Which tasks are worth automating? Not all of them. Automating recurring tasks works best when the work has a regular schedule, clear input and output, and few exceptions. This article gives you a checklist, twelve concrete examples, and a first step you can take 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, rather than only when someone remembers.
Are the input and output clear? The task starts with a defined source, such as a mailbox or a folder of documents, and 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. For fixed rules that need no interpretation, a conventional workflow may be enough.
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 or draft translation | 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 |
A small or midsize business can also investigate one clearly defined task. You might choose collecting invoice data, preparing quote reminders, or listing open orders. First check the available data, the connection to your software, and who is responsible for the result.
Keep the scope small. For help choosing that first task, read start with one workflow. To see how a full process works, from analysis to setup, automating business processes with AI explains it step by step. If you want to experiment yourself first, a tool such as n8n lets you map steps visually. Choose a local installation if you want to work on your own infrastructure; setup and integrations need technical attention.
Distinguish preparation from execution at each step. A draft stays with an employee for review. Consider automatic execution only after testing representative inputs and exceptions, with limited permissions and a way to detect errors.
Require explicit approval for payments, publications, and other high-risk changes. If input is missing or a result falls outside the agreed limits, have the workflow stop and alert an employee.
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.
Explore our AI automation for businesses services, or read about our approach to move from selecting 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.
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.
No. Start with one regularly recurring task with clear input and output. 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.
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.
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.
