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/

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Gary Sloper

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Scott Bryan

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