AI Won't Fix a Broken Process, It Will Just Make It Faster

A team I worked with was excited to use AI to automate a weekly reporting process that everyone agreed was painful. It took two people most of a day to compile, and it was a natural first candidate for AI-assisted automation. We built it. It worked. And it took me a few weeks to realize we'd made the wrong problem faster.

The reporting process was slow because the underlying data lived in four disconnected systems that didn't agree with each other, and someone had to manually reconcile the discrepancies before the numbers were trustworthy. AI made the compilation and formatting fast. It didn't and couldn't fix the fact that the underlying data was inconsistent. We now produced an unreliable report faster than before, which is arguably worse, because it looked polished enough that people trusted it more than they should have.

This is the pattern I now watch for on every AI initiative: automating a process before asking whether the process itself is actually the right one. Speed multiplies whatever you already have. If what you have is a good process with a slow, manual bottleneck, AI is a genuine win. If what you have is a broken process wearing a slow manual step as camouflage, AI just makes the brokenness harder to see.

The fix, in our case, wasn't more AI, it was going back and doing the unglamorous work of fixing the data reconciliation problem at the source, across the four systems, before layering automation back on top. That took longer than the automation project itself. It was also the part that actually mattered.Before automating anything with AI, I now ask one question first: if a very fast, very literal person did this process exactly as documented, would the output be trustworthy? If the honest answer is no, fix the process first. Automation isn't a substitute for that conversation, it's a multiplier that makes skipping it more expensive, not less.