Case Study: Preventing Over-Automation by Simplifying the Workflow First
- Katina Ndlovu
- 1 day ago
- 3 min read
Context
This case involved a service-based business exploring additional automation to speed up delivery. Over time, layers of tools and automations had been added incrementally in response to small problems.
The result was a process that technically “worked” but required constant maintenance, troubleshooting, and explanation.
The Core Problem
The workflow had become over-engineered.
Key issues included:
Too many steps for simple outcomes
Automations triggering other automations unnecessarily
Manual overrides frequently required
High effort required to maintain and debug the system
Automation had increased complexity instead of reducing it.
Why This Was an Automation and Systems Issue
The issue was not insufficient automation.
The issue was that automation had been applied to steps that should not have existed in the first place. Each automated step added fragility and increased the surface area for failure.
The system needed simplification, not acceleration.
The Approach
The work focused on reducing the workflow before automating anything further.
Key actions included:
Mapping the full end-to-end process
Identifying steps that did not add value
Removing unnecessary handoffs and checks
Collapsing multiple automated steps into single actions
Retaining automation only where it reduced effort without adding risk
Automation was reduced deliberately.
What Changed
After simplification, the workflow became easier to understand and maintain.
There were fewer automations, but they were clearer, more reliable, and easier to monitor. Manual steps that remained were intentional and required less context to complete.
The system became lighter and more resilient.
Evidence of Operational Improvement
The improvement was visible in system stability.
Specifically:
Fewer automation failures and alerts
Reduced need for manual intervention
Less time spent troubleshooting workflows
Faster onboarding for anyone interacting with the system
Removing automation reduced operational noise.
Time and Cost Impact (Conservative Estimate)
Before simplification, troubleshooting and maintaining over-automation required approximately 45 to 75 minutes per day.
After removing unnecessary steps and automations, this dropped to approximately 10 to 15 minutes per day.
Estimated time saved:
20 to 30 hours per month
Using a conservative operational cost of $40 to $75 per hour, this represents:
$800 to $2,250 per month in recovered time capacity
This value came from reduced maintenance and intervention, not increased output.
Why This Matters for Automation and AI Support
More automation does not mean better systems.
This case demonstrates that judgement in not automating is just as valuable as knowing when to automate. Reliable systems are often simpler, not more complex.
Where This Pattern Commonly Appears
This issue frequently affects:
Businesses that automate reactively
Teams layering tools over time
Operations with complex exception handling
Systems no one fully understands anymore
Case Study: Relationship to Automation and AI Support Work
This case shows that automation and AI support is about system health, not maximum automation. Removing unnecessary steps is often the most effective optimisation.
FAQs
What does this case study demonstrate?
It shows that reducing or removing automation can improve reliability when workflows have become over-engineered.
Why was automation removed instead of expanded?
Because several automated steps were compensating for unnecessary complexity rather than solving real problems.
Does removing automation mean doing more work manually?
No. Removing unnecessary steps reduced overall effort by eliminating maintenance, troubleshooting, and manual overrides.
How do you decide which automation to remove?
By identifying steps that add little value, trigger frequent exceptions, or increase maintenance without reducing effort.
Is simplification a one-time exercise?
No. As systems evolve, periodic simplification prevents workflows from becoming brittle over time.
Who benefits most from this approach?
Businesses that have layered tools and automations incrementally and now experience frequent system friction.
Does this mean automation is discouraged?
No. Automation is valuable when it reduces effort. This case shows the importance of applying it selectively.
How does this relate to AI usage?
The same principle applies to AI. Removing unnecessary AI steps can improve clarity and accountability.
How Can I Help You?
If your systems feel fragile or difficult to maintain, simplification is often the fastest path to stability.
You can explore related automation and workflow case studies below or get in touch to review where automation may be adding complexity instead of reducing effort.
Author
Katina Ndlovu works with service-based businesses to design workflows, systems, and automation that reduce friction rather than add complexity. Her approach prioritises clarity, judgement, and reliability over maximal automation.
She documents applied systems work through case studies that show how simplifying workflows often delivers more value than adding new tools.
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