To identify a high-value vertical AI opportunity, start with an industry’s costly problems, narrow the search to a specific workflow, estimate the business value of improving it, and verify that data, integration, adoption, and risk conditions make a pilot practical. The industry alone is not the opportunity: a buyer needs a measurable outcome, such as less downtime or lower processing cost, rather than an AI label.
1. Choose an industry worth investigating
Use industry-level signals to decide where to look, not to pick a product idea. Consider the size of the sector, the range of plausible AI applications, investment in startups, and evidence of economic impact. McKinsey argues that applications with greater economic benefit are more likely to attract paying customers. Its older analysis described nearly 600 discrete AI uses across major industries—about 400 involving some machine learning and 300 involving deep learning. Those counts are historical, not a current inventory or a ranking of today’s best markets. McKinsey’s industry-selection analysis
Look for problems whose costs or consequences are visible to a buyer: production delays, downtime, rework, errors, labor constraints, compliance exposure, or lost sales. Ask whether the organization already measures the outcome and has a reason to improve it. A large sector with diffuse, hard-to-measure pain may be less promising than a narrower market with an urgent problem and a clear budget owner.
2. Find a workflow where the pain happens
Talk to the people doing and managing the work. Ask where tasks are repetitive or low-value, where work waits for scarce expertise, and where people spend time interpreting ambiguous information. Request concrete examples: what triggered the task, what information the worker needed, where it slowed down, and what happened when it was delayed or done incorrectly.
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OpenAI’s business guide recommends collecting workflow problems from employees and prioritizing promising examples. It is useful as a discovery approach, not independent proof that any particular AI deployment will succeed. In a Fanatics Betting and Gaming example quoted in the guide, CFO Andrea Ellis described asking finance staff to identify processes that could benefit from AI, then using that list to create a roadmap of projects to explore. OpenAI’s guide to identifying AI use cases
3. Define a narrow, measurable use case
Move from an industry to a particular workflow, sometimes called a microvertical. Describe the current work, the bottleneck, the proposed change, and the business result in plain language. For example, “help maintenance teams identify likely equipment faults sooner to reduce machine downtime” is a testable proposition; “AI for manufacturing” is not.
- Who does the work? Name the role, team, or buyer affected.
- What happens today? Specify the task and the point where delay, cost, or error occurs.
- What would change? Explain what the system would produce or automate and how it would fit into the existing workflow.
- What metric could move? Choose an outcome the organization can baseline and measure, such as time per case, downtime, error rate, cost per transaction, or conversion.
Estimate value using the buyer’s own baseline rather than a generic claim about AI. For example, determine how often the workflow occurs, what each delay or error costs, and what fraction of that cost a successful intervention could plausibly avoid. Treat an estimate as a hypothesis until a pilot measures the result. McKinsey’s microvertical argument is that customers pay for solving a specific problem with solid ROI, not for the technology label alone.
4. Check data, integration, and adoption feasibility
A valuable problem is not automatically a viable AI opportunity. Confirm that the relevant data can be accessed lawfully and operationally, is sufficiently complete and usable, and can be managed over time. Identify which systems hold it, how frequently it changes, and who is responsible for data access and quality.
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1Clear out junk files and repair common Windows errors2Fix the driver behind crashes, sound loss and screen glitches3Repair Windows errors before they cause bigger problemsThen map the path from model output to action: which existing tools must connect, who sees the result, who reviews it, and what happens when it is wrong or unavailable. In smart manufacturing, NIST’s July 2026 roadmap identifies complex industrial data and integration with heterogeneous sensing and control systems as deployment challenges. These constraints can be central to the economics of a manufacturing use case, not a technical detail to defer until after product selection. NIST’s smart manufacturing AI roadmap
Adoption conditions vary by sector and geography. McKinsey’s 2026 analysis of Central Europe associates richer data and standardized processes with faster scaling, while noting that operationally complex sectors may progress more gradually. That regional analysis should not be treated as a universal adoption ranking. For a specific opportunity, check whether the workflow is standardized enough to change, whether frontline teams can act on the output, and whether the organization has an owner able to implement the change. McKinsey’s Central Europe analysis
5. Assess risk and the human role
Before committing to a pilot, consider what an incorrect, missing, or delayed output could cause. Higher-consequence decisions may require stronger reliability evidence, clearer explanations, auditability, escalation paths, or human approval. Decide explicitly which decisions the system may support, which it may make, and which must remain with a qualified person.
NIST’s AI Risk Management Framework (AI RMF) is a voluntary resource for considering AI risks; NIST says version 1.0 is under revision. Check NIST’s current framework status before relying on a specific version as guidance. NIST AI Risk Management Framework
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6. Compare the remaining candidates and pilot carefully
When several workflows survive the initial screen, compare them using the same questions. This is a decision aid synthesized from the sources above, not a validated universal scoring formula. Adapt the criteria to the industry, geography, buyer, and consequences of error; do not let a numerical total conceal a serious feasibility or safety barrier.
| Dimension | Questions to answer | Evidence to seek |
|---|---|---|
| Economic value and ROI | What measurable cost, delay, error, or revenue outcome could improve? | A baseline, a credible path to impact, and a buyer who values the result. |
| Urgency and willingness to pay | Is the problem important enough to fund now? | A responsible budget owner, an existing cost or priority, and a reason to act. |
| Workflow specificity and fit | Can the use case be described as a defined task and result? | A clear user, trigger, output, and place in the existing process. |
| Data and integration | Can the system obtain the needed data and connect to the work? | Access permissions, data quality and management, system interfaces, and an action path. |
| Deployment and adoption readiness | Can the organization change the process and use the output? | Standardized steps where needed, operational ownership, and engaged users. |
| Risk and oversight | What is the consequence of error, and what controls are needed? | Reliability needs, explainability, escalation, and appropriate human review. |
Choose a small number of candidates for pilots. For each, define the baseline, target outcome, measurement period, accountable owner, and what would count as a reason to stop or expand. McKinsey’s Central Europe analysis recommends assessing economic value, technical feasibility, and risk before putting use cases into a roadmap; its estimates and examples are regional. For context, that 2026 analysis modeled more than €700 billion in potential AI value in Central Europe, including more than €280 billion attributed to automation. These are modeled regional estimates, not realized savings or a global forecast. It also reported 10–20 percent cost reductions for software engineering based on McKinsey’s client experience, not a general result for every software team. McKinsey’s Central Europe analysis
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