Why AI Fails in Engineering Teams—and How to Fix It

Most engineering leaders think they're "doing AI" because someone on the team spun up an LLM prototype or dropped ChatGPT into a workflow. But when it's time to integrate AI into the actual product? Nothing ships. That's because real AI readiness isn't about dabbling—it's about building systems where AI can live, learn, and adapt safely.

Here's what that looks like—and what your team can do this week to get closer:

✅ 1. You actually understand your system. Can your team point to where pricing is calculated? Or how access control works across services? If not, you're not ready for AI.

Start by documenting the 3 most business-critical flows. You don't need to map the whole system—just the parts where AI can unlock real value. This slicing is used at many companies in order to isolate and test results.

🧱 2. Your code is modular—AI can be dropped in without chaos. If your logic is buried in a 1,000-line function that talks to 10 other services, you can't plug AI in without risk.

Pick one workflow. Wrap it behind a clean interface. Then explore: how could AI augment or replace this? No rewrites needed—just isolation and intent.

🧪 3. You can test AI without praying to the demo gods. If validating AI means "click around the UI and hope it works," you're not ready.

You need simple, automated checks that verify:

• Did the AI follow the business rule?

• Would a human have done the same thing?

Start small, but make it measurable. Next week I will provide your team all the insights you need for an effective Eval.

📊 4. You monitor what AI does—like any other critical system. You need logs. You need alerts. You need to know:

• When AI is invoked

• What decisions it made

• And where it's failing silently

Don't wait until you're deep in production. Add observability from day one.

Bottom line: AI can't improve what you can't isolate, evaluate, or observe. But if you start with one slice of your system and get that right—AI won't just ship. It'll scale.

Want a deeper breakdown or example from a real enterprise codebase? Happy to share what we've seen across over 100+ applications. Just reply.

— Samai