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Start with a recurring business problem, not a machine-learning model. Find a gap between current and desired outcomes, define who is affected and how success will be measured, then check whether machine learning is the right tool. A promising use case also needs accessible, suitable data, a workable place in the existing process, an accountable sponsor, and a bounded way to test its value.
Where can machine learning help your business?
Look for work where a repeated decision or activity produces a measurable shortfall: for example, avoidable errors, slow approvals, uncertain demand, or service requests that are routed inconsistently. Microsoft’s Cloud Adoption Framework advises organizations to “Start with business problems” and examine recurring activities before choosing an AI technology (Microsoft Cloud Adoption Framework).
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Talk with the people who perform the work and the people accountable for its result. Map how the process operates today, how often the issue occurs, who bears its cost, and what happens when it goes wrong. A complaint such as “we need AI for support” is not yet a use case; “agents spend time sorting repetitive requests, delaying resolution of complex cases” is a problem that can be investigated.
Examples from Microsoft and Google Cloud guidance—such as equipment troubleshooting, claims support, demand forecasting, inventory operations, and repetitive customer-support inquiries—are prompts for local discovery, not proof that those projects will deliver a particular return. Your process, users, data, and constraints determine whether an idea is worthwhile.
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How do you know if a business problem is a good fit for machine learning?
Write down the activity, user, and intended result
State the use case in one sentence before discussing products or models. A useful template is: “For [user], improve [recurring activity or problem] by [intended intervention], so that [business measure] changes from [baseline] to [target] over [period].” Include the business owner and identify whose work or decisions would change.
For example: “For the service operations team, reduce the time spent routing incoming support tickets by recommending a category and queue, so median routing time moves from the measured baseline to an agreed target over a six-week pilot.” This is a testable proposal, not a promise that a model will meet the target.
Microsoft Learn recommends asking, “What is the problem to be solved? What are the underlying root causes? How does the current process work?” Its business-envisioning guidance also emphasizes clear objectives and measurable success criteria (Microsoft Learn business-value guidance). The sentence should describe a real operating outcome rather than a technology deliverable such as “build a classifier.”
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First clarify the output the process needs. As a screening heuristic, conventional machine learning may fit a task that predicts, classifies, detects patterns or anomalies, estimates risk, or supports optimization using historical examples. Generative AI may fit work that creates, summarizes, or transforms unstructured language or documents. The format of the data alone does not determine the solution: task requirements, examples or labels, tolerance for errors, and operational constraints matter.
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Also ask whether a clear rule, a better handoff, or a simpler workflow change would solve the problem adequately. If so, AI may add cost and complexity without improving the outcome. Google Cloud advises that AI and generative AI should support business goals rather than exist in isolation, and recommends assessing the problem, user expectations, measurable goals, and process changes before selecting an approach (Google Cloud: Choose an AI/ML solution).
How should you compare candidate use cases?
Compare candidates across business value and executional fit, then treat readiness and risk as decision gates. Microsoft’s Business, Experience, Technology (BXT) framework organizes consideration around business viability, user desirability, and technical feasibility; its scoring scales are planning aids, not empirically validated predictions of success (Microsoft Learn BXT guidance).
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| Dimension | Questions to answer | Evidence to seek |
|---|---|---|
| Business value and strategic fit | Which objective would improve: revenue, cost, risk, service, or productivity? Is the outcome important enough to justify the work? | A defined baseline, a measurable target, and a business owner who can explain the value. |
| User demand and workflow fit | Who has the problem? Would the proposed output help them, and what steps in their work would change? | Input from affected users, evidence of demand, and a plausible path to adoption. |
| Technical and data feasibility | Can the team access suitable data? Can a solution meet required performance and integrate with the systems where work happens? | Data access and quality checks, available skills and infrastructure, integration requirements, and operational ownership. |
| Risk and safeguards | What harm could an incorrect or inappropriate output cause? When should a person review, override, or escalate it? | Known failure modes, acceptable error limits, permissions, safeguards, and a way to monitor outcomes. |
| Resources and change effort | What will it take to build, test, maintain, and introduce the solution? Can the organization make the required process change? | People and time available, a realistic pilot window, and a plan for ongoing support. |
Use the comparison to decide what to do next, not to manufacture certainty with a single “AI readiness” score. A high-value idea with weak data access or unclear workflow ownership may warrant discovery or a constrained prototype before a full pilot. A low-value idea with substantial integration and change costs can be deferred. If you use a numerical rating, treat it as a way to make assumptions visible and compare candidates—not as evidence that the project will succeed.
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Choose a candidate with a meaningful, measurable problem and enough readiness to test it responsibly. A useful first project has an engaged business sponsor, users willing to participate, data and permissions that can be checked, and a path for its output to reach the real workflow. Prefer a bounded test over a broad transformation whose success criteria, ownership, or operating process remain unclear.
Best Value
Before the pilot begins, agree on the current baseline, target, evaluation period, users, data permissions, acceptable errors, and escalation behavior. Name who decides whether to continue, change, or stop. These controls make it possible to distinguish a useful intervention from a model that works in isolation but does not improve the business process.
Choose a small set of outcome measures tied to the original problem. Depending on the use case, that could include cost or revenue, task or resolution time, first-contact resolution, user satisfaction, adoption, escalation rate, or the share of work completed without human intervention. Pair business outcomes with model-quality and safety checks when needed. Metrics suggested in a vendor example are options to consider, not evidence that a solution will achieve them.
What needs to be in place beyond a working model?
Machine learning is only one part of an operating change. Microsoft’s organizational guidance emphasizes business sponsorship and collaboration between business and data or machine-learning teams; in practice, identify who will maintain the solution, monitor its results, respond to failures, and keep the workflow accountable after a pilot. A model that cannot be integrated into a real process or supported over time is not a ready business use case.
Keep the purpose, affected users, measure, sponsor, data dependencies, risks, and next decision visible to everyone involved. That gives teams a common basis for deciding whether evidence supports deployment, further investigation, a different technical approach, or no AI at all.
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