Enterprise AI has moved beyond experimentation. The challenge now is building systems that deliver answers companies can trust—especially in highly regulated industries where accuracy, governance, and compliance are nonnegotiable.

In this episode, Gary and Scott welcome Evan Siegel of eGain back to the Macro AI Podcast. Drawing on his experience in financial services, customer experience, and large-scale contact center operations, Evan explains how organizations are moving from AI pilots toward practical, measurable deployment.

The conversation explores eGain’s expanding focus on banking and healthcare, why enterprise knowledge has become foundational infrastructure for AI, and how companies can reduce hallucinations by connecting AI systems to accurate, governed, and continuously maintained information.

They also discuss:

• What has changed most in enterprise AI over the past year
• The unique AI challenges facing banking and healthcare
• Why knowledge architecture may matter more than the latest foundation model
• How organizations can build accurate, explainable, and compliant AI systems
• The business metrics that demonstrate real AI value
• Whether enterprises will use one foundation model or orchestrate several
• The most common mistakes companies make when beginning their AI journey
• How AI agents could reshape customer service over the next three to five years

For business and technology leaders, this episode provides a practical look at what it takes to move from AI enthusiasm to trusted, governed, and measurable execution.

Featured guest: Evan Siegel, eGain

Follow the Macro AI Podcast for practical conversations about artificial intelligence, enterprise technology, and the strategies business leaders need to understand what comes next.

eGain

https://www.egain.com/

Send a Text to the AI Guides on the show!

About your AI Guides

Gary Sloper

https://www.linkedin.com/in/gsloper/

Scott Bryan

https://www.linkedin.com/in/scottjbryan/

Macro AI Website:

https://www.macroaipodcast.com/

Macro AI LinkedIn Page:

https://www.linkedin.com/company/macro-ai-podcast/

Gary's Free AI Readiness Assessment:

https://macronetservices.com/events/the-comprehensive-guide-to-ai-readiness

Scott's Content & Blog

https://www.macronomics.ai/blog

Kimi K3 is one of the most ambitious AI model launches of 2026—and it could reshape the global competition between Chinese and American AI companies.

In this episode of the Macro AI Podcast, Gary Sloper and Scott Bryan explain who built Kimi K3, how Moonshot AI created a 2.8-trillion-parameter mixture-of-experts model, and why its architecture is designed for long-running coding and agentic work.

Gary and Scott also clarify the frequently misunderstood difference between open-weight and open-source AI. They examine whether businesses will begin hosting models like Kimi K3 themselves, why most companies will still rely on managed infrastructure, and where smaller private models may deliver greater value.

The discussion also compares Kimi K3 with leading American open models from NVIDIA, Google, OpenAI, Meta and IBM. Finally, Gary and Scott address model distillation, data security, deployment costs, geopolitical risk and the questions executives should ask before adopting a Chinese AI model.

Listen for a practical business explanation of what Kimi K3 means for enterprise AI strategy.

Send a Text to the AI Guides on the show!

About your AI Guides

Gary Sloper

https://www.linkedin.com/in/gsloper/

Scott Bryan

https://www.linkedin.com/in/scottjbryan/

Macro AI Website:

https://www.macroaipodcast.com/

Macro AI LinkedIn Page:

https://www.linkedin.com/company/macro-ai-podcast/

Gary's Free AI Readiness Assessment:

https://macronetservices.com/events/the-comprehensive-guide-to-ai-readiness

Scott's Content & Blog

https://www.macronomics.ai/blog

Artificial intelligence is only as good as the data behind it. In this episode, we sit down with Jeff Lunsford, CEO of Tealium, to discuss why customer data has become one of the most strategic assets for enterprises embracing AI.

As organizations race to deploy AI applications, digital assistants, predictive analytics, and agentic workflows, many discover that fragmented, outdated, or poorly governed customer data becomes the biggest obstacle—not the AI model itself. Jeff shares how enterprises can move beyond traditional Customer Data Platforms (CDPs) to create real-time customer intelligence that powers meaningful AI outcomes.

During our conversation, we explored how the customer data landscape has evolved from the early days of tag management into today's world of real-time data orchestration, AI activation, and predictive decisioning. Jeff explains where Tealium fits within the modern enterprise architecture alongside data warehouses, cloud platforms, reverse ETL, and customer engagement systems.

We also discuss the importance of creating real-time customer context, enabling AI systems to make faster, more intelligent decisions while maintaining strong governance, privacy, consent management, and regulatory compliance. Jeff provides a practical overview of AIStream and explains how organizations can deliver AI-ready data to applications, models, and autonomous agents in real time.

The conversation also explores:

• Why data quality—not AI models—is often the biggest barrier to successful AI deployments
• The role of real-time customer context in improving personalization and customer experiences
• Predictive intelligence and AI-driven decisioning
• AI at the edge and real-time activation
• Building trusted AI through strong governance, privacy, and consent management
• Partner ecosystems spanning cloud providers, data platforms, and AI technologies
• Emerging trends including Model Context Protocol (MCP) and agentic AI workflows
• Practical advice for CIOs, CMOs, CDOs, and CEOs preparing their organizations for the next generation of AI

Jeff also shares career advice for students entering the workforce, discussing the skills that will remain valuable as AI continues to reshape nearly every industry.

Whether you're leading AI strategy, modernizing your customer data architecture, or simply trying to understand how AI creates business value beyond the model itself, this episode offers practical insights into one of the most important foundations of enterprise AI: trusted, real-time customer data.

Topics Covered

• Tealium overview and enterprise strategy
• Customer Data Platforms (CDPs)
• Real-time customer data and context
• Data orchestration and activation
• AI readiness
• AIStream
• Predictive intelligence
• AI decisioning
• Customer experience personalization
• Privacy, consent, and governance
• Data quality for AI
• Agentic AI and MCP
• Enterprise AI strategy
• AI careers and future workforce

If you enjoyed this episode, be sure to subscribe to The Macro AI Podcast, leave a review, and share it with colleagues interested in AI, enterprise architecture, customer data, and digital transformation.

Send a Text to the AI Guides on the show!

About your AI Guides

Gary Sloper

https://www.linkedin.com/in/gsloper/

Scott Bryan

https://www.linkedin.com/in/scottjbryan/

Macro AI Website:

https://www.macroaipodcast.com/

Macro AI LinkedIn Page:

https://www.linkedin.com/company/macro-ai-podcast/

Gary's Free AI Readiness Assessment:

https://macronetservices.com/events/the-comprehensive-guide-to-ai-readiness

Scott's Content & Blog

https://www.macronomics.ai/blog