CDO Magazine Podcast Series

The best analytics teams do more than explain the past. They help shape the future.

What happens when commercial analytics shifts from reporting historical performance to enabling proactive decisions?

In this conversation with Subrato Chatterjee, Global Growth Partner for Life Sciences at Tiger Analytics, Ashutosh Katiyar, Executive Director, Commercial Strategy, Insights & Analytics at Regeneron, shares how organizations can build intelligence capabilities that identify signals early, support scenario planning, and create stronger connections between analytics and patient outcomes.

AI can automate workflows. It can also amplify risk if trust and governance fall behind.

In the last episode of the interview series with CDO Magazine Editorial Board Member Justin Heller, Monte Carlo CEO Barr Moses explains why data quality, workforce readiness, and AI governance will determine which enterprises lead the next phase of transformation.

How does Procter & Gamble scale AI safely while maintaining a competitive edge? Enterprise AI VP Jeff Goldman shares how the company manages model risk and drives continuous algorithmic innovation.

AI tools are becoming commoditized. Data foundations are not.

Enstar Group CDO David Tuppen explains why governance, automation, and trusted data matter more than the next AI platform.

In Part 2 of this three-part conversation, Jeff Goldman, VP, Enterprise Data Science at Procter & Gamble, joins Donna Medeiros of Data Society Group to discuss what it really takes to scale AI across a global enterprise.

Goldman explains how P&G built long-term AI fluency programs, launched internal generative AI tools like ChatPG, and trained more than 20,000 employees through its decentralized Formula AI initiative.

The conversation also explores:

Listen now.<...

As AI agents move deeper into enterprise workflows, organizations are confronting difficult questions around autonomy, oversight, security, and trust.

In Part 2 of this three-part conversation, Monte Carlo CEO Barr Moses joins data leader Justin Heller to examine:

A practical discussion on what responsible AI deployment actually looks like at enterprise scale.

In this episode, Enstar Group Chief Data Officer David Tuppen discusses the realities of enterprise modernization inside highly regulated environments and why AI success still depends on trusted data foundations.

Topics include:

In conversation with Robert Lutton, Vice President at Sandhill Consultants.

Former Qlik CEO Mike Capone joins Deloitte’s Dr. Adita Karkera for the final part of our three-part

series on the future of enterprise AI.

The conversation explores:

• Agentic AI use cases delivering value today

• Board-level AI leadership expectations

• Why trusted partnerships matter more than ever

• How Qlik + Deloitte view the next five years of AI transformation

Listen now.

What does it actually take to scale AI inside a global enterprise?

In this episode, Donna Medeiros, Vice President of AI and Data Advisory at Data Society Group, speaks with Jeff Goldman, Vice President, Enterprise Data Science at Procter & Gamble, about how P&G operationalizes AI through a combination of data science, AI engineering, and its AI Factory model.

The conversation covers:

• Enterprise AI operating models

• Scaling AI across global markets

• Embedding AI teams into the business

• Why infrastructure and engineering determine AI success

• How P&G measur...

As healthcare organizations race to operationalize AI, leaders must balance innovation with governance, interoperability, and patient outcomes.

In this episode, former L.A. Care Health Plan CIO Tom MacDougall speaks with Robert Lutton of Sandhill Consultants about responsible AI adoption in healthcare, why organizations are still in the early stages of operational AI maturity, and how trusted, standardized data will define the next phase of healthcare transformation.

The discussion also explores:

• AI governance in regulated environments

• Real-world healthcare analytics use cases

• Provider benchmarking and care optimization

• The role of...

In the final part of this three-part CDO Magazine series, Karthik Yajurvedi, Chief Data Officer for the Commonwealth of Massachusetts, joins Deloitte’s Adita Karkera for a conversation on what it really takes to operationalize data sharing across government agencies.

The discussion explores governance without bottlenecks, building trust across silos, context-based data quality in the age of AI, and why modern CDOs must increasingly operate as strategic business leaders rather than purely technical executives.

Listen now.

How do enterprises build AI systems that employees actually trust?

In Part 2 of this three-part conversation, former Qlik CEO Mike Capone joins Deloitte’s Dr. Adita Karkera to explore responsible AI deployment, AI literacy, transparency, and the cultural changes required to scale AI successfully.

The discussion covers:

Listen now on the CDO Magazine Podcast.

What changes when AI agents, not just humans, become consumers of enterprise data?

In this conversation, Monte Carlo CEO Barr Moses joins CDO Magazine Editorial Board Member Justin Heller to break down:

• The shift to agent-first data trust

• Why AI governance is becoming more complex

• The risks of poorly governed AI agents

• Why enterprises cannot delay AI experimentation waiting for “perfect” data

Part 1 of a three-part series exploring the future of enterprise AI, governance, and trusted data.

What separates isolated AI success from scalable enterprise capability?

In this episode, Deepak Shah, Chief Data Officer at the U.S. Army Western Hemisphere Command, discusses why operational discipline, governance, and trusted data foundations are becoming essential for enterprise AI scale.

The conversation examines:

• Why frameworks, playbooks, and runbooks matter

• How trust in AI depends on traceability and accountability

• The risks of relying on “heroics” instead of repeatable systems

• What real enterprise AI maturity looks like

An insightful discussion for data and AI leaders focused on long-term...

What does responsible AI actually look like inside a healthcare payer organization?

In this podcast episode, former L.A. Care Health Plan CIO & CTO Tom MacDougall discusses how healthcare organizations are rethinking data strategy as rising costs, regulatory pressure, and real-time care demands reshape the industry.

Key discussion points include:

• Why healthcare payers are moving beyond claims-driven data models

• How interoperability is bringing data closer to the point of care

• Building continuously validated clinical data repositories

• Why human-in-the-loop AI remains essential in healthcare decision-making

• How L.A. Care...

What actually determines whether enterprise AI succeeds?

According to former Qlik CEO Mike Capone, it’s no longer the models.

It’s the harder work underneath:

🔹Trusted data foundations

🔹Governance and lineage

🔹System alignment

🔹Operational trust

🔹Decision intelligence at scale

In conversation with Dr. Adita Karkera of Deloitte, Capone explains why organizations moving toward automation and agentic AI must rethink how they operationalize trust across enterprise systems.

The discussion also explores:

• Why many AI initiatives fail to produce measurable outcomes

• Why...

In Part 2 of this series, Massachusetts CDO Karthik Yajurvedi breaks down what it actually takes to make data sharing work across government agencies.

From early childhood systems to workforce programs, the conversation explores how secure, trusted data sharing enables real outcomes for citizens.

Key themes include:

🔷 Why fragmented data limits policy decisions

🔷 The role of trust, privacy, and compliance

🔷 How modern platforms and agreements enable collaboration

🔷 What production use cases look like in practice

Listen now.

Most organizations are not short on data or technology. They are short on alignment.

In this episode, Vincent Brown, Regional Head of IT for the Americas at Fugro, joins Michael Sutter, CEO of Enlivened Tech, to unpack why transformation efforts fail when foundational steps are skipped.

Brown introduces the “Swiss cheese model” of misalignment and explains how gaps in awareness, ownership, and accountability derail outcomes.

You’ll learn:

🔹Why awareness must come before execution

🔹How to define and operationalize data ownership

🔹Why IT enables data, but doesn’t own it

🔹W...

Most data initiatives don’t fail during execution. They fail at the start.

In this episode, Deepak Shah, Chief Data Officer at the U.S. Army Western Hemisphere Command, breaks down what it takes to build enterprise data initiatives that scale and endure.

🔹Why most data initiatives fail before execution and how to fix the starting point

🔹Using vision, mission, and goals as execution discipline

🔹What “solve and scale” actually requires: structured intake, prioritization, and governance

🔹Why trust, not the latest models or tools, determines whether data and AI initiatives scale<...

AI in healthcare is scaling fast. Coordination is not.

In this final part of a three-part series, Nasim Eftekhari, Chief AI and Analytics Officer at City of Hope, joins Erik Pupo of Guidehouse to examine what it takes to orchestrate AI at scale in oncology.

Key takeaways:

• Why agent orchestration is the next enterprise AI challenge

• What happens when AI agents don’t communicate

• How multimodal AI expands clinical intelligence

• The data foundation still missing in healthcare

• What defines scalable AI in oncology

In conversat...

In Part 1 of this three-part series, the Chief Data Officer for the Commonwealth of Massachusetts discusses the foundations of public-sector data leadership. The interview explores how the role is structured, the similarities and differences between public and private sector data leadership, and the importance of storytelling in driving alignment and trust.

In conversation with Adita Karkera, Chief Data Officer for Deloitte’s Government and Public Services.

Scaling data and AI is not just a technical challenge. It is a series of decisions that shape how fast you move, how much control you retain, and whether your strategy holds up over time.

In this episode, Katrin Botzen, Corporate Director, Global Data and Analytics at Henkel, breaks down what the company got wrong in its early data and AI journey, and how those lessons now guide its approach.

The conversation with Julian Schirmer of OAO, focuses on practical, enterprise realities, from embedding governance and security into systems from the start, to making smarter...

Most organizations are still stuck in AI pilots. A few are scaling.

What’s the difference?

In this episode, Truist’s Sanjay Sankolli breaks down the operating model shifts required to move from isolated AI success to enterprise-wide impact.

You’ll hear:

🔷Why governance should act as a guardrail, not a gate

🔷How decision latency quietly slows innovation

🔷What it means to move from AI tools to AI as an operating capability

🔷Why data, trust, and alignment matter more than the number of AI models

This is the f...

How to Scale Oncology AI Safely: AI Governance Lessons from Hope LLM

As AI adoption accelerates in healthcare, the real challenge is no longer proving value. It’s scaling responsibly.

In this episode, Nasim Eftekhari, Chief AI and Analytics Officer at City of Hope, joins Erik Pupo of Guidehouse to unpack how Hope LLM is transforming oncology workflows across care, clinical trials, and research.

Key themes:

🔹Turning fragmented oncology data into real-time clinical insight

🔹Reducing clinician burden and reclaiming “pajama time”

🔹Embedding clinical trial matching into care delivery

...

AI adoption isn’t just about building models — it’s about building confidence across the enterprise.

In Part 3 of this four-part series, Truist’s Sanjay Sankolli joins NayaOne CEO Karan Jain to explore how organizations can move faster on AI without compromising governance or risk.

This episode covers:

🔷Why governance should act as a guardrail, not a bottleneck

🔷How to focus AI efforts using value stream mapping

🔷What “production-ready” AI really means in regulated environments

🔷Why pre-mortems are critical for building enterprise confidence

🔷How leading organizations are shifting...

In this episode, Katrin Botzen, Corporate Director, Global Data and Analytics at Henkel, breaks down how data products are being operationalized inside a global enterprise.

As analytics moves closer to where data is created, organizations must rethink ownership, governance, and alignment across distributed teams.

Botzen shares practical insights on:

🔷Building data products aligned to domains

🔷Embedding governance through contracts and transparency

🔷Avoiding duplication through discoverability and standards

🔷Identifying high-value data products across finance, supply chain, and sales

🔷Why the “people factor” is the biggest barrier to scale

A pr...

In this episode, Sanjay Sankolli, Chief Architect for AI and Data at Truist Financial Corporation, explains where AI is delivering real, measurable impact today inside a large, regulated enterprise.

Moving beyond the hype around autonomous AI, Sankolli highlights how organizations are seeing gains through decision augmentation, workflow acceleration, and improved developer productivity.

Key topics include:

🔹Why augmentation is outperforming automation today

🔹How agentic workflows are evolving beyond traditional RPA

🔹The growing importance of unstructured data in enterprise AI

🔹Why developer productivity is a critical, overlooked enabler

🔹The role...

AI in oncology is often discussed in terms of breakthroughs. But inside leading cancer centers, the focus is much more practical: what works, for whom, and how do you measure success?

In this first episode of a four-part series, Nasim Eftekhari, Chief AI and Analytics Officer at City of Hope, joins Erik Pupo of Guidehouse to examine how oncology AI is evaluated in real-world settings.

Eftekhari outlines why performance metrics take priority over financial return, why FDA-approved solutions still require local validation, and where AI is already delivering measurable impact across diagnostics and clinical workflows.<...

In the final part of this three-part series, Edgar Gallo, Chief Data Officer at Daimler Truck North America, shifts the conversation from AI use cases to what it actually takes to scale AI inside an enterprise.

The discussion explores the human and operational realities of AI adoption, including how leaders build trust, design ownership into the process, and create safe environments for responsible use.

In conversation with Susan Wilson of Alation, Gallo also shares why adoption matters more than model sophistication, and how the next phase of AI will depend on interoperability across enterprise ecosystems.<...

In this episode, Katrin Botzen, Corporate Director of Global Data and Analytics at Henkel, shares how the company applies AI across R&D, supply chain, and finance to solve real business problems.

The discussion explores:

🔷Why starting with business problems is critical for AI success

🔷How fragmented data limits decision-making

🔷The role of data architecture and governance in scaling AI

🔷Real-world use cases, from R&D optimization to finance insights

In conversation with Julian Schirmer of OAO.

This episode offers a grounded view of enterprise AI, focusing...

In this episode, Sanjay Sankolli, Chief Architect for AI and Data at Truist, joins Karan Jain, Founder and CEO of NayaOne, to explore why enterprise AI often stalls before delivering real value.

The conversation focuses on the structural challenges behind AI adoption, including weak data foundations, misaligned operating models, and the complexity of scaling in regulated environments.

You’ll hear:

🔹Why AI must be treated as an operating model shift, not just a technology upgrade

🔹What changes when moving from pilot to production

🔹Why the “last mile” is where most AI initiat...

AI might be the headline, but execution is the story.

In the final part of this three-part series, Philippe Rambach gets practical about what it takes to make AI work inside a large enterprise.

This episode explores:

🔸Why most of AI transformation is really change management

🔸What goes wrong when AI is introduced as a new tool

🔸How embedding AI into existing workflows drives adoption

🔸Why training 140,000 employees is not optional

🔸And how AI leaders spend their time recalibrating expectations

“If you need 100% accuracy, let’s n...

In this episode, Edgar Gallo, Chief Data Officer at Daimler Truck North America, shares how agentic AI is being applied to real-world supply chain challenges.

The discussion with Susan Wilson of Alation, focuses on how AI agents can reduce repetitive work, support planners, and improve responsiveness in complex manufacturing environments.

Gallo explains how breaking work into decision-making and execution enables AI to handle structured tasks while preserving human judgment where it matters most.

The conversation also explores how trust is built, not through technology alone, but when AI and human reasoning consistently arrive...

As organizations invest heavily in data, analytics, and AI, one challenge continues to hold many initiatives back: adoption.

In this episode, Mars’ Ujjwal Sehgal, Global Head of People Analytics, and Rachel Belino, HR Data Officer, discuss how the company is tackling this issue across its global workforce analytics initiatives.

Speaking with Shachin Prabhat of Tiger Analytics, they explore the barriers that often prevent analytics solutions from gaining traction, including complex dashboards, the lack of trust in data, and the challenge of delivering insights at the right moment.

They also explain how Mars approaches go...

In the final episode of this three-part series, Apurva Wadodkar, Senior Director and Head of Data and AI at TI Automotive, discusses how organizations can drive real adoption of data products across the enterprise.

In conversation with Merav Yuravlivker, Chief Learning Officer at Data Society, Wadodkar introduces the “Data Buffet” framework, which organizes data products by business domain and brings stakeholders directly into the design process.

Key insights include:

💠Why data product adoption is the true measure of success

💠Fixing upstream data quality issues through the “leaky faucet” mindset

💠Embedding standar...

As AI agents begin to enter enterprise workflows, organizations must rethink how trust, governance, and context are built into data systems.

In this episode, Edgar Gallo, Chief Data Officer at Daimler Truck North America, joins Susan Wilson of Alation, to discuss how metadata, data catalogs, and organizational culture are enabling AI transformation inside one of the most complex manufacturing environments.

Gallo explains why the future of the CDO role is built on influence, collaboration, and curiosity, and why metadata is emerging as the operational trust layer that allows AI agents to function reliably at scale.<...

In this episode of the CDO Magazine podcast, Philippe Rambach, Chief AI Officer at Schneider Electric, explains why the company avoids treating AI as a simple innovation initiative.

Instead, Schneider Electric approaches AI as enterprise transformation that requires clear governance, operating models, and shared technology platforms.

In conversation with Dr. Julian Schirmer of OAO and HEC Paris, Rambach discusses how Schneider Electric is building AI capabilities that move beyond experimentation and deliver value at scale.

Listeners will learn:

💠Why scaling AI requires more than successful pilots

💠How hub-and-spoke AI teams...

In Part 2 of this series, Mars data leaders Ujjwal Sehgal and Rachel Belino explain how the organization moved from siloed analytics to reusable, cross-domain data. The conversation explores why a common data model became essential, how Mars balances fast delivery with long-term data investment, and how these foundations are preparing the enterprise for agentic AI.

In conversation with Shachin Prabhat of Tiger Analytics.

This episode offers practical guidance for data and AI leaders working to scale adoption, reuse, and enterprise impact.

As enterprises invest aggressively in generative and agentic AI, many leaders face a critical challenge: defining what success actually looks like.

In this episode, Fortune 500 data governance and responsible AI leader Patrick McQuillan joins Peter Geovanes of Juris Tech to discuss how executives should approach AI investments with discipline and long-term thinking.

McQuillan explains why organizations must anchor AI initiatives to customer outcomes, define acceptable risk thresholds early, and build the governance and infrastructure needed for sustainable results.

He also warns against short-term thinking that produces quick wins but erodes long-term value.

As enterprises accelerate AI adoption, governance is becoming one of the most important challenges facing data leaders.

In this episode, Apurva Wadodkar, Senior Director and Head of Data and AI at TI Automotive, joins Merav Yuravlivker, Chief Learning Officer at Data Society, to discuss how organizations can build governance structures that support both oversight and innovation.

Wadodkar outlines a practical approach to AI governance built around four pillars: security, privacy, architecture, and legal review. She also explains why enterprises should maintain a central repository of AI initiatives and how architecture governance can prevent duplicated efforts...