Automating recurring tasks does not pay off for every task. It works best when the work has a regular schedule, clear input and output, and few exceptions. This article gives you a checklist to spot those tasks, twelve concrete examples, and a first step for today.
Go through your weekly task list with three questions. Three yeses make a task worth investigating, not automatically safe to run without supervision.
Does the task recur regularly? Daily, weekly, or monthly at a predictable time, not only when someone remembers.
Are the input and output clear? The task starts from a defined source, such as a mailbox or a folder of documents. It ends with a predictable result: a report, a summary, or an updated file.
Are there few exceptions? The steps are almost the same each time. Anything unusual falls into a category you can define in advance.
An agent such as Hermes can help with these tasks if the necessary integrations and permissions are available. Agree in advance who checks the result and when the agent must stop.
The table describes possible uses, not measured results. What you can automate depends on your data, integrations, and risks. Start with a draft or an overview you can check; grant write access only after a successful test.
| Task | Possible AI assistance | Oversight and scope |
|---|---|---|
Compiling reports | Combine data from approved sources into a draft report | Check the source, measurement period, and totals before sharing |
Editing content | Suggest improvements to spelling, structure, and wording | Have an employee approve factual claims and publication |
Summarizing documents | Create a summary, a draft translation, or a list of action items from meeting notes | Compare important details with the original document |
Finding information | Search accessible projects and knowledge bases | Limit access to approved sources and check where information came from |
Automating follow-ups | Prepare reminders based on recorded deadlines | Check the recipient and deadline; agree who approves sending |
Updating your CRM | Flag missing fields and suggest additions | Check additions against the source before changing the CRM |
Maintaining websites | Prepare page updates and check links | Check a preview and approve publication separately |
Reviewing code | Read code and suggest improvements | Run tests and get human review before merging or deploying |
Internal documentation | Suggest updates to manuals and procedures | Have the process owner check the content and whether it is still valid |
Preparing emails | Draft recurring messages | Have the sender check the content and recipient before sending |
Recurring checks | Run agreed checks and report discrepancies | Define thresholds for discrepancies and who follows up |
Collecting data | Gather and group data from approved systems | Check completeness, duplicate records, and access permissions |
To see how finding information works in practice, read about searching internal knowledge with an AI agent.
When you automate repetitive tasks, start with one that recurs often and carries little risk. Collecting data for a weekly report is a good example.
If you have several candidates, score each one with three questions. How often does it come back? How long does it take each time? And what would a mistake cost? Leave tasks with financial or legal consequences for later, even if they take more time. The first task mainly teaches you how the setup works. It does not have to prove everything at once.
Pick one well-defined task. First check the available data, the connection to your software, and who is responsible for the result. According to Eurostat, about one in three businesses in Belgium and in the Netherlands used at least one AI technology in 2025. That figure covers businesses with ten or more employees. Across the EU, it was one in five.
Good first tasks include collecting invoice data, preparing quote reminders, or listing open orders. To see how that works for quotes, read about automating quotes with an AI agent. Our guide to connecting AI to your CRM and CMS helps you assess that connection.
For help choosing that first task, read start with one workflow. To see a full process step by step, from analysis to setup, read automating business processes with AI.
Not every task needs an AI agent. Some steps follow fixed rules: a new form entry always triggers the same actions. In that case, a workflow tool such as n8n or the automation built into your software is often enough. A tool like that lets you map the steps visually. Choose a local installation if you want to work on your own infrastructure; setup and integrations need technical attention. An agent helps when the task means reading and judging text. Think of summarizing documents, drafting a reply, or deciding which customer a request belongs to. Many processes combine both: fixed steps around one task that needs judgment.
Let the agent only prepare the work at first, and decide for each step when it may carry it out. A draft stays with an employee for review. Test a step with representative inputs and exceptions first. Only then let the agent carry out that step on its own, with limited permissions and a way to catch errors.
Require separate approval for payments, publications, and other high-risk changes. If input is missing or a result falls outside the agreed limits, the workflow should stop and alert an employee.
Our own Hermes installation at Voltti works this way. Each content task starts as a ticket that the agent picks up. It ends with a verification report in that same ticket. The agent reads our Search Console data through read-only access and suggests which pages to improve. You can read more about that in improving SEO with an AI agent. The agent edits content in our CMS and sometimes publishes directly. People review the work afterward and correct it when needed.
Describe one task as if you were handing it to a new colleague. Write down the input, steps, expected output, and known exceptions. Test the description with a real example and an unusual case. That will show you what is missing before you automate the task. For a more thorough version, use our seven-question briefing for your AI agent pilot.
Before you start, write down how long the task takes now and how often someone has to correct the result. Track the same two things during the pilot and compare them after a few weeks. If the agent's drafts still need as many corrections as before, adjust the description or pick a different task. Also note the exceptions you run into: they show where the agent needs a stop rule.
Explore our AI automation for businesses services. Or read about our approach to move from choosing a task to running a pilot.
Look for regular repetition, clear input and output, and few exceptions. That makes a task worth investigating. Then check data quality, access permissions, risks, and who will review the result. Have several candidates? Start with the one that comes back most often and does the least harm when a result is wrong.
No. Start with preparation and human review. Automate execution only for tested steps with limited permissions, clear stop conditions, and agreed approval rules for high-risk actions. Payments, publications, and messages to customers stay with a person. That changes only after you have seen the agent handle normal and unusual cases correctly.
No. Start with one regularly recurring task with a fixed input and a clear result. After a pilot, assess what works reliably and what still needs review. Evaluate the next task separately; the same setup will not automatically fit a different process. You can reuse the access rules and review steps you already agreed on.
We assess that for each task. An API or accessible interface can help, but neither is a guarantee. Access permissions, data quality, software limitations, and integration reliability also determine what is possible. Without a working connection, the agent can sometimes still prepare work that an employee then enters into the software.
The timeline depends on the scope, available integrations, access permissions, and test cases. Map those out first, then agree on a schedule for setup, review, and possible rollout. You can test a small task with a working connection in a short pilot. A task that needs new integrations takes longer.
