Office tasks worth automating hide in habits; a tally finds them

In a 2002 study, 209 students at Texas A&M wore a wristwatch that chimed every hour and wrote down what they were doing and thinking. About 43% of what they recorded were habits, things done almost daily in the same place, and while doing them their minds were more often somewhere else.

Office work runs on the same machinery, and that is why the hardest part of automating it is noticing what to automate. Asking a team what to automate relies on memory, and memory is least reliable for the small tasks people repeat all day. By the time a task is frequent enough to be worth automating, it has often become a habit.

That gap matters for anyone who runs or builds software for a small company, because the opportunity sits in exactly those small tasks. This piece explains why people miss their own repetition, why the small tasks make the best first projects, and how to run a one-week tally that finds them.

Habits run without anyone watching

The study, by psychologists Wendy Wood, Jeffrey Quinn and Deborah Kashy, defined a habit plainly: something done just about every day, usually in the same location. In the first study, 70 students kept the hourly diary for one day and 35% of the behaviors they listed were habits. In the second, 209 students kept it for two days and the figure was 43%.

What happened in their heads was the striking part. In the first study, for 60% of the habits people recorded, they were thinking about something unrelated. For less routine actions, their thoughts matched what they were doing in 70% of cases.

Overall, the authors estimate, their participants did almost 50% of everything without thinking about it. The sample was college students, and the authors note that the figure could be a lower bound, since students may spend more of the day on study and new activities than other people. Nobody was told the study was about habits until the debriefing.

Now apply the definition to an office. Copying an order number from the web shop into the accounting system meets it. So do the same lookups before every customer call. Both are done almost daily in the same place, which by the study’s definition makes them habits.

Asking people returns the official process

The usual way to find automation candidates is to ask: interviews, workshops, a process map on a whiteboard. All of them depend on people reporting their own behavior, and that is a weak instrument.

Some of the best evidence comes from screen time, where self-reports can be checked against device logs. A 2021 meta-analysis led by Douglas Parry pooled 47 studies covering about 50,000 people. Guess how often people’s average estimate of their own use came within 5% of the log. It happened in 3 of 49 comparisons, about 6%.

The errors ran both ways: people over-reported in 23 comparisons and under-reported in 23 (Parry et al.). The overall correlation between what people said and what the logs showed was 0.38, which the authors call only medium in magnitude. They note that memory problems are most apparent for behavior that is frequent and woven into daily life. That is the profile of an office habit.

User researchers learned this long ago. Jakob Nielsen’s 2001 essay on the first rule of usability tells designers to “pay attention to what users do, not what they say.” Watching people work in their own setting, the method called contextual inquiry, can “uncover low-level details that have become habitual and invisible,” as Nielsen Norman Group puts it.

Ask how someone handles a supplier invoice and you hear the process. Watch the next one arrive and you see the work.

The opportunity sits in tasks, not in jobs

The research on what can be automated points at the same small tasks. In 2017 the McKinsey Global Institute estimated that less than 5% of occupations could be fully automated with technology already demonstrated. Yet about half of all the activities people are paid to do could be, worth almost $15 trillion in wages. The activities it rated most exposed included data collection and processing.

McKinsey raised that figure in 2023. With generative AI, it estimated that work taking up 60 to 70% of employees’ time could technically be automated. That is a theoretical ceiling: McKinsey’s adoption scenarios put the halfway point somewhere between 2030 and 2060. The same report recalls a 2012 MGI estimate that knowledge workers spent about a fifth of their time, one day a week, searching for and gathering information.

Surveys of workers point the same way. Asana’s Anatomy of Work Index says people spend 60% of their time on “work about work,” such as searching for information, switching between apps and chasing the status of tasks. It puts duplicated work at 209 hours a year per knowledge worker.

Two cautions apply. Asana sells work-management software, and its figures are self-reported, the kind of measurement the Parry study warns about. Read the shares as a direction rather than a measurement. Taken together, the sources point one way: the time worth winning back is scattered across small tasks inside jobs.

Small, frequent tasks make the best first projects

A small, frequent task usually follows a narrow rule, uses input that keeps its shape, and has one obvious owner. A mistake costs little and shows up fast. The big process everyone named in the workshop has the opposite profile: many people, many exceptions and rules nobody wrote down.

The arithmetic also favors the small task. Randall Munroe’s chart Is It Worth the Time? works out how long you can spend making a routine task faster before you lose time over five years. Shave one minute off something done five times a day and you can spend six days on the automation. Shave 30 seconds off something done 50 times a day and the budget is four weeks.

The chart counts 24-hour days and no upkeep; our recomputed xkcd table uses a working year and subtracts the hours spent keeping an automation running.

Small automations have costs too. Each one is another thing that can break, and ten of them without owners is a mess of its own. Some repetition should be deleted rather than automated, such as a status report nobody reads.

Watching people work raises fair questions about consent, especially when software does the watching. A tally kept by the person themselves avoids most of that. Our explainer on task mining and process mining sets out what regulators say about software that records screens.

What this means for you: run a one-week tally

The tally takes a pen, a sheet of paper and five working days. It replaces estimates with counts, which is the whole point.

1. Pick one person who volunteers. Ideally someone who does a lot of routine work and wants less of it. Say plainly that the tally measures tasks, not performance, and that the sheet stays with them.
2. Count, never estimate. Put the sheet next to the keyboard. Each time they copy something from one system to another, look up something they looked up before, or retype data that exists elsewhere, they write the task once and add a tick each time it recurs. Note the rough minutes for one instance.
3. Set an hourly prompt. Wood’s participants had a watch chime every hour to prompt their entries. A recurring reminder on the hour does the same job and catches what slipped past.
4. Add one hour of watching. Midweek, a colleague sits beside them for an hour and notes every switch between applications. Compare that list with the sheet. The gaps are the habits the tally missed.
5. Score the list on Friday. For each item, multiply ticks by minutes. Then ask whether the rule fits in one sentence, whether the input keeps the same shape, what a mistake would cost, and who would own the automation.
6. Pick one and build it small. Take the highest-scoring item with a clear rule, stable input and a cheap mistake. Check it against the xkcd chart. Before it runs, the person whose work changes should see exactly what it will do and say yes. If saving email attachments by hand tops the list, our guide to saving Outlook attachments automatically covers three ways to build it.

Then wait a month and run the tally again. Once the loudest repetition is gone, the next one is easier to hear.

The students in the 2002 study learned what the research was about only at the end. What they did on autopilot became visible because a watch chimed every hour and asked. A sheet of paper by the keyboard can do that job in an office.

Sources

• Wendy Wood, Jeffrey M. Quinn and Deborah A. Kashy, Habits in Everyday Life: Thought, Emotion, and Action, Journal of Personality and Social Psychology 83(6), 2002.
• Douglas A. Parry and others, A systematic review and meta-analysis of discrepancies between logged and self-reported digital media use, Nature Human Behaviour, 2021; University of Bath summary, May 17, 2021.
• Jakob Nielsen, First Rule of Usability? Don’t Listen to Users, Nielsen Norman Group, August 4, 2001.
• Kim Flaherty, Contextual Inquiry: Inspire Design by Observing and Interviewing Users in Their Context, Nielsen Norman Group, December 6, 2020.
• McKinsey Global Institute, Harnessing automation for a future that works, January 12, 2017.
• McKinsey, The economic potential of generative AI: The next productivity frontier, June 14, 2023.
• Asana, How work about work gets in the way of real work, updated April 17, 2026.
• Randall Munroe, Is It Worth the Time?, xkcd 1205.