AI agent use cases: practical examples for business

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AI agents can help with work that requires several connected decisions: investigating a price discrepancy, assembling a proposal or following a research question across different sources. Their useful feature is the ability to choose a next step after seeing what the previous step returned.

For a business, the starting point is a recognisable job. What information does someone gather? What changes their next action? What result must they hand over? Those questions make an AI agent easier to assess than a promise to automate an entire department.

The examples below show reported applications across four business functions, followed by an illustrative trades scenario. The reported examples come from supplier case studies with customer accounts. They establish what those organisations describe using; they are not independent tests of performance or evidence that the same results will transfer to your business.

Finance operations: investigate an order discrepancy

An order arrives with a price that differs from the company's records. Before invoicing, someone must work out whether the customer has an agreed promotion, whether the product details match and what needs correcting.

Microsoft's Danone case study, published in October 2025, describes an agent that checks customer orders against internal order, pricing and promotional systems. It detects discrepancies, suggests pricing adjustments and drafts emails that employees can use to resolve issues.

The useful contribution is a prepared exception: the conflicting information, a proposed explanation and a draft response. The source does not establish that the agent independently changes prices or issues invoices.

For a smaller business considering this task, a sensible trial could stop at that preparation stage. A finance employee would review the evidence and decide what to change. Straightforward orders could continue through existing rules. The question to test is whether investigating the exceptions becomes easier without creating extra reconciliation work.

Sales: prepare a proposal from scattered knowledge

A customer asks for a solution that combines several services. Preparing a relevant proposal means finding current offer details, previous work and the right internal expertise. The information required changes with the request.

Fujitsu's reported sales application uses specialised agents to retrieve and combine knowledge from internal sources. Microsoft's March 2025 account describes a system that interprets the request and prepares proposals, with sales-team feedback used during development.

This is a useful distinction between generating fluent text and doing preparation. A convincing paragraph still needs the right facts behind it. An agent's contribution can include gathering those facts and identifying which sources answer the customer's requirements.

An illustrative service-business version might prepare a proposal outline, link each capability claim to an approved record and flag an unsupported delivery date. The sales lead would decide scope, commercial terms and commitments. None of those permissions follows automatically from access to the document library.

Marketing: turn campaign data into a reviewable audit

A campaign audit starts with data, but its value lies in deciding which findings deserve attention. Different accounts may require different follow-up questions about performance, structure or missing information.

Microsoft Advertising's June 2026 Stagwell case study describes an agent used by Assembly teams. It pulls live campaign data, applies audit frameworks and produces recommendations and client-ready outputs. Practitioners review the work and lead strategy.

That human boundary is explicit: the reported application helps people assess campaigns. The article does not demonstrate unattended changes to advertising budgets.

A useful audit also needs a traceable connection between a recommendation and the account data. If a reviewer has to reconstruct every calculation or hunt for each supporting fact, a polished report may create more checking than it saves. Treat review effort as part of the job when evaluating a similar use case.

Research: decide what to investigate next

Research is often a sequence of changing questions. An initial source may contradict an assumption, point to another field or leave a gap that needs a different search.

Anthropic's FutureHouse case study describes that loop directly: form a subhypothesis, assess the information and potentially search again. Its literature agents gather scientific sources and produce cited answers or reports. Researchers can inspect the reasoning and evidence when deciding which experimental paths to pursue.

This example shows why adapting the next step can matter. It also preserves the distinction between finding evidence and establishing that a conclusion is sound. A bibliography is useful only if the sources support the claims made from them.

For a commercial research brief, an illustrative acceptance check would ask whether the result answers the commissioned question, distinguishes fact from inference and makes unresolved gaps visible. The person relying on the brief needs enough evidence to judge its use.

Trades: prepare a repair visit

Consider an illustrative repair business preparing tomorrow's appointments. A booking lacks a reliable equipment model number. The coordinator could check previous visits, request a label photograph or ask a technician to resolve conflicting details.

An agent could choose between those steps based on what it finds. Its permitted job would be to prepare an equipment record with supporting evidence. Diagnosis, parts selection and promises to the customer would stay with the appropriate person.

This is information work inside a business that delivers physical work. It does not require a robot, an autonomous technician or an outside purchase. The agentic glossary example compares this same job with a fixed rule that sends an information request whenever a field is empty.

Find the useful boundary in your own business

Across these examples, the most helpful question is what can be handed over for checking: an investigated discrepancy, a proposal, an audit or a sourced answer. Identify that output before choosing software.

The word “agent” alone does not settle whether you need one. Some applications combine ordinary rules, document generation and model-selected actions. Our comparison of AI agents and automation explains where each approach fits.

Choose one recurring task and use the first-use-case guide to define a small trial. If the work also looks like a contribution your customers could buy, the next question is how it might become an AI-native service offer.