7 Common AI Delivery Mistakes Teams Should Avoid

We've seen many AI programs stall. Here are seven common delivery mistakes.

1. Scope creep. Too many features. Ship one thing well.
2. Ignoring data quality. Garbage in, garbage out. Clean data first.
3. No user feedback loop. Build, measure, learn. Talk to users weekly.
4. Over-engineering. Use APIs before custom models.
5. Underestimating compliance. HIPAA, GDPR, SOC 2. Plan early.
6. Hiring too late. Get technical help before you're stuck.
7. No clear success metric. Define what "works" means before you start.