AI may be making data easier to use, but the real breakthrough is helping more people make smarter decisions with it. Host Dr. Darren interviews Wei Zheng, Chief Product Officer at Conductor, for a history lesson in AI, data management, and the evolution of enterprise software from Silicon Valley’s .com boom to today’s AI-powered marketing era.

## Key Takeaways

- Data silos are still one of the biggest barriers to insight, even in modern cloud data platforms like Snowflake, Databricks, and Redshift.

- AI can help bridge the gap between business users and technical teams by turning natural language into useful data workflows.

- The future of data is less about replacing people and more about augmenting teams with faster, smarter tools.

- Search engine optimization is evolving into AEO and GEO, helping brands get discovered in AI tools like ChatGPT, Gemini, and Claude.

- Companies that unify search, website, and campaign data can make better marketing decisions and improve attribution.

- The same lesson from the .com boom still applies today: timing, product-market fit, and execution matter as much as the idea itself.

## Chapters

- 00:00 Introduction: AI, data, and the next revolution

- 01:13 Wei Zheng's origin story

- 05:05 Lessons from the .com boom and Webvan

- 09:20 How product management bridges business and technology

- 12:08 Why data became the backbone of enterprise software

- 16:10 Democratizing data for non-technical users

- 19:40 Why data silos still slow companies down

- 23:05 AI as a bridge between business users and engineers

- 27:15 Augmented teams and the future of work

- 31:10 What Conductor does: SEO, AEO, and GEO

- 35:00 How marketers can use unified data to get found in AI search

- 38:20 Closing thoughts and where to learn more

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

Most organizations do not fail at digital transformation because they lack ideas. They fail because the ideas do not survive contact with day-to-day work. A new capability is announced, a pilot begins, the diagram looks promising, and then reality gets in the way: handoffs are unclear, approvals are inconsistent, ownership is fuzzy, and the work that matters most is still done by habit rather than by design. That gap between intention and execution is the problem the FORGE practices are meant to address.

Execution breaks down when the Process Domain and Digital Domain drift apart, leaving technology to quietly reshape how work actually happens. This episode explores common failure points in process-to-technology mappings, including workflow, decision rights, evidence, data dependencies, exceptions, and feedback. It also explains how Governance and Risk Management make those mappings explicit, controlled, and resilient.

Summary