Free Guide
Most AI Implementations Fail. Yours Doesn't Have To.
95% of enterprise AI projects fail. We analyzed hundreds of implementations across construction, manufacturing, and physical industries to uncover why, and how to avoid the same fate.
What You'll Learn
Why 2/3 of in-house AI builds fail
MIT study insights on build vs. buy decisions
The $500M data mistake
How Zillow's dirty data cost them everything
The 76% talent gap trap
Why hiring your way out doesn't work
The 7% revenue risk
EU AI Act compliance you can't ignore
The 10 Mistakes Covered
1Building In-House Instead of Buying
2Treating Your Data Like Trash
3Chasing AI for AI's Sake
4Getting Stuck in Pilot Purgatory
5Trying to Hire Your Way Out
6Ignoring Change Management
7Underestimating the True Cost
8Failing to Plan for Integration
9Skipping Governance Until It's Too Late
10Expecting Magic Without Setup Work
Don't Let Your AI Project Become a Statistic
Download the guide and get actionable strategies for every stage of implementation.