AI agent examples
by business function.
An AI agent is software that takes on a piece of work. It gathers what it needs from your systems and outside sources, uses tools to take steps and hands decisions to a person where they need one. Below are examples of what agents run in sales, operations, finance, procurement and leadership reporting.
They are patterns, not case studies. Each one describes the job, what the agent works from and where a person stays in.
What makes a good first agent
Start where the work needs judgement across several sources.
The examples that hold up share a few traits. The work happens often enough to matter. It needs judgement across more than one source, which is where people lose time chasing and checking. And it ends in something a person can check against a standard: a list, a match, a draft, an explanation.
Steps with a fixed input and a fixed output are better done by ordinary software or a workflow. Decisions about people, such as hiring or performance, stay with people. Redesign the work before you automate it goes into how to split a process between software, agents and people.
Sales
AI agents for sales.
Sales work depends on context spread across the CRM, email, calls and outside sources the CRM never covered. More on AI agents for sales.
- An account list that refreshes itself
Each morning an agent rebuilds a ranked list of accounts from the CRM and outside sources such as registries and partner lists, showing only companies the rep hasn’t already worked. The rep decides who to call.
- Meeting prep before every external call
Before each meeting, an agent pulls previous calls, emails, deal history and similar companies into a short brief in the channel the team already uses.
- CRM upkeep
An agent checks records against outside sources, proposes updates with the source attached and writes only the fields it has been allowed to change.
- A first draft of the proposal
An agent assembles a draft from past proposals, pricing rules and the account’s history. A person edits it and decides when it goes out.
Operations
AI agents for operations.
Operations work breaks at the joins between systems. More on AI agents for operations.
- Planning and scheduling prep
Before a planning round, an agent pulls capacity, open orders, constraints and recent changes into one view and flags conflicts. The team makes the plan.
- Back-office checks
An agent checks documents against each other and against the records they should agree with. Matches move on. Exceptions go to a person with the evidence attached.
- Exception follow-up
When a delivery date, document or approval is missing, an agent chases it inside the company and drafts the message to the supplier or customer for a person to send.
- Reports that build themselves
An agent builds recurring reports on schedule, notes what moved since the last one and links each number to where it came from.
Finance
AI agents for finance.
Finance work is full of matching and explaining across ledgers, banks and documents. Agents do the matching and the first explanation. People keep the sign-off.
- Invoice matching
An agent matches each invoice against the purchase order and the goods receipt, passes the ones that agree and sends exceptions to a person with the three documents side by side.
- Reconciliation prep
An agent reconciles transactions across the bank, the ERP and subledgers, explains the differences it can and flags the rest for review.
- Variance commentary
At month end, an agent drafts why each number moved against budget or the last period, with a link to the source behind every figure. The controller reviews and edits.
- Collections prep
An agent ranks overdue accounts with the context a person needs, such as open disputes or support tickets, and drafts the reminder. A person approves it before it goes out.
Procurement
AI agents for procurement.
Procurement agents check suppliers, contracts and spend against each other so buyers start from the facts.
- Supplier onboarding checks
An agent gathers registration details and certificates, checks them against public registries and lists what is missing. Changes to bank details always go to a person.
- Contract terms against invoices
An agent compares invoiced prices and terms with the contract and flags the differences, with the clause attached.
- Negotiation prep
Before a negotiation, an agent summarises spend, volumes, delivery issues and contract dates for that supplier in one brief.
Leadership and reporting
AI agents for leadership teams.
Leaders mostly need the same questions answered faster and with sources.
- The weekly business review pack
An agent builds the pack from the same sources every week, notes what changed and links each number back to its system.
- Questions with sources
Leaders ask questions in the tools they already use, such as Slack, Teams or ChatGPT, and get answers drawn from one model of the business, with the source of each fact.
- Market and account watch
An agent follows outside sources such as registries, tenders and news for changes that touch your accounts or suppliers, and sends a short digest.
At a glance
What the agent does, and what stays with a person.
What every example needs
The same platform underneath each one.
None of these work from tools alone. An agent that only has tools rebuilds a picture of the company on every run and loses it when the run ends. Each example above runs on a world model for knowledge work: your systems joined with outside sources, with every fact keeping its source. For a single task, the agent gets a context graph with only what that job needs.
Each agent gets only the tools its job needs, anything that leaves the company waits for a person, and every action is recorded. That is set out in AI agent governance. The Ortelian platform is hosted in the EU, and we host and operate it as part of the engagement.
Your own agents can use the same platform headless through scoped tools. It plugs into ChatGPT Enterprise and Microsoft Copilot over MCP, set up for your workspace inside the engagement.
How to start
Pick one piece of work, then audit it on site.
Ortelian is a platform plus a forward-deployed team. We work on site not because our product needs it, but because your company does: which of these examples fits depends on how your work actually runs. A full engagement starts with a two-week audit that maps the work and ranks where agents help most. Then we build in two-week cycles until the first job meets the standard. After launch, your team runs it or we keep running it for you. Either way, we host and operate the platform. Detail on how we work, and a checklist to run first in the AI readiness assessment.
If you are weighing up who should build it, see Ortelian vs building in-house and Ortelian vs AI consultancies, or the step-by-step guide on how to deploy AI agents in an enterprise.
Questions
Questions people ask about AI agent examples.
What are some examples of AI agents in business?
Common examples are an account list that refreshes itself and meeting prep in sales, document checks and planning prep in operations, invoice matching and variance commentary in finance, supplier checks in procurement and weekly review packs for leadership. In each, the agent gathers and checks, and a person keeps the decisions.
What is the difference between an AI agent and an AI assistant?
An assistant answers a person who then does the work. An agent takes on the work itself: it follows what is happening, uses tools to take steps in your systems and asks a person where a step needs approval.
Which function should start first?
Wherever the work is held up the most and depends on judgement across several sources. That is often sales or operations, but the audit decides it from how the work actually runs, not from a list.
Do AI agents replace the people in these roles?
No. Agents take the gathering, checking, chasing and first drafts. People keep the judgement, the relationships and the decisions, and anything that leaves the company waits for their approval.
Can these agents work inside ChatGPT or Copilot?
Yes. Ortelian plugs into ChatGPT Enterprise and Microsoft Copilot over MCP, set up for your workspace inside an engagement, so people can use the agents and the context behind them from the tools they already have.
Let’s find your first agent.
Bring the work that takes the most chasing. We’ll tell you which of these patterns fits and whether an audit is worth it.
