Short answer
Start with one specific, repetitive task and a measurable goal. Then decide which data and systems the agent needs access to, build a prototype with real data, add checks and human approval — and only then put it into everyday use.
1. Choose the right task
The best first tasks for an agent are ones employees do often, following similar rules, and that take a lot of time: entering invoices, sorting requests, answering typical questions, moving data between systems.
2. Describe the process
Write down how an employee does this work today: where the data comes from, what decisions are made, what happens with exceptions. This description becomes the agent’s instructions.
3. Define access and limits
Which systems does the agent need to access (e-mail, CRM, accounting), and what may it do on its own? What only after approval? The fewer permissions, the safer.
4. A prototype with real data
Within a few weeks you can test how accurately the agent works with your actual documents. This shows whether the idea pays off before you invest a bigger budget.
5. Integration and rollout
The agent is connected to your systems, with logging, error handling and monitoring. Employees can see what the agent has done and correct it.
An off-the-shelf tool or your own agent?
Simple tasks may be covered by ready-made tools. If the agent has to work with your systems, data and rules, you usually need a custom solution. On costs — how much an AI agent costs.