CDO Magazine Podcast Series

Kyle DuPont, Founder and CEO of Data X-Ray, speaks with Maribeth Achterberg, CDO Magazine Editorial Board Member, about how AI is changing the value of unstructured data. The conversation focuses on metadata, file relevance, and how enterprises can help users and agents find the information needed for specific workflows.

In the final episode of this three-part series, Muralidhar Vemulapalli, Chief Enterprise Architect and Acting Chief Technology Officer at Arkansas Blue Cross Blue Shield, tells Chris Gengo, Chief Innovation Officer at HiLabs, why AI approving eighty percent of prior authorization requests does not mean reviewers can step back. Vemulapalli lays out the decision-centric architecture, funding discipline, and governance principles health plans need to move AI from pilot to production. The conversation closes with a forecast on which health plans will still be processing claims by the next decade and which will be preventing the risks behind them.

Rohit Chauhan, former Executive Vice President of Artificial Intelligence & Fraud Solutions at Mastercard, tells Robert Lutton, Vice President of Sandhill Consultants, why a more powerful AI model will not fix fraud detection on its own. Chauhan argues that trust in AI comes from the data architecture underneath it, not from the model's output, and explains how connected context can flip a flagged transaction into a normal one.

In this CDO Magazine interview, Muralidhar Vemulapalli, Chief Enterprise Architect and Acting Chief Technology Officer at Arkansas Blue Cross Blue Shield, tells Chris Gengo, Chief Innovation Officer at HiLabs, why ungoverned AI cannot be deployed at enterprise scale. Vemulapalli lays out the governance model, from AI inventory to human-in-the-loop review, that turns fast models into trustworthy infrastructure. The conversation also covers why provider data is becoming a competitive differentiator instead of a compliance checkbox.

Enterprise leaders frequently fail to justify data investments because they report on technical outputs rather than commercial outcomes. In this video, Mindfuel CEO Nadiem von Heydebrand joins CDO Magazine Editorial Board Member Justin Heller to solve this operating model problem. The discussion provides a clear framework for tracking AI value to prove true return on investment to the board.

Banks continue investing in more sophisticated fraud models, yet many still struggle to keep pace with increasingly connected fraud schemes. In Part 1 of this three-part interview series, Rohit Chauhan, former Executive Vice President of Artificial Intelligence & Fraud Solutions at Mastercard, explains to Robert Lutton, Vice President of Sandhill Consultants, why connected data architecture has become the foundation for trusted Enterprise AI and modern fraud detection.

Muralidhar Vemulapalli, Chief Enterprise Architect and Acting Chief Technology Officer at Arkansas Blue Cross Blue Shield, tells Chris Gengo, Chief Innovation Officer at HiLabs, why most health plans generate insight but never turn it into an operational decision. Vemulapalli argues that AI governance and a unified data fabric matter less than the decision infrastructure and shared accountability that actually move outcomes. This is part one of a three-part CDO Magazine conversation on enterprise AI strategy in healthcare.

In the final episode of this three-part series, Carrier Global Chief Data Officer Arun Nandi joins Flexor Co-Founder and CEO Or Zabludowski to explain how centralized AI governance and decentralized context ownership can work together inside a large enterprise. Nandi also unpacks why large language models have commoditized domain knowledge once locked inside departing employees and legacy vendors, and why senior executives are increasingly building their own AI prototypes instead of waiting on IT teams.

Many AI initiatives deliver outputs, yet struggle to generate sustained business value. In this conversation, Nadiem von Heydebrand explains why organizations must shift from project-based thinking to product-based execution and why identifying the right use cases remains one of the biggest challenges in enterprise AI.Nadiem von Heydebrand, CEO & Co-Founder of Mindfuel, joins Justin Heller, Editorial Board Member at CDO Magazine, to discuss continuous ROI, demand management, and the operational discipline required to prioritize high-value AI opportunities.

As enterprises race to deploy AI, many risk creating technical debt that will be difficult to unwind in the future. Arun Nandi, CDO at Carrier Global, explains why open standards, architectural flexibility, and trusted data products are becoming essential for sustainable AI adoption. Nandi joins Or Zabludowski, Co-founder and CEO of Flexor, to discuss governance, enterprise architecture, and emerging talent models shaping the next generation of AI organizations.

Scaling Next Best Action platforms across diverse therapeutic areas requires solving the critical challenge of field-level user adoption. In this interview, Commercial Strategy, Insights & Analytics Executive Ashutosh Katiyar and Subrata Chatterjee, Global Growth Partner for Life Sciences at Tiger Analytics, reveal how explainable AI drives enterprise trust. Discover how embedding invisible analytics directly into existing commercial workflows eliminates operational friction and accelerates decision-making velocity.

As organizations face a massive wave of agentic AI adoption, managing technical debt and platform sprawl has become a critical priority for enterprise tech leaders. In this episode, Carrier Global Chief Data Officer Arun Nandi and Flexor CEO Or Zabludowski explore how to transition from traditional data structures to open, agent-optimized architectures. The discussion outlines how robust AI governance can serve as an operational accelerator rather than a bureaucratic bottleneck.

Enterprise data teams frequently focus on generating technical output rather than delivering true business outcomes. Mindfuel CEO and Co-Founder Nadiem von Heydebrand shares insights with Justin Heller, a CDO Magazine editorial board member, on how to isolate core operational problems to quantify real business value. This discussion provides a clear framework for executing data strategy based on performance metrics instead of mere capability development.

In this interview, Subrato Chatterjee, Global Growth Partner for Life Sciences at Tiger Analytics, talks with Commercial Strategy, Insights & Analytics Executive Ashutosh Katiyar, about overcoming user adoption hurdles in decentralized data systems. Katiyar explains how embedding natural language querying into existing workflows eliminates the need for tool retraining while maintaining central data governance. The conversation provides a blueprint for transforming data analysts from information gatekeepers into strategic advisor

Organizations often focus on AI ambitions before addressing data readiness, governance, and trust. In this conversation, Molly Just-Behr explains why AI success depends on strong data foundations, transparent governance, workforce development, and cross-agency data sharing.Molly Just-Behr, Global Head of Public Sector at Databricks, joins Dr. Adita Karkera, Chief Data Officer for Government and Public Services at Deloitte, to discuss how public sector organizations can responsibly scale AI while maintaining accountability and mission focus.

Government organizations are under pressure to deliver greater impact with limited resources. In this conversation, Molly Just-Behr explains why unified data and AI foundations are becoming essential for improving mission outcomes, increasing efficiency, and reducing operational risk. Dr. Adita Karkera, Chief Data Officer for Government and Public Services at Deloitte, interviews Molly Just-Behr, Global Head of Public Sector at Databricks, about democratizing data access, modernizing legacy environments, and building momentum for AI adoption across government agencies.

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:Why business sponsorship drives AI prioritizationThe measurement challenge behind scaling AIWhat separates scalable AI initiatives from stalled pilotsHow AI is reshaping teamwork and innovationListen 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:The priorities dominating CDO agendas todayWhy AI-ready data foundations matter more than everHow AI agents are accelerating data operationsWhere human intervention still plays a critical roleWhy security and reliability continue to make or break enterprise AI initiativesA 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:Simplifying complex data estatesGovernance and privacy by designWhy AI exposes poor data qualityReducing manual processes and Excel dependencyThe evolving mandate of the CDOWhy business ownership is critical for adoptionIn 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 transformationListen 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 measures business value from AI investmentsFirst part of a three-part series.

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 interoperability and data lineage in scaling healthcare AI

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:Why trust determines AI adoptionThe evolution from data literacy to AI literacyGovernance beyond policies and councilsTransparency during workforce transformationWhy critical thinking still matters in the AI eraListen 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” dataPart 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 likeAn insightful discussion for data and AI leaders focused on long-term execution and trust.

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 Health Plan approaches trustworthy and audit-ready AIIn conversation with Robert Lutton of Sandhill Consultants.Listen now.

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 scaleIn 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 “walled garden” AI ecosystems break down• Why enterprises may already be closer to agentic AI than they think• Why rebuilding entire AI stacks can become expensive and riskyA timely discussion on where enterprise AI is actually headed and what leaders may be underestimating.Listen now.

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 practiceListen 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🔹What strong data foundations mean for AI success🔹The leadership mindset required to navigate constant changeA practical conversation for data and AI leaders trying to turn ambition into execution.Listen now.

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 scaleIn conversation with Adita Karkera, Chief Data Officer for Deloitte’s Government and Public Services.From vision and mission to measurable goals and execution discipline, this is a practical look at how transformation actually happens inside complex organizations.Listen now.

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 oncologyIn conversation with Erik Pupo of Guidehouse.

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 build versus buy decisions, to enforcing architecture discipline in a fast-moving environment.If you are navigating the shift from experimentation to scale, this episode offers a clear view of what works, what fails, and what to do differently.

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 modelsThis is the final part of a four-part series with Karan Jain, Founder and CEO of NayaOne, exploring how enterprises can operationalize AI in regulated environments.Part 1 examined why AI initiatives stall after promising pilotsPart 2 explored where AI is delivering measurable impactPart 3 focused on evaluating AI solutions without losing control

How to Scale Oncology AI Safely: AI Governance Lessons from Hope LLMAs 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🔹Using AI to improve trial feasibility and patient access🔹Accelerating research through real-world evidenceThe conversation also explores a critical shift in enterprise AI:From driving adoption → to implementing governance.With rising demand from clinicians, AI governance strategy and implementation have become essential to ensure safe, scalable deployment. This episode offers a grounded, real-world perspective on what it takes to operationalize AI in complex, regulated environments.🎧 Listen now📖 Read the full article:

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 toward evidence-based AI decisionsIf your organization is struggling to move from pilots to production, this conversation offers a clear, practical framework for scaling AI responsibly.

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 scaleA practical look at what it takes to move from data platforms to data products.In conversation with Julian Schirmer of OAO.

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 of fragmented data architectures in limiting AI at scaleIn conversation with Karan Jain of NayaOne.This episode is Part 2 of a 4-part series on operationalizing AI in banking.

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.The conversation also explores how organizations should think about success versus failure when outcomes can range from incremental improvements to life-or-death impact.A grounded discussion on what it actually takes to make AI work in cancer care.

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.This episode offers a practical perspective on what separates AI experiments from real, sustained impact.

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 insightsIn conversation with Julian Schirmer of OAO.This episode offers a grounded view of enterprise AI, focusing on execution, not just ambition.

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 initiatives break down🔹How regulatory uncertainty is influencing enterprise decision-making🔹Why multi-vendor evaluation is becoming the new normThis is Part 1 of a 4-part series on operationalizing AI in the enterprise.

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 not waste time.”A grounded conversation on what it takes to move AI from experimentation to real business impact.Featuring Rambach in conversation with Dr. Julian Schirmer, Co-Founder at OAO.

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 at the same outcomes.This episode offers a grounded perspective on what it takes to move from AI experimentation to real operational value.

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 governance, protects sensitive workforce data, and builds the data foundations needed for AI-ready analytics environments.The conversation offers practical lessons for data and analytics leaders working to turn analytics investments into real business adoption.