On this episode, we welcome Scott Brown, an architect at Andelyn Biosciences. Scott has a focus on data flow enablement, operational excellence and innovation to serve analytics and AI.

We discuss the critical importance of human-in-the-loop oversight, the evolving role of evaluation frameworks for AI outputs, and the broader shift toward hybrid AI systems that blend generative and traditional machine learning techniques. From building early, low-risk use cases to preparing for more advanced agentic AI applications, we highlight actionable strategies for responsibly scaling AI across industries.

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On this episode, we welcome Nan Li, an experienced AI and machine learning leader at Nationwide. Nan shares her insights from years of enabling AI and machine learning adoption within large enterprises, with a focus on workforce readiness, human-AI interaction, and operating model design.

We explore how enterprises are evolving to support AI adoption beyond technical teams, highlighting the shift from centralized innovation to more distributed, domain-embedded use. Additionally, we examine the emerging personas within an AI-enabled workforce—builders, users, and protectors—and the cultural and structural changes required to empower each group. From democratizing low-risk use cases to managing risk in high-impact workflows, we offer a practical look at how organizations can build an AI-ready workforce in a responsible, scalable way.

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In this episode, we welcome Mike Alvarez, Chief Technology Officer at NeuZeit, a consulting firm specializing in value acceleration, particularly for AI initiatives. Mike shares insights on agentic architectures, their role in automating complex workflows, the evolving landscape of AI governance, and how businesses can balance innovation with structured AI adoption.

We explore the challenges and opportunities in AI value acceleration, discussing how enterprises can move beyond experimentation to real, operationalized impact. We also examine key trends shaping AI in 2025, from advancements in AI-driven automation to the growing need for human-centered governance frameworks.

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This week, our hosts Nick Woo, Rehgan Bleile, and Brendan Kelly celebrate their 20th episode with a reflection on AI advancements in 2024 and predictions for 2025.

In this episode, the team dives into the diverse maturity levels of enterprise AI adoption, from early experimentation to large-scale deployment, the role of executive buy-in, and collaboration between business and technical teams. They highlight the rise of AI agents, growing reliance on data quality, and the shift toward optimization and governance as enterprises consolidate tools and processes. Looking ahead, they predict faster deployment cycles, deeper integration of AI into workflows, and a focus on user-centered design.

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On this episode, we welcome Bhavna Mehta, Assistant Vice President of Data and Analytics at Cincinnati Children’s Hospital Medical Center. Bhavna leads initiatives in data governance, AI governance, analytics, and business intelligence, leveraging her technical expertise and strategic insight to bridge the gap between innovation and practical implementation in healthcare.

We explore the critical role of AI ethics and governance in healthcare, focusing on explainability, trustworthiness, and alignment with organizational values. Additionally, we discuss how fostering a culture of data-driven innovation empowers organizations to adopt AI responsibly and effectively. Finally, we examine the transformative potential of AI when strategically aligned with priorities like improving patient care, reducing clinician burnout, and accelerating research impact.

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This week, our hosts Nick Woo, Rehgan Bleile, and Brendan Kelly return for a special Q&A episode, addressing listener questions about AI adoption and governance in large enterprises. They explore how organizations balance AI innovation with practical implementation, focusing on integrating AI into existing systems while managing data privacy and security.

The hosts dive into how enterprises can approach AI use case prioritization, weighing internal applications against more complex, customer-facing solutions. They also discuss the shift from building AI in-house to purchasing specialized solutions for greater efficiency. Additionally, they highlight the critical role of governance, including AI Centers of Excellence (COEs) and committees, in overseeing and guiding AI initiatives.

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This week, our hosts Rehgan Bleile (Avon), Nick Woo, and Brendan Kelly discuss the latest in AI, including AI regulation, synthetic data, and risk management. We discuss the push for watermarking AI-generated content to increase transparency and the challenges of enforcing these regulations. We then cover the risks of using synthetic data repeatedly for training, which can lead to a decline in AI model accuracy and reliability over time.

We also dive into how organizations are setting up AI Centers of Excellence (COEs) and councils to better manage AI initiatives and navigate the balance between innovation and risk. Additionally, we explore a shift from large, general-purpose models to smaller, specialized models that are better suited for specific tasks.

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On this episode, we welcome Kathy Koontz, who currently serves as the Global Head HCLS Data & AI at AWS. Her career includes significant roles at Nationwide, Teradata, and Wells Fargo, where she has focused on embedding core data practices into organizational frameworks.

During this episode, we dive into Kathy's expertise, gaining valuable insights for integrating core data fundamentals and practices. We explore how change management is essential for effectively deploying AI across organizations. Additionally, Kathy highlights the significance of understanding the content and quality of unstructured data, explaining that this knowledge is essential for accurately representing and achieving successful business outcomes.

Kathy Koontz

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