AI use cases: the tasks companies actually hand to AI in production
By the E-Solutions Web editorial team. Published , updated . How we write
The short answer
The AI use cases that work in companies today are repetitive tasks built on text, documents or conversations: answering customers, reading invoices and orders, qualifying leads, answering employees, preparing reports. In 2025, Eurostat counted nearly 20% of EU enterprises with ten or more employees using AI, most often for marketing or sales and for business administration. The best first case is frequent, rule-based and measurable.
This guide lists the AI use cases that hold up in production, first by business function, then by industry. Each line links to a page where we describe the case in more detail, the tools involved and the rules that apply. The figures come from Eurostat and the regulatory points from the official text of the EU AI Act; both are listed at the end.
What makes a good AI use case
A good AI use case is a task your team repeats often, that runs on text, documents or conversations, and whose result someone can check. What decides is the shape of the work. A task that happens forty times a day, follows known rules and ends with an entry in a system is a strong candidate. A decision taken twice a year on judgment alone is not.
Adoption is growing fast, from a low base. According to Eurostat, 19.95% of EU enterprises with ten or more employees used at least one AI technology in 2025, up 6.47 percentage points on 2024. The gap by size is wide: 17% of small enterprises, 30.36% of medium ones and 55.03% of large ones. Among enterprises using AI, the most frequent purposes were marketing or sales (34.70%) and the organization of business administration processes or management (31.05%). Logistics came last, at 6.08%.
Those figures show where companies started, and the value may lie elsewhere. The cases below are sorted by where the work sits in your company, so you can find your own situation in them.
AI use cases by business function
Each business function has two or three tasks where AI pays off first, usually the ones that eat hours of reading, typing and answering. The table groups them by department. Each landing page goes into the tools, the data and what stays with a person.
| Function | Use cases that work in production | What stays with a person |
|---|---|---|
| AI for customer service | Answering tickets and chats from your own content, sorting and routing requests, drafting replies for review, calls answered outside office hours | Refunds, complaints and anything outside the rules |
| AI for sales | Researching and qualifying leads, enriching the CRM, preparing meetings, following up, drafting answers to tenders | The relationship, the price and the commitment |
| AI for finance | Reading supplier invoices, matching them to orders, bank reconciliation, chasing missing documents at closing | Payment approval and the final entries |
| AI in procurement | Comparing supplier quotes, following up on orders and documents, checking deliveries against orders | The choice of supplier and the order itself |
| AI for HR | Answering recurring employee questions from your policies, onboarding, preparing documents | Anything touching hiring, evaluation or dismissal |
| AI for IT support | First-level helpdesk, password and access requests, answers from the internal knowledge base, ticket triage | Administrator rights and security decisions |
Two patterns cover most of this table. The first is the assistant that answers from your own documents, with the source shown; our knowledge assistant is built that way. The second is the agent that reads an incoming document or message and prepares an entry in your system, which is the job of document AI. The overview of every function is on the solutions page.
AI use cases by industry
Each industry has its own documents, its own software and its own rules, and they shape an AI project more than the technology does. A purchase order in wholesale, a claim in insurance and a site report in construction are all documents to read and file, but each comes with its own vocabulary, its own business software and its own constraints.
| Industry | Typical use cases | What sets the industry apart |
|---|---|---|
| AI in manufacturing | Order intake, quality non-conformities, spare-parts after-sales, quoting special parts | Industrial ERP, bills of materials, technical sheets |
| AI for distributors | PDF and email orders keyed into the ERP, availability and price questions, customer portal | Volume of orders, customer price lists, EDI |
| AI in logistics | Carrier emails, proof of delivery, booking requests, shipment status questions | Transport and warehouse management systems |
| AI in construction | Site reports and snag lists, tender documents, quantity takeoffs | Field work, photos, patchy network |
| AI for law firms | Research in the firm’s own files, AI contract review, document review | Professional secrecy, where data is hosted |
| AI for accountants | Collecting client documents, pre-booking entries, reminders | Production software, the e-invoicing reform |
| AI in banking | Onboarding and KYC files checked before review, advisor meeting notes, answers from your procedures | A person decides on each client, credit scoring is high-risk, DORA contracts |
| AI in insurance | Claims files completed from received documents, policy questions | High-risk pricing rules, broker channels |
| AI for real estate | Answering portal leads out of hours, visit scheduling, tenant requests | Mandates, listings, property management |
| AI for e-commerce | “Where is my order” handled end to end, returns, product sheets | Store platform, carriers, accessibility rules |
| AI for hotels | Guest questions in several languages, bookings, arrival information | Property management systems, travel platforms |
| AI in government | Citizen requests, field reporting, answers from administrative documents | Public procurement, accessibility, hosting |
| AI for nonprofits | Field reports sorted and mapped, grant reports drafted from your data, donor and member questions | Volunteers in the field, sensitive data on beneficiaries |
Eurostat’s 2025 figures show how uneven adoption still is across sectors. Information and communication leads, with 62.52% of enterprises using AI, followed by professional, scientific and technical activities at 40.43%. Every other sector covered was below 25%, from 24.82% in real estate to 10.79% in construction. A low rate in your sector leaves room: the tasks are there, and most companies around you have not automated them yet. All industries are gathered on the industries page.
When a use case calls for an AI agent
A use case calls for an agent when the task runs across several steps and several systems, and today someone switches between tools to finish it. Summarizing an email is a single step. Reading an order email, checking stock in the ERP, finding the last price, drafting the confirmation and waiting for approval is a task, and that is what an agent does.
The difference matters for the budget and for the risk. A single-step use case can often be covered by a feature in a tool you already pay for, or by an AI integration in your CRM or ERP. An agent needs access rights, a log of every action and a point where a person approves before anything sensitive. Our guide to what an AI agent is goes through that distinction, and the AI agents page shows how we build them.
Between the two sits workflow automation: a chain of fixed steps, with AI inside one or two of them to read or write. When the path is always the same, that is often enough, and cheaper to run. AI automation covers that ground.
The use cases the EU AI Act treats as high-risk
A handful of use cases are classified as high-risk by the EU AI Act, and they concern decisions about people. Annex III of Regulation (EU) 2024/1689 lists the areas. The ones a typical company meets are:
- Employment: systems used to recruit or select people, in particular to place targeted job ads, to screen and filter applications and to evaluate candidates, and systems that make decisions on promotion, termination or task allocation, or that monitor performance. Our page on AI recruiting shows how a person keeps the decision on every application.
- Credit: systems that evaluate the creditworthiness of individuals or establish their credit score, fraud detection excepted.
- Insurance: risk assessment and pricing for individuals in life and health insurance.
- Essential public services: systems used by public authorities to decide on eligibility for public benefits and services.
According to the European Commission, the rules for these high-risk areas apply from December 2, 2027, after the 2026 digital omnibus moved the date. Most of the use cases in the tables above, such as answering customers, reading invoices or supporting employees, fall outside Annex III. They can still carry transparency duties: under Article 50, a chatbot must let people know they are talking to an AI, unless it is obvious. Our EU AI Act guide sets out the obligations and the calendar.
How to choose your first AI use case
Choose the task that happens most often, follows the clearest rules and has a result you can count, even if it is not the most impressive one. The first project has two jobs: save real time, and teach your company how an AI system behaves on its own data. A modest case that works does both.
| Criterion | Question to ask | Good sign |
|---|---|---|
| Volume | How many times a week does the task happen? | Dozens or hundreds of times |
| Rules | Could a new employee learn it from a written procedure? | Yes, with a list of exceptions |
| Data | Are the documents and systems it needs reachable? | Emails, PDFs or an API you already have |
| Cost of an error | What happens if the output is wrong once? | A person catches it before it counts |
| Measure | Can you count the time or the delay before and after? | Yes, from tickets, logs or a timesheet |
| People | Does the team doing the task want help with it? | They are the ones asking |
Scoring your candidates on these six lines usually leaves two or three clear winners. Our free AI readiness check runs a shorter version online, and the ROI calculator turns the volume and the time per task into an estimate you can show your management. For a full inventory, with volumes measured on your real work and a fixed price for the first agent, the AI readiness assessment goes further.
See two use cases running
The quickest way to judge a use case is to watch it work on real-looking documents. Two agents run live on this site, on the data of a fictional company. The knowledge assistant demo answers questions about internal policies and quotes the passage each answer comes from. The document extraction demo reads a supplier invoice, extracts the fields and flags the one that does not add up.
Both are use cases from the tables above, in the form they take in production. When one of them looks like a task your team does every day, that is usually where to start, and a first agent built on it typically goes live in four to eight weeks.
Find your first use case in 30 minutes
You probably already know the task: the one your team complains about every week. Bring it to a free 30-minute assessment. We look at the volume, the documents and the systems involved, and tell you whether it makes a good first agent, a simple automation or neither. If the task is a good fit, you leave with the next step, and the scope and price of the build are fixed in writing before anything starts.
Book your free 30-minute assessment, or try the document extraction demo first on a sample invoice. We reply within one business day.
Frequently asked questions
What are the most common AI use cases in business?
Eurostat’s 2025 survey of EU enterprises found that companies using AI apply it first to marketing or sales (34.7% of them) and to the organization of business administration processes (31.1%). In practice, that means drafting and answering messages, qualifying leads, reading documents and preparing reports. Logistics was the least common purpose, at 6.1%.
What is the difference between an AI use case and an AI agent use case?
An AI use case can be a single step, such as summarizing a document or classifying an email. An AI agent use case covers a whole task across several steps and systems: the agent reads the request, looks up the data, prepares the action and asks a person before anything sensitive. Agents fit tasks that today require someone to switch between tools.
Which AI use cases are high-risk under the EU AI Act?
Annex III of Regulation (EU) 2024/1689 lists them. For most companies, the relevant ones are recruiting and evaluating candidates, decisions on promotion, termination or task allocation, creditworthiness assessment of individuals, and risk assessment and pricing in life and health insurance. Customer service, document reading and internal assistants are usually not in that list.
How do I find AI use cases in my company?
Start with the work itself. List the tasks your teams repeat every week, count how often they happen and how long each takes, and note the data and tools involved. The tasks that are frequent, rule-based and easy to measure are your candidates. An assessment or a short online diagnostic can structure that inventory.
Which industries use AI the most?
In Eurostat’s 2025 figures for EU enterprises, information and communication leads with 62.5% of enterprises using AI, followed by professional, scientific and technical activities at 40.4%. In every other sector covered, the share was below 25%, from 24.8% in real estate down to 10.8% in construction.
Sources
- Use of artificial intelligence in enterprises, Statistics Explained, Eurostat, data extracted December 2025, accessed 2026-10-01.
- Regulation (EU) 2024/1689 laying down harmonised rules on artificial intelligence (Artificial Intelligence Act), Annex III, EUR-Lex, Official Journal of the European Union, published 2024-07-12, accessed 2026-10-01.
- AI Act, European Commission, Shaping Europe’s digital future, updated 2026-08-03, accessed 2026-10-01.