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To identify a high-value vertical AI opportunity, start with an industry’s costly problems, narrow them to a specific workflow, and test whether better performance could produce measurable value. Then check the data, system access, adoption conditions, and risks before committing to a pilot. The goal is not to find an industry that sounds “AI-ready”; it is to find a buyer with a consequential problem that a feasible, trustworthy solution can improve.
Start with an industry, but choose a workflow
Industry selection is a useful first filter, not the final decision. Look for sectors where delays, errors, labor constraints, lost sales, or other problems have meaningful economic consequences—and where a buyer can measure those consequences. McKinsey’s older article on AI opportunities suggests considering sector size, the breadth of possible AI use cases, startup equity funding, and the observed economic impact of AI applications. Its reasoning is that buyers are more likely to pay when an application can deliver greater economic benefit.
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That article describes nearly 600 discrete AI uses across major industries, with about 400 involving some machine learning and 300 involving deep learning. These are historical counts from the article, not a current inventory or a ranking of today’s best industries. Use the screening logic, not its old sector rankings, as a present-day verdict. McKinsey’s industry-selection analysis
Once an industry looks promising, move down to a specific workflow or “microvertical.” A buyer is more likely to act on a clear outcome—such as reducing machine downtime, lowering processing costs, or increasing sales—than on a broad pitch to “bring AI to manufacturing” or another whole sector.
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Find costly work by talking to the people who do it
Ask operators, frontline staff, and managers where work repeatedly gets stuck. Useful signals include tasks that are manual and repetitive, low-value work consuming skilled employees’ time, queues caused by a shortage of expertise, and cases that stall because someone must interpret ambiguous information. Ask for concrete examples: what triggered the task, what information was needed, who handled it, how long it took, and what happened when it went wrong.
OpenAI’s business guide recommends collecting these problems from employees and prioritizing the most promising examples. It also quotes Andrea Ellis, CFO of Fanatics Betting and Gaming, describing a finance-team exercise: “We formed an AI automation task force with this guiding principle. We asked all finance team members to detail processes that they felt could benefit from AI. We took that list and created a roadmap of projects we wanted to explore.” This is a discovery method, not proof that any particular candidate will succeed. OpenAI’s guide to identifying AI use cases
Turn the problem into a testable value hypothesis
Write down who performs the workflow today, where the bottleneck occurs, what an AI-enabled process would change, and which business metric could move. A useful hypothesis connects an intervention to an outcome: for example, helping a maintenance team identify a likely equipment issue sooner could reduce the time a machine is unavailable. The claim still needs a baseline and a test; the example alone does not establish that AI will improve the result.
Estimate value using metrics the prospective buyer already tracks. Depending on the workflow, that might include time per case, rework, error rates, service delays, downtime, labor capacity, or revenue lost to missed opportunities. Separate the value of faster or more consistent work from any costs required to achieve it, such as review effort, integration, or changes to the process. Ask who owns the metric and who controls the budget: the person experiencing the pain may not be the buyer.
Look for willingness to pay, not just enthusiasm. A strong candidate has a recognizable cost or opportunity, a buyer motivated to change it, and a result that can be measured against the current process. Stanford’s Erik Brynjolfsson, quoted in OpenAI’s guide and attributed there to McKinsey’s January 2025 “AI In the Workplace,” says: “This is a time when you should be getting benefits [from AI] and hope that your competitors are just playing around and experimenting.” Treat that as a perspective on urgency, not evidence that a specific opportunity has a positive return.
Check whether the workflow is feasible to support
A valuable problem is not automatically a viable product opportunity. Before ranking it highly, examine whether the information needed to help with the task is accessible, sufficiently reliable, and usable in the buyer’s environment. Confirm permissions and ownership as well as technical availability; data that exists but cannot be used for the intended purpose does not make a practical input.
Then map the systems and people around the workflow. Identify where information originates, where output must go, which existing tools need to connect, who reviews recommendations, and who takes action. Consider whether staff can fit the new process into their work and whether the workflow itself can change. An AI output that arrives outside the system people use—or adds more review work than it saves—may have little operational value.
These constraints can be especially prominent in manufacturing. NIST’s July 2026 smart-manufacturing roadmap identifies complex industrial data, data management, and integration with heterogeneous sensing and control systems as deployment challenges. The point is not that every manufacturing use case is infeasible; it is that data and integration belong in the opportunity assessment from the beginning. NIST’s smart manufacturing roadmap
Account for adoption differences between industries
Industries do not adopt AI under identical conditions. McKinsey’s 2026 analysis of Central Europe associates richer data and standardized processes with faster scaling, while noting that operationally complex sectors may scale more gradually. That is a regional observation, not a global rule, and it suggests a practical question: can a pilot operate within the customer’s actual process, systems, and decision rights?
Keep regional figures in their proper scope. In its 2026 Central Europe analysis, McKinsey estimates more than €700 billion in potential AI value in the region, including more than €280 billion attributed to automation. These are modeled regional estimates, not realized savings or a worldwide forecast. The article also reports that software engineering cost reductions of 10 to 20 percent were based on McKinsey’s client experience; that range should not be generalized to every team or geography. McKinsey’s Central Europe analysis
Screen for risk, reliability, and human oversight
Ask what the consequences of an incorrect, incomplete, or delayed output would be. The answer affects the reliability required, how results should be explained, and whether a person must review or approve them before action. Define what the system may do, what it may only recommend, and how people can catch and correct failures.
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Independent reader supportYour contribution helps us test, update, and keep practical guides available for everyone.Compare candidates without pretending there is a universal score
When several workflows remain plausible, compare them across the same dimensions. The dimensions below synthesize the cited guidance; they are a decision aid, not a validated scoring formula. Rate candidates consistently, record assumptions, and investigate any weak or unknown area instead of hiding it in an average.
| Dimension | Questions to ask | Evidence to look for |
|---|---|---|
| Economic value and ROI | What costly outcome could change, and how would the buyer measure the difference? | A baseline, a named metric, and an outcome the buyer values. |
| Urgency and willingness to pay | Who owns the problem, how costly is delay, and is there a motivated budget holder? | A buyer willing to sponsor a test and discuss the value of improvement. |
| Workflow fit | Is the task specific, and can useful output reach the person or system that acts on it? | A mapped process, clear users, and a defined role for the AI output. |
| Data and integration | Can the required data be accessed and managed, and can the solution fit the existing systems? | Known data permissions and sources, plus a realistic integration path. |
| Adoption readiness | Can the organization change the process and support people using the new approach? | Operational ownership, compatible processes, and a plausible route from pilot to use. |
| Risk and oversight | What could go wrong, how serious would it be, and where is human review needed? | Defined limits, reliability expectations, review responsibilities, and ways to detect errors. |
Choose a pilot that can answer a business question
Choose a small number of candidates for which the buyer, workflow, metric, and feasible path are clear enough to test. Set a baseline before deployment, name an owner for the workflow and an owner for the outcome, and agree on what result would justify continuing. Include the review and integration work needed in the pilot design so the measured result reflects a usable process, not an isolated model demonstration.
McKinsey’s Central Europe analysis recommends assessing economic value, technical feasibility, and risk before placing use cases on a roadmap. Its examples and estimates concern that region; the practical lesson is to make those checks before committing, not to assume its regional findings apply unchanged elsewhere.
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