The Automation That Failed and What We Learned

Our AI automation failed because we tried to automate human judgment. A law firm client intake system kept misclassifying cases—divorce cases went to business litigation, personal injury to estate planning. The fix: AI gathers data, humans make the final classification.

We do not like to talk about failures. But if we only shared our successes, we would be lying about what this work really looks like.

Last year, we built an automation for a law firm that was supposed to handle initial client intake. It worked perfectly in testing. Clients would describe their case, the AI would categorize it, and the right attorney would receive the lead.

In production, it was a disaster. The AI kept misclassifying cases. A divorce case would go to the business litigation team. A personal injury case would go to the estate planning attorney. Leads were slipping through the cracks.

We spent weeks trying to fix it. Better training data. More refined categories. Clearer instructions. Nothing worked consistently.

Finally, we had an honest conversation with the client. The problem was not the AI. The problem was that we had tried to automate a decision that required human judgment. The nuances of legal classification were too complex for the system we had built.

"

The companies that thrive are not those with the most technology, but those who apply technology most thoughtfully.

We refunded the project and rebuilt it differently. Instead of having AI make the classification, we had it gather information and present it to a human intake coordinator with a recommended category. The human made the final call.

That hybrid system works beautifully. The AI handles the tedious data gathering. The human handles the judgment call. Everyone wins.

The lesson we learned: automation should amplify human capability, not replace human judgment. When we forget that, we fail.