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

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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 - **Fix the process first**: Many AI failures come from outdated workflows, poor training, and disconnected tools.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