Enterprise AI Fails on Boring Problems

I've watched dozens of enterprise AI projects. The ones that fail rarely fail because the model wasn't good enough.

They fail on problems nobody wants to talk about at conferences.

The actual failure modes

Data quality. Your LLM is only as good as what you feed it. Most enterprise data is messy, inconsistent, spread across systems that don't talk to each other. Cleaning this up isn't exciting. It's also where projects should spend 60% of their time.

Integration. The demo works great in a notebook. Then you need to connect it to SAP. And Salesforce. And that legacy system from 2003 that nobody understands but everyone depends on.

Change management. You built an AI assistant that saves 30 minutes per task. Nobody uses it. It wasn't part of their workflow. Their manager didn't push it. The training was a one-hour webinar three months ago.

Evaluation. How do you know if your AI is working? Most teams can't answer this. They have vibes, not metrics. When something breaks, they find out from users.

What I've seen work

Start small. Not "transform the enterprise with AI." More like "automate this one report that takes 4 hours every week."

Pick boring use cases. Document classification. Data extraction. Summarization. Clear success criteria. Measurable ROI.

Invest in evaluation before you build. Define how you'll know if it's working. Build your test set. Instrument everything.

Plan for humans in the loop. AI that needs 100% accuracy to be useful will never be useful. Design for review. Make corrections easy. Use those corrections to improve.

The uncomfortable part

Most enterprise AI value comes from automation, not intelligence. You're replacing manual copy-paste, not human judgment.

That's not a criticism. These problems are well-defined and valuable. Solve them first.

The ambitious stuff—AI that thinks like a human—maybe tackle that after you've shipped something that works.