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1Repair Windows errors before they cause bigger problems2Scan for outdated or missing drivers - takes under a minute3Clear out junk files and repair common Windows errorsAutomation can remove repetitive steps without reducing the total work around them. Setup, upkeep, monitoring, exceptions, and coordination can absorb the time saved—or shift it to someone else. That does not mean automation always creates more work: outcomes depend on the process, the people doing it, and how the automated and human tasks are designed together.
Why does automation create more work?
Automation changes how work is done; it does not make the surrounding process disappear. A task that once involved direct manual effort may become a workflow that needs to be configured, checked, repaired, and adapted as conditions change. The extra work may fall on the original operator, a technical team, or colleagues who handle exceptions and handoffs.
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Maintenance grows as workflows and systems change
Automations rely on inputs, interfaces, dependencies, and business processes that can change. When they do, someone may need to correct defects, update dependencies, refactor workflows, fix bugs, or improve continuous integration and delivery. A 2024 IEEE study examined GitHub Actions workflow files in almost 200 mature projects across ten programming languages; those figures describe the study’s scope, not a general estimate of automation’s costs. The study’s available abstract identifies maintenance as relevant work, but does not support broader claims about its results. IEEE study on GitHub Actions workflow maintenance.
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Robotic process automation (RPA) can have a similar upkeep burden as deployments scale. Noppen and coauthors’ BPM 2020 chapter presents 11 guidelines for low-maintenance RPA implementations, based on literature and four case studies and validated with experts using a Delphi method. The public university record does not provide the full text of those guidelines, so the number should not be mistaken for a ready-made checklist. University of Ghent record for “How to Keep RPA Maintainable?”.
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Human oversight and workload shift rather than vanish
When automation takes over a routine action, people may still need to monitor it, intervene when something unusual happens, and manage the consequences of failures. The European Union Agency for Railways cautions that automation can increase or decrease mental workload depending on circumstances. It also warns that underload can harm performance, just as overload can. This is rail human-factors guidance, but its central point matters anywhere a person must supervise an automated process: removing hands-on activity does not automatically make the remaining job easier. ERA Automation Myth Busting Paper #3.
Implementation brings learning, coordination, and accountability work
Introducing automation often means learning new procedures, changing roles, and deciding who owns decisions and exceptions. In a process study of five robotization projects at one chemical-industry organization, Isaza and Cepa observed increased workload and changing motivation during implementation. They theorized that workload and skill requirements could expand at first and later contract after workable configurations emerged. The study also associated active employee inclusion with protecting accountability. This is evidence from one organization and industry, not a universal implementation timeline. Isaza and Cepa’s process study.
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Coordination can be another hidden cost. NIST’s manufacturing maintenance guidance describes a risk when human and digital knowledge systems remain separate and decisions are made case by case: people and machines can be disadvantaged even when they share goals. NIST recommends a hybrid approach that uses technology to augment human intelligence, and notes that implementation can be hindered when the organizational or cultural problem is unclear. NIST guidance on a hybrid-intelligence approach to smart manufacturing.
How can I avoid creating extra work when automating a process?
1. Define the problem before choosing a tool
Write down the bottleneck, who experiences it, and what outcome would count as an improvement. Is the goal to reduce delays, avoid repeated data entry, improve consistency, or free people for work that needs judgment? NIST’s manufacturing guidance warns that technology use is hindered when the core organizational or cultural problem is unclear. Starting with the problem helps avoid automating an awkward process without fixing its cause.
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2. Map the full process, including exceptions
Trace the work from input to outcome, rather than counting only the step that looks repetitive. Record handoffs, approvals, unusual cases, failures, and recovery work. Ask what happens when information is missing, an interface changes, or the automated step produces an unexpected result. This mapping is a practical way to expose work that might otherwise be shifted to operators or support teams; it is a recommendation drawn from the maintenance and human-work evidence, not a tested intervention with a universal result.
3. Design human and automated work together
Make ownership explicit: who monitors the system, makes decisions it cannot make, handles exceptions, and maintains the workflow? The hybrid approach in NIST’s guidance treats technology as a way to augment human intelligence rather than separating people from the process. Also check the demands on people after automation: a role can become more mentally demanding, or so passive that attention suffers. ERA’s rail guidance supports checking both overload and underload.
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4. Involve affected employees before implementation
Ask the people who perform or support the work where automation would help, which judgment should remain human, and what new responsibilities the change would create. The five-project chemical-industry study links active employee inclusion with preserving accountability as tasks change. Involvement can also surface exceptions and handoffs that are easy to miss from a process diagram alone.
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Decide who is responsible for monitoring, defect fixes, dependency and interface changes, documentation, and periodic review. Treat those duties as ongoing work in the operating plan, not as a temporary launch task. The RPA literature describes maintenance concerns as implementations scale, while the GitHub Actions study identifies workflow maintenance activities such as defect correction and dependency updates.
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6. Measure net work, not just task speed
Compare the time removed from the automated steps with time added for setup, review, exceptions, recovery, maintenance, and coordination. Measure the end-to-end process for the people affected, including work transferred to another team. This is a practical evaluation method, not a formula validated by the cited sources; the evidence does not establish one universal way to calculate automation’s net effect.
7. Pilot the redesigned process and adjust it
Test the automation in the intended setting, with real operators and representative exceptions. Check whether the full process improves, whether workload remains manageable, and whether the maintenance and handoff arrangements work. A supermarket cashier field experiment by Jie Gong and I. P. L. Png found that specialization enabled by automation increased scanning productivity by more than 10% in that studied setting. The result illustrates how task design can matter; it is not a forecast for other industries or processes. Gong and Png’s supermarket cashier field experiment.
Independent reader supportYour contribution helps us test, update, and keep practical guides available for everyone.What the evidence does—and does not—show
These sources document credible ways automation can add or shift work, as well as a case where task specialization paired with automation improved measured productivity. They cover different settings: manufacturing maintenance, railway human factors, GitHub Actions, RPA, a supermarket experiment, and robotization projects in one chemical-industry organization. They do not establish that automation always increases work, or provide a cross-industry figure for how much work it creates or saves. The useful question for a specific project is whether the complete process—including human oversight and lifecycle upkeep—works better after the change.
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