling, fewer errors, lower cost, or better decisions.
- Frequency and volume: how often the task occurs and how much work is involved.
- Data and dependencies: what information, systems, integrations, approvals, and controls are required.
- Human role: which judgment or action remains with a person, and who is accountable for the result.
Also distinguish individual productivity assistance from business automation. Microsoft’s strategy guidance describes individual assistance as improving work inside existing tools, such as writing support or meeting preparation; business automation changes how the organization operates or delivers value, such as customer routing or demand forecasting. The distinction helps teams estimate adoption, integration, and process-change needs.
Compare value with readiness and execution fit
There is no universal score or threshold that makes a use case “high value.” A practical shortlist weighs expected business impact against the company’s ability to deliver and capture that impact. Gartner’s January 2026 guidance emphasizes tying work to an existing business outcome and baseline; Microsoft and AWS also include strategic fit, user need, feasibility, and data readiness in their prioritization guidance.
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| Dimension | Questions to ask |
|---|---|
| Outcome impact | Could this improve cost, revenue, quality, speed, risk, insight, or decision confidence? Which outcome is most important to the company now? |
| Strategic fit | Does the use case advance a stated priority or strengthen an important business capability? |
| User need and adoption | Do affected users want the change? Can they realistically adopt a revised workflow? |
| Technical and data feasibility | Are the required data available, usable, and consistent? What integrations, evaluation, and human review would be needed? |
| Effort and change burden | Can the company support the work alongside existing priorities? What deployment, process, staffing, or vendor changes are required? |
| Evidence quality | Is there a baseline, a measurable result, and a focused test that could change the investment decision? |
Microsoft’s business envisioning framework groups strategic business impact and executional fit as two organizing axes. Treat them as a way to structure discussion, not as a validated formula for returns. Its worked examples—including store operations assistance, a shopping application, and inventory management—illustrate evaluation, not guaranteed results for another company.
Gartner’s 2026 article identifies possible productivity impacts such as work quantity, work quality, work scope, insights, and decision confidence. Those categories can help teams spot outcomes beyond minutes saved, but the measure should fit the use case and its business purpose.
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Sort the shortlist into action paths
Use a simple impact-versus-effort view or a documented scorecard, and write down the assumptions behind each judgment. Microsoft’s framework suggests different paths based on impact and execution fit:
- High impact, strong execution fit: accelerate toward a minimum viable product or bounded pilot.
- High impact, weak execution fit: research or incubate the idea; investigate data gaps, integrations, skills, and process changes rather than rejecting it automatically.
- Low impact, strong execution fit: consider it only if it is inexpensive and useful, but do not let ease alone make it a priority.
- Low impact, weak execution fit: shelve it unless the underlying business case changes.
Revisit classifications when data improves, capabilities change, costs shift, or company priorities move. A strategic bet may become practical later, while a former quick win may lose relevance.
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Test a bounded hypothesis before scaling
Choose a pilot that is narrow enough to evaluate and appropriate to the organization’s AI maturity. State the hypothesis, baseline, success criteria, duration, owner, and resources before starting. Microsoft’s adoption planning guidance recommends using proof-of-concept results to refine prioritization and implementation plans; it also suggests internal, non-customer-facing projects as a way to constrain risk while getting started.
- Set the decision the pilot must inform. Specify what evidence would justify scaling, revising, or stopping the effort.
- Measure the current workflow. Record the baseline that corresponds to the intended outcome, not just a convenient activity metric.
- Run the smallest representative test. Use realistic data and include the integrations, human review, and exception handling that matter to the workflow.
- Compare results and costs. Assess the change against the baseline and account for implementation, oversight, and process changes needed to capture benefits.
- Decide and document. Scale, adjust, or stop based on what the test established; update the shortlist with what the team learned.
Count captured value, not just time saved
A pilot that shows faster task completion does not by itself prove financial value. Gartner’s January 2026 guidance asks organizations to consider how productivity gains will turn into financial benefit. That may require changed workflows, roles, staffing plans, revenue capacity, or vendor spending. If saved time is not redeployed or converted into another measurable outcome, report it as productivity improvement rather than booked cost reduction.
Likewise, a high score on a prioritization chart does not establish that a model will work reliably in production. Validate performance with the actual data and workflow, and account for integration, evaluation, oversight, and risks before expanding a pilot. No single department, technology, or financial threshold is established as the best choice for every mid-market company.
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