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When employees keep spreadsheets or manual steps after an automation launches, the cause may be a mismatch between the tool and the work—not a software bug. If the system adds data entry, mishandles exceptions, fails to connect to existing tools, or leaves people unsure what decisions they still own, staff may route around it to get the job done. That behavior is an operational warning: investigate the workflow, training, trust, and accountability before treating the problem as a technical defect.
Why do employees work around an automation?
People bypass an automated workflow when following it is harder, slower, riskier, or less clear than using the method they already trust. The automation may run as designed and still fail operationally: it can be technically functional but poorly fitted to the work people must complete.
Common causes include extra data entry, incomplete handling of unusual cases, disconnected systems, insufficient training, and uncertainty about who is accountable when an automated decision is wrong. Concerns about job security or loss of decision-making authority can also reduce trust. A workaround is therefore evidence to investigate—not proof of laziness, resistance, or a particular software defect.
The new process adds work instead of removing it
If employees must enter the same information in two places, check an automated result manually, or maintain a separate tracker for work the system cannot represent, the automation has not removed the burden. It may have shifted effort from one part of the process to another. HealthIT identifies increased cost or effort, limited productivity improvement, and data-consistency problems among barriers to workflow automation.
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The system does not match the real workflow
Plans and diagrams often describe the standard path, while the people doing the work know the exceptions: missing information, approvals, corrections, urgent cases, and handoffs. If an automation supports only the standard path, workers may use a spreadsheet or side channel for the cases that do not fit. A design that ignores those exceptions can appear successful in a demonstration and become an obstacle in daily use.
Workers do not know what the change means for them
Employees may worry about job loss, reduced authority, unclear responsibilities, or having their expertise disregarded. James Benneyan, a professor of mechanical and industrial engineering, describes the alternative: “Automation should be about freeing people to do work at the top of their training and specialty instead of throwing [automation solutions] at them, which are intended to replace them.” That distinction matters in practice: explain which tasks are changing, which decisions remain human, and where workers can raise concerns.
Training, integration, and accountability are weak
In Statistics Canada’s 2022 Survey of Advanced Technology, 20.2% of enterprises rated lack of employee training a significant obstacle; 19.5% rated difficulty integrating new technologies with existing systems, standards, and processes significant; and 17% rated employee resistance to change significant. These are reported obstacles in that survey, not a measure of how many automation projects fail.
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Does a workaround mean the automation is broken?
Not necessarily. A bug is a technical defect; a workaround is a behavior that may signal a defect, a workflow mismatch, an integration gap, a policy problem, or a usability and training issue. Diagnose the observed failure before deciding which one it is.
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For example, if the automation produces an incorrect result for a valid input, investigate its logic and data. If it produces the expected result but employees still maintain a spreadsheet because they need an approval or exception the system does not support, the underlying process or system design may be the issue. If users do not know how to handle a flagged case, the process may need clearer instructions and role-based training. These causes can overlap; a technical fix alone will not correct a missing approval path or unclear ownership.
Use the symptom to choose what to inspect
| What you observe | What to investigate |
|---|---|
| Repeated re-entry of the same information | Integration, duplicate steps, and whether systems share consistent data |
| Side spreadsheets for particular cases | Exceptions, missing fields or approvals, and whether the formal process covers those cases |
| Employees check or override routine results | Result quality, explainability, decision rights, and whether workers know how to challenge an outcome |
| Users abandon the workflow after launch | Training, process fit, support, workload, and whether the pilot included representative users and cases |
This is a diagnostic starting point, not a substitute for examining the actual process. Ask staff what triggers the workaround, what they do instead, and what would happen if they did not take that extra step.
Why can automation work in a pilot but fail in production?
A pilot can omit the volume, handoffs, exceptions, integrations, and role differences that shape everyday operations. It may prove that a tool can complete a narrow task without proving that the end-to-end process is workable for all affected employees.
Gartner reported in 2026 that 28% of surveyed infrastructure-and-operations AI use cases fully succeeded and met ROI expectations, while 20% failed outright. Gartner attributed failures largely to initiatives that were overly ambitious, poorly scoped, or did not fit operations. Those figures refer to the surveyed I&O AI use cases, not to all business automation projects. Gartner’s Q&A states the operational point directly: “AI that doesn’t fit into the organization’s operations simply can’t deliver ROI.”
Before expanding a pilot, test the complete path of work, not just the automated task. Include the people who handle exceptions, the systems that exchange data, and the controls needed when a result is uncertain or unsafe. Define what success means in measurable operational terms, and check whether the pilot’s assumptions still hold at production scale.
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How can leaders detect workarounds before adoption collapses?
Measure how work is actually completed, not only whether the automation is online. Uptime can confirm availability, but it cannot show whether employees are duplicating entries, moving work into shadow spreadsheets, or correcting results outside the system.
Ask workers and observe the process
Map the current process with the people who perform it, including handoffs, exceptions, informal checks, and side files. Ask which steps the automation removes, which it adds, and what workers do when it cannot handle a case. HealthIT emphasizes end-user engagement during development and implementation, alongside process redesign, support, adoption, and training.
Track operational signals alongside technical health
- Workaround frequency: how often people use a side spreadsheet, manual handoff, or alternate route for a defined task.
- Rework: how often an automated result or record needs correction.
- Exception volume: how many cases leave the standard path, and whether the system supports their resolution.
- Completion time: how long the end-to-end task takes, including checks and re-entry.
- User-reported friction: recurring complaints, confusing steps, or uncertainty about the next action.
Define these measures before rollout and compare like with like. For instance, a higher exception count after launch could mean the process has become harder, or that the organization is now recording exceptions that were previously invisible. Talk with users before interpreting a metric as success or failure.
The prevalence of workarounds is not trivial. Smartsheet’s 2026 Operational Excellence Report found that 76% of surveyed operations professionals relied on manual workarounds, while 61% lacked full visibility into their work and only 8% believed they had achieved operational excellence. These are survey findings about operations professionals, not a universal rate of automation failure. They do show why a project team should make informal work visible instead of assuming the documented process tells the whole story.
What should leaders do before expanding an automation?
Start with the work, then decide what to automate. A practical rollout makes the process explicit, limits the first objective, prepares affected employees, and gives the organization a way to detect and correct problems.
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- Map the process as performed. Document the normal path, exceptions, handoffs, data sources, approvals, and unofficial tools. Include people who do the work, not just process owners.
- Remove unnecessary steps before automating. Redesign duplicate checks or avoidable handoffs where appropriate; otherwise, automation can make an inefficient process faster to repeat without making it better.
- Choose a narrow, measurable outcome. Set realistic scope and ROI assumptions. Define the task, users, cases, baseline, and expected result so that the pilot tests an operational improvement rather than ambition alone.
- Test integration and data quality. Confirm that required systems exchange the needed information and that staff are not forced to re-enter or reconcile it. Test representative cases, including exceptions.
- Train by role and clarify decision rights. Show users how to complete their part of the process, what to do when the system cannot proceed, which decisions remain human, and how to escalate an incorrect or unsafe result.
- Set ownership and feedback routes. Name who is responsible for reviewing a wrong or unsafe outcome and give workers a clear channel to report friction or propose changes. OECD recommends worker consultation in algorithmic-management contexts.
- Review operational evidence before scaling. Check workaround frequency, rework, exceptions, completion time, and user feedback alongside technical availability. Revise the workflow when the evidence shows people are routing around it.
Process redesign is a financial issue as well as an adoption issue. Camunda reported in 2026 that 72% of organizations said process-related challenges caused AI initiatives to fail, at an average reported cost of $1.55 million per business. These are reported findings about AI initiatives and process-related challenges; they are not a guaranteed cost or failure rate for an individual automation project.
How should you compare two automation approaches?
Compare how each option changes the full work process, including what happens when the standard path fails. A feature checklist alone can miss whether staff can use the tool with existing systems, understand its decisions, and recover from exceptions.
| Decision factor | Question to ask |
|---|---|
| End-to-end workflow fit | Does it support the real process, including exceptions and handoffs? |
| Integration and data quality | Does it work with existing systems, standards, and reliable data? |
| Training burden | What must each role learn, and how will users get support? |
| Worker control and explainability | Can people understand relevant outputs and know when they can intervene? |
| Accountability | Who reviews an incorrect or unsafe outcome, and how is it escalated? |
| Workaround visibility | Can the organization detect side processes and measure their frequency? |
| Outcome and ROI | Is the expected improvement specific, measurable, and realistic for the scope? |
| Governance and revision | Can workers give feedback, and can the process be changed when evidence warrants it? |
What does the evidence say about automation failure?
Available findings support the view that workflow fit, training, integration, worker concerns, and process design are material issues. They do not establish what share of all automation projects fail specifically because employees work around them.
The distinction matters: the figures above measure different populations and outcomes. Statistics Canada reports enterprises’ ratings of technology obstacles; OECD reports adoption and concerns related to algorithmic-management tools; Gartner’s figures concern surveyed I&O AI use cases; and Camunda reports organizations’ views of process-related AI failures. Smartsheet reports workarounds among surveyed operations professionals. None is a single, universal measure of the precise causal claim that staff workarounds kill a particular proportion of automations.
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So treat a workaround as a signal to investigate rather than a verdict. The practical question is what makes the alternate route necessary, and whether the right remedy is a technical correction, integration work, process redesign, training, clearer accountability, or a combination.
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Frequently Asked Questions
Why are employees still using spreadsheets after we automated the process?
A spreadsheet may be serving as an unofficial place to track exceptions, approvals, or information the formal workflow does not capture. Ask users what the file helps them accomplish and what would break if they stopped maintaining it; that reveals whether the gap is in the process, data, or system design.
How do I know whether employees are bypassing a workflow?
Look for repeated manual corrections, duplicate records, work completed outside the expected system, and discrepancies between system activity and observed work. Pair logs with confidential, non-punitive conversations so employees can explain why they use an alternate route.
Should we disable the old manual process after automation goes live?
Not until the automated path has been validated for ordinary work, exceptions, and recovery from errors. Removing a fallback too early can make a flawed process harder to contain; set clear conditions for when it is safe to retire the parallel route.
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Assign an identifiable operational owner who can review the case, determine whether the issue is data, logic, process, or policy, and coordinate a correction. Users also need a practical escalation route rather than being left to guess whether they may override the result.
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