One lesson fundamentally changed how I evaluate automation: just because a process can be automated doesn't mean it should be.
Early in my career, I was captivated by the promise of automation. Like many operations leaders, I assumed that if technology could save time, it was automatically the better choice. What I learned was that efficiency on paper doesn't always translate into effectiveness in practice.
Today, I evaluate automation by asking one question: Does this make the work better for both the client and the team?
The return isn't measured only in labor hours saved. I look at adoption rates, error reduction, client experience, employee satisfaction, training requirements, and how much cognitive load we're actually removing. If a new system creates confusion, requires months of retraining, or introduces workarounds that employees immediately begin using, the projected ROI was probably overstated.
I've also learned that disruption has a cost. Every new platform, workflow, or automation asks people to change habits they've built over years. That change consumes time, attention, and trust. If the improvement isn't meaningful enough to justify that investment, sometimes the smartest operational decision is leaving a process alone.
The people closest to the work usually have the clearest perspective on whether an automation will actually solve a problem or simply shift it somewhere else. Before implementing a new system, I want input from the people who will use it every day. They often identify friction points leadership never sees, and involving them early leads to stronger adoption and better long-term results.