@vasuman: The most common reasons why AI pilots fail, even at the 'best' companies: In order of prevalence: 1. Not starting with …

The most common reasons why AI pilots fail, even at the 'best' companies: In order of prevalence: 1. Not starting with a thorough process mapping exercise (standard operating procedures vs. what’s actually happening across systems) 2. Automating a process step instead of the entire process (saving 8 mins in a 22-day process still leaves you with a 22-day process) 3. Not capturing baseline metrics for cycle time, cost per transaction, error rate, etc. 4. Building a sidekick instead of a background agent (chatbots will always only be used by ~10% of users) 5. Not embedding the AI into the organization’s existing systems of record (if users have to login, adoption will be low) 6. Not assigning an AI owner (IT built it, the business tolerated it, and no one was “on call” when it broke) 7. Not engaging legal, infosec, and finance early (we routinely see pilots “discover” these groups in month 3 - and get immediately killed) 8. Not scoping for exceptions (the “demo” only covers the “happy path” - but exceptions often represent 40%+ of process volume) 9. Not running the AI in “shadow mode” for a period of time to build confidence (going straight to production often leads to an AI mishap in front of the CFO) 10. Building with only a single AI model provider (one price hike or deprecation later, the pilot is a migration project) 11. Focusing on vanity metrics (e.g. # of logins, “adoption”) instead of business metrics (time saved, $ generated, risk reduced) 12. Not planning for month 7 (who will own and pay for the AI?) 13. Not engaging the subject matter experts (if you capture all their knowledge in an AI, what incentive do they have to help?) Varick does enterprise AI the right way. Work with us below: