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A use case is the task and the decision it supports—not a particular algorithm or vendor product. For example, forecasting product demand is a use case; a forecasting model is one possible technique for it.
What kinds of work can machine learning do?
Many industry examples share a small number of task types. Naming the task makes it easier to compare applications across sectors and to see what the system actually contributes.
- Prediction and forecasting: estimate a future value or event, such as demand, credit losses, equipment failure risk, or crop yield.
- Classification: assign a category, such as whether a transaction merits fraud review or an image may show a defect.
- Anomaly detection: flag data that differs from an expected pattern, for example an unusual sensor reading or transaction.
- Image, text, and other data analysis: extract or classify information in medical images, product labels, sensor streams, or scientific datasets.
- Recommendation and ranking: order or suggest options, such as a tailored product or service. In high-impact settings, recommendations should not be confused with a justified final decision.
- Decision support and optimization: provide inputs to choices about routes, schedules, inventory, or resource allocation. An ML output may inform a workflow without controlling it.
These categories can overlap. A system might forecast equipment failure, flag a high-risk machine, and send a recommendation to a maintenance team; those are distinct steps in one operational use case.
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Where is machine learning used?
Examples below describe application areas identified by the OECD, FDA, or NIST. They show the kinds of work organizations explore or perform, not proof that every deployment is effective or that a named regulator has endorsed a particular system.
Healthcare and life sciences
Applications include supporting diagnosis and disease-prevention work, detecting outbreaks, assisting treatment discovery, tailoring interventions, and enabling self-monitoring. The FDA describes potential ML applications across medical devices, diagnostic and therapeutic development, commercial manufacturing, regulatory assessment, and post-market surveillance.
Examples the FDA discusses for its own work include exploring algorithms to identify high-risk imported seafood, analyzing data for adverse events, assessing synthetic datasets for training or testing, and forecasting the timing of certain abbreviated new drug applications. It also describes natural-language processing to identify and code adverse events in product labels for safety-review work. These are examples of work under evaluation; they do not establish FDA approval of a particular ML system or demonstrate clinical benefit.
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Finance and insurance
Financial-sector applications include fraud monitoring, anti-money-laundering workflows, credit scoring and underwriting, credit-loss forecasting, portfolio and risk management, algorithmic trading, robo-advice, tailored banking products, and chat-based customer service. Insurance examples include advice and claims handling.
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Manufacturing and supply chains
NIST identifies predictive maintenance from equipment-sensor data, defect detection for quality control, demand forecasting, inventory management, safety monitoring, supply-chain disruption analysis, resource allocation, and production scheduling. The OECD’s 2026 review of AI uptake in high-impact EU sectors likewise identifies predictive maintenance, quality assurance, and supply-chain optimization as prominent manufacturing applications.
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Industrial applications can involve connected, multi-stage processes: data must be collected and exchanged across equipment and systems, and human observations may also inform decisions. A model’s output is useful only if it fits factory operations—for example, if a maintenance warning reaches the right person in time to act.
Agriculture
Computer vision and other predictive methods can support crop and soil monitoring, precision farming, robotics, and analysis of environmental factors that affect yield. The OECD also describes monitoring and analytics as ways to optimize inputs and support resilience. These are potential applications, not a guarantee of higher yields or improved farm outcomes.
Transport and mobility
Applications include route optimization, freight logistics, public-transport management, and systems related to automated driving. These tasks involve different risks: a route recommendation, for example, is not equivalent to a system making or controlling a driving decision. Reliability, safety, infrastructure, operating conditions, and interactions with people all matter.
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Science and public services
In science, ML can help collect and process large-scale data, support reproducibility, and accelerate research workflows. Public-sector, criminal-justice, and security applications are also discussed in OECD material. Because these settings can affect people’s rights or access to services, a sound description should identify the specific task, who makes the decision, and what oversight applies—not treat an entire sector as one use case.
Retail, marketing, and customer-facing services
Marketing and advertising, tailored products, and customer-service chat functions are among the application areas described by the OECD. Forecasting demand and managing inventory are also relevant to retail operations. These examples establish broad task categories; they do not establish a measured result for a particular retailer or campaign.
Independent reader supportYour contribution helps us test, update, and keep practical guides available for everyone.How common is adoption?
Adoption figures depend on the geography, sector, survey definition, and whether a measure covers AI broadly or machine learning specifically. They should not be treated as interchangeable evidence of ML deployment or business impact.
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- European Union, 2024: The OECD’s 2026 review reports that 8% of EU transport businesses and 11% of EU manufacturing businesses reported AI adoption, compared with 13% across the EU economy. These are AI adoption rates, not ML-only rates; the review did not have comparable figures for healthcare and agriculture.
- U.S. manufacturing: A NIST page published in 2026 reports that 46% of manufacturers used AI tools such as chatbots in manufacturing operations. It also reports that more than 80% expected to increase AI use over the following two years. The first figure is not limited to ML, and the second describes expectations rather than observed future adoption. The NIST page does not expose the underlying survey’s full identity or method in the reported material.
The OECD’s 2026 EU review notes that deployments can be narrow, at pilot stage, or not integrated into core operations; larger, better-resourced organizations tend to lead, while smaller organizations can face gaps in infrastructure, skills, and investment capacity. Adoption alone does not show that a system has delivered a return, improved outcomes, or become part of day-to-day work.
How do you decide whether a use case is worth pursuing?
Start with the decision or workflow, not with a model. Compare candidate use cases against the same questions before selecting technology or setting success targets.
- Define the decision and its user. State what task or decision would change, who acts on the output, and what happens today without the model.
- Check data readiness. Determine whether enough timely, representative, and legally usable data exists. If the task needs labels or feedback, establish how reliable examples will be collected.
- Map the costs of errors. Consider false positives, false negatives, inaccurate forecasts, and performance drift. Identify who could be harmed and how a mistake would be detected and corrected.
- Set the human-review role. Specify whether a person can inspect, override, or appeal an output, and whether human review is necessary given the consequences of error.
- Test operational fit. Check whether the system can connect to existing software, equipment, escalation paths, and working practices, and respond within the time the task requires.
- Choose a baseline and success measure. Record current performance before deployment. Measure operational or social outcomes separately from model metrics such as classification accuracy; a better model score does not by itself prove a better real-world result.
- Assess governance and maintenance. Plan for privacy, cybersecurity, fairness, safety, explainability, accountability, monitoring, and changes to the model or workflow over time.
NIST’s AI Risk Management Framework is voluntary and is intended to help organizations and individuals approach trustworthy AI design, development, and deployment. NIST’s documented use cases are examples, not endorsements of the organizations or their implementations.
What can prevent an ML application from working well?
A technically plausible model may still be a poor fit for the task. NIST lists data quality and availability, initial cost, workforce skills, privacy and cybersecurity, and legacy-system integration as barriers in manufacturing. More broadly, weak or unrepresentative data, unclear responsibility for acting on results, or a workflow that cannot accommodate review can undermine an application.
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