Every business is being told to "use AI". The useful question is narrower: which tasks in your company are frequent enough, repetitive enough and checkable enough that AI will save real time without creating new risk? These are the use cases we see working in production, grouped by function.
Customer support
- Answering common questions from your knowledge base, with sources, and handing over to a person for anything unusual.
- Drafting replies for agents to review, using the customer's history and your policies.
- Triage: classifying and routing incoming tickets, emails and messages to the right team.
Operations and finance
- Document processing: extracting data from invoices, purchase orders and forms, validating it and entering it into your systems.
- Reconciliation: comparing records across systems and explaining mismatches.
- Reporting: turning raw operational data into written summaries for managers.
Sales and marketing
- Account briefings before meetings, gathered from CRM, email and public information.
- First drafts of proposals and follow-up emails in your house style, reviewed by the salesperson.
People and HR
Answering employee policy and leave questions from HR documents and the employee's own records, and flagging attendance anomalies before payroll runs. Decisions about people stay with people.
Engineering
Coding assistants and agents that write tests, fix routine bugs and open pull requests for human review are among the most widely adopted uses of AI, because the output can be checked automatically by tests and code review.
What successful use cases have in common
- High volume: enough repetitions that saving minutes per task adds up.
- A clear definition of a good result, so accuracy can be measured.
- Access to the right data, with permissions respected.
- A person reviewing consequential outputs, at least at first.
- A baseline: how long the task takes and how often it goes wrong today.
Where to start
List the tasks your teams repeat most, score each on volume, value, risk and how easy the result is to check, and pick the top one. Prototype it on real data, measure it against the baseline, and only then decide whether to build it properly. One proven use case teaches you more than ten pilots.
Questions people ask
What is the easiest AI use case to start with?
- Usually answering internal or customer questions from existing documents, or classifying and routing incoming requests — both are high volume and easy to check.
Do we need our own AI model?
- Rarely. Most business use cases are best served by existing models connected to your data through retrieval and tools.
How do we measure AI ROI?
- Measure the task before AI — time taken, error rate, cost — then measure the same things after, on the same kind of work.