AI to production
From demo to daily use
AI projects usually fail for project reasons rather than model reasons. This is the project manager’s path through them.
RAND’s 2024 study reports estimates that more than 80% of AI projects fail, roughly twice the rate of other IT projects. It names five root causes: misunderstanding the problem, too little good data, focusing on technology rather than the problem, weak infrastructure, and problems beyond what AI can do.
Choosing an AI Use Case
Picks problems where AI can create value, and avoids expensive demos.
Defining AI Success
Agrees what good looks like before the model gets built.
AI Data Readiness
Checks you have the data before promising a result.
Evaluating AI Quality
Replaces “it looks good” with evidence.
From Pilot to Production
Gets past the demo that never ships.
Human in the Loop
Decides where people must stay in control.
AI Risk and Governance
Handles bias, privacy, security and regulation before they become incidents.
Running AI in Production
Keeps a live system good, safe and affordable.
Using AI in Your Own PM Work
Saves hours on project admin, without handing over your judgment.
When it goes wrong
Cases
Templates
Sources
- J. Ryseff, B. De Bruhl and S. Newberry, The Root Causes of Failure for Artificial Intelligence Projects (RAND, 2024)
- Gartner prediction on generative AI projects, as reported by TechRepublic
- NIST AI Risk Management Framework 1.0 (2023) and Generative AI Profile, NIST AI 600-1
- EU AI Act and Digital Omnibus on AI, Regulation (EU) 2026/1744, as reported by the National Law Review