Why 95% of AI Projects Fail and How to Succeed #358

Host *Dr. Darren* sits down with *Michael Chavira**, co-founder and managing partner of **Axiologic**, to unpack the real reasons **AI projects fail**. From AI governance and workflow redesign to training, adoption, and ROI, this conversation shows why successful **AI implementation* is rarely a plug-and-play solution—and why people, process, policy, and technology all have to move together. Key Takeaways **AI amplifies existing systems**: If your workflows are broken, AI will usually make the problems bigger, not better. **Start with AI maturity assessment**: Before buying tools, determine where your organization is actually ready for AI adoption. **Fix the process first**: Many AI failures come from outdated workflows, poor training, and disconnected tools. **Governance matters**: Clear AI policies, data protection rules, and shadow AI controls are essential for organizations. **Pilot before scaling**: Choose one use case, prove value, and measure ROI before rolling AI out across the business. **Use AI to support people, not replace thinking**: The best results come from practical, specialized AI tools that fit real work. Chapters *00:00* Intro and the 95% AI failure problem *01:40* Michael Cervera’s origin story *05:10* Why AI projects struggle to deliver ROI *09:30* People, process, policy, and technology *13:05* Workflow problems and the JIRA example *17:20* AI governance, shadow AI, and data leakage *22:15* Choosing the right AI pilot *26:10* Building vs. buying AI tools *30:00* Vibe coding, prototypes, and operationalizing AI *34:00* Dissertation research and what’s next *37:10* How to connect with Michael and Axios Logic Solutions Audio Episode: https://share.transistor.fm/s/f1b03c8c Blog Post: https://embracingdigital.org/en/episo...