Finding Work Tasks Worth Automating with AI

A task list before a tool

Start an automation push by hunting for tools and the question of what to automate gets lost. First, spend about a week noting what you actually do: the task, how long it took, how often, and whether it is done the same way each time. That is enough. Written down, the repetitive tasks that eat more time than you thought become visible, and the candidates sort themselves out.

• Example entry: "Tidy weekly sales sheet / 40 min / every Monday / same format each time"
• Example entry: "Sort enquiry emails / several times a day / falls into a few fixed types"

What makes a good candidate

Not every repetitive task suits automation. The more of these conditions a task meets, the better it is to hand to AI or a simple automation tool. Being able to check the result quickly matters most: if checking takes as long as doing the job, the gain disappears.

• It repeats often
• The incoming material is mostly in the same format
• You can quickly tell whether the result is right
• Mistakes are minor and reversible
• The rules can be explained in words

What a person must always check

For tasks like these, AI may draft, but a person must make the final call. Put a check step directly before anything that goes outside, is hard to undo, or affects people.

• Emails and documents going to customers or partners
• Anything that moves money: transfers, payments, refunds
• Judging people: hiring, appraisal, discipline
• Decisions on contracts, law or tax
• Deleting data or bulk edits

Ask the AI for candidates

Give the AI your task log and ask it to sort automation candidates, and you may get ideas you had not thought of. Describe only the shape of the work, leaving out company names, customer details and internal figures. Any specific tool or feature it suggests needs checking against what is actually available where you work.

• Example: "Here is a list of repetitive tasks from my week. Sort each into: good to automate, AI drafts only, or must be done by a person, with a one-line reason."
• Example: "For each task marked good to automate, note where a person should check the result."

Trial small and keep a record

Once you have a candidate, try one at a time for a short period. While trialling, note the time taken and how many times you had to fix the output. If time drops but fixes are frequent, refine the request; if neither improves, the task does not suit automation. Judging by records rather than gut feeling means less time wasted on tools that do not help.

Three levels of automation

Automation does not have to be grand. Widening it in stages like this limits the risk while still paying off. Always run macros or scripts written by AI on a copy of the data first, never the original, and get an explanation of what the code does before relying on it.

• Level 1: save common requests as templates and paste in new material
• Level 2: bundle repetitive calculations and tidying into spreadsheet formulas or macros
• Level 3: connect work tools into a flow, with a human check step built in

Company policy and data

Whether work material may go into an outside AI service is your organisation's call. Check whether the service is approved and what data may be entered. Automations built on personal accounts can be left running with no one in charge when someone changes roles. Write a short note on what each automation does and what it connects to, and share it.

Before you build

Before setting up an automation, check:

• Was the task chosen from a real log of work?
• Can the result be checked quickly?
• Is the human check point decided?
• Is it within approved services and data?
• Was it tested on a copy first?
• Is there a note so someone else could take it over?