CDO Magazine Podcast Series

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.

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 standards and governance into data architecture💠Preparing for emerging manufacturing challenges such as tariffs and supply chain volatilityRead Part 1 here.Read Part 2 here.

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.This episode is Part 1 of a three-part series exploring AI transformation in manufacturing.

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 combine expertise and domain knowledge💠Why enterprise AI platforms help prevent technology fragmentation💠How reusable AI modules accelerate value across use casesListen to the full discussion.

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.Key discussion points:🔹Why AI initiatives should start with customer value🔹Defining measurable outcomes before deployment🔹The risks of over-focusing on POCs🔹Why emerging technologies like agentic AI lack consistent standards🔹How infrastructure and talent planning future-proof AI programsListen to Part 1 here.Listen to Part 2 here.

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 across teams.The conversation also introduces Wadodkar’s Decision Intelligence Framework, designed to help data organizations focus on improving business decisions rather than simply delivering analytics outputs.This episode is Part 2 of a three-part series exploring how enterprises are building scalable AI capabilities.

Healthcare is entering a new phase where AI is becoming foundational infrastructure rather than isolated pilots.In this episode, Parminder Bhatia, Chief AI Officer at GE HealthCare, joins Cindi Howson of ThoughtSpot to discuss how leaders should think about AI architecture, the rise of hybrid edge and cloud systems, and the real-world impact AI is already delivering in hospitals.They also explore workforce transformation, AI literacy, and what leaders must prepare for as AI becomes embedded into everyday healthcare operations.

What does it take to move AI from experimentation to enterprise impact?In this episode, Philippe Rambach, Chief AI Officer at Schneider Electric, explains how the company is embedding AI across its global energy technology portfolio. The discussion covers energy optimization, grid balancing, employee productivity, and preserving institutional knowledge.In conversation with Dr. Julian Schirmer, Co-Founder at OAO.This is Part 1 of a three-part series exploring how Schneider Electric is scaling AI to deliver measurable value.

Organizations have more data than ever, yet leaders still struggle to act with confidence.In this episode, Mars leaders Ujjwal Sehgal and Rachel Belino join Shachin Prabhat of Tiger Analytics to explore why the gap between data and decision-making persists.They discuss:🔷The hidden causes of the data-to-insight gap🔷Early lessons from scaling people analytics at Mars🔷Why reusable data is becoming a strategic priority🔷The importance of discoverability, governance, and lineageListen Now.

Organizations have more data than ever, yet leaders still struggle to act with confidence.In this episode, Mars leaders Ujjwal Sehgal and Rachel Belino join Shachin Prabhat of Tiger Analytics to explore why the gap between data and decision-making persists.They discuss:🔷The hidden causes of the data-to-insight gap🔷Early lessons from scaling people analytics at Mars🔷Why reusable data is becoming a strategic priority🔷The importance of discoverability, governance, and lineageListen Now.

In Part 1 of this three-part series, Apurva Wadodkar, Senior Director and Head of Data and AI at TI Automotive, shares a practical starter kit for building an AI practice from the ground up.She discusses the Five-E framework, the importance of education and prioritization, and why a unified chatbot strategy can accelerate productivity and adoption across the enterprise.In conversation with Merav Yuravlivker, Chief Learning Officer at Data Society.Listen Now.

AI is pushing organizations to move faster. But many are still missing the foundations.In this episode, a Fortune 500 data governance and responsible AI leader explains why the current AI moment is less about new models and more about disciplined data strategy. The conversation explores governance maturity, executive pressure to become AI-enabled, and why monitoring and metadata are becoming essential infrastructure.In conversation with Peter Geovanes.Listen Now.

Healthcare is moving beyond AI experimentation and into operational scale.In this episode, Parminder Bhatia, Chief AI Officer at GE HealthCare, joins Cindi Howson of ThoughtSpot, to discuss how agentic AI is transforming hospital workflows, radiology backlogs, maternal care coordination, and clinical decision-making.With more than 115 FDA-authorized AI-enabled devices, GE HealthCare offers a real-world perspective on:🔹Orchestrating complex healthcare systems with AI🔹Implementing layered guardrails and human oversight🔹Balancing innovation with regulatory responsibility🔹Building patient trust through measurable outcomesThe future of healthcare AI isn’t unchecked automation. It’s progressive autonomy with accountability.Listen now.

In the final episode of this three-part series, Justin Marsico, Assistant Commissioner for Fraud Prevention and Financial Integrity at the U.S. Department of the Treasury’s Bureau of the Fiscal Service, shares how the Chief Data Officer role has transformed from a compliance-centered function into a mission-critical operational leader.He discusses the creation of the Fiscal Data Hub, the push toward enterprise-wide automated data cataloging, and why simplifying governance requires tight coordination between the CIO, CDO, and CFO.For federal agencies and large enterprises alike, this episode offers a blueprint for aligning governance with fraud prevention at scale.In conversation with Carly Mitchell of Guidehouse.

AI models are evolving rapidly but enterprise advantage is built elsewhere.In this episode of the CDO Magazine Podcast, Fortune 100 enterprise CTO and transformation leader Jyoti Chawla joins Karen Odegaard of Guidehouse to unpack why data foundations, governance, and operating models — not AI models — determine whether organizations can scale AI responsibly and effectively.Drawing on decades of leading large-scale modernization efforts across IBM, Deutsche Bank, and Cisco, Chawla discusses managing AI risk, preparing for agentic AI, and building operating models that align innovation with measurable business outcomes.A must-listen for CDOs, CTOs, and AI leaders navigating AI at scale.

What does it take to lead enterprise data transformation in an AI-driven world?In this episode, Jo O’Hazo, Chief Data Officer at Advantage Solutions, reflects on her journey as a data leader and what it means to be an enterprise data guru. She discusses the importance of strong data foundations, earning trust across the business, and why AI adoption must feel natural to scale effectively.This conversation with Informatica’s Nathan Turajski closes a three-part series exploring people-led data transformation, measurable impact, and the leadership mindset required to sustain change.

Johnson & Johnson (J&J) Global Services sits at the center of how J&J scales enterprise operations, enabling employees and end-to-end processes through a service model designed to create value, improve experiences, and deliver measurable impact. As enterprise AI moves from experimentation to execution, J&J Global Services plays a critical role in translating emerging technologies into trusted, scalable capabilities for a highly regulated, global workforce.

Preventing fraud at scale is not a policy exercise. It’s a data and systems challenge.In Part 2 of this three-part series, Justin Marsico, Assistant Commissioner for Fraud Prevention and Financial Integrity at the U.S. Department of the Treasury, joins Carly Mitchell of Guidehouse to discuss how Treasury uses data and analytics to verify over a billion payments each year.Marsico shares how payment verification works in practice, how analytics are introduced into live payment systems with zero tolerance for error, and what agencies and states should consider as they strengthen fraud and improper payment controls.Listen to Part 1 of the series.Read the full interview on CDO Magazine.

As enterprises move from generative AI (GenAI) experimentation toward scalable, production-ready capabilities, leaders are being forced to rethink how AI fits into operating models, how it is adopted by employees, and how trust is established across the organization. The shift is no longer about proving technical feasibility. It is about aligning data quality, governance, and everyday usage to support sustainable enterprise-wide impact.Johnson & Johnson’s (J&J) Global Services organization plays a central role in operational transformation by delivering scalable capabilities across data, digital solutions, and business services to various stakeholders.In Part 1 of this three-part series, Ajay Anand, SVP of Strategic Solutions and Commercial Services, Global Services at J&J, frames GenAI as a stakeholder-first transformation discipline that only scales when it is grounded in high-quality data, governed content, deep domain understanding, and consistent adoption across the enterprise.In this second installment, Anand continues the conversation with EY’s Kevin Barboza on how Global Services balances near-term GenAI delivery with longer-term agentic AI strategy, how enterprise adoption is driven through structured change management, and why AI governance, accessibility, and disciplined data management form the foundation of trust as organizations prepare to scale AI responsibly into 2026.

What does success look like in data and AI governance when KPIs alone are not enough?In Part 2 of this three-part series, Jo O’Hazo, Chief Data Officer at Advantage Solutions, joins Nathan Turajski of Informatica to discuss how data leaders measure progress in ways that actually change behavior.The conversation covers:♦️Moving from siloed KPIs to integrated business metrics♦️Why stakeholder trust is a leading indicator of data maturity♦️How compliance and ethics support innovation instead of slowing it down♦️The real risk of prioritizing speed over accuracyO’Hazo explains why governance embedded into the core enables reusable trust, faster delivery, and scalable AI outcomes.Listen to Part 1 here.

In this episode, Nitin Kumar, Director of Data Science and GenAI at Marriott International, joins Ben Blanquera, VP of AI and Sustainability at Rackspace Technology, to discuss how Marriott applies data science and generative AI to personalize guest experiences and drive measurable business outcomes.This conversation explores:🔶The shift from data stewardship to enterprise value creation🔶How AI is changing loyalty strategies in hospitality🔶Why modern data leaders must translate AI into business languageThis episode is Part 1 of a four-part series on enterprise data leadership and GenAI.🎧 Listen now.

The federal government makes trillions of dollars in payments each year, making payment integrity one of its most complex and critical challenges.In this first episode of a three-part series, Justin Marsico, Assistant Commissioner for Fraud Prevention and Financial Integrity at the U.S. Department of the Treasury’s Bureau of the Fiscal Service, joins Carly Mitchell, Partner at Guidehouse, to discuss how Treasury is strengthening the Do Not Pay Program.They explore why fraud prevention has become a top administrative priority, how OMB memo M-25-32 is removing long-standing barriers to adoption, and what agencies must do now to move from limited usage to full participation — while remaining compliant with privacy and legal requirements.This episode provides a clear view into how policy, data, and governance intersect to protect public funds at scale.🎧 Listen to Part 1 of the series now

Ajay Anand, SVP of Strategic Solutions and Commercial Services, Global Services at Johnson & Johnson, shares how the Global Services uses data quality, content governance, and domain expertise to scale GenAI responsibly. He explains how the chatbot JAIDA, Johnson & Johnson Artificial Intelligence Digital Assistant, supports more than 138,000 employees.Anand breaks down:🔹Why AI must be treated as a stakeholder-first discipline🔹Why JAIDA is the launchpad for GenAI at enterprise scale🔹How Global Services operationalizes AI for employees🔹Why data and content quality define whether GenAI delivers actual value🔹How to integrate GenAI without adding friction or compromising experienceListen Now.

Jo O’Hazo, Chief Data Officer at Advantage Solutions, joins Nathan Turajski of Informatica for a deep dive into the foundations of enterprise-scale data and AI.O’Hazo explains why her work begins with people rather than platforms, how micro successes compound into large transformation milestones, and why trust must evolve through listening, alignment and repeatable outcomes.She also discusses how AI is revealing the gaps many organizations have ignored, and why strong governance, accuracy and architectural consistency matter more than ever.Listen Now.

In this concluding episode of a three-part series, Andrew Foster, Chief Data Officer at M&T Bank, joins Parker Thompson of Denodo to discuss what it really takes to lead data initiatives in a large, regulated enterprise.Foster shares candid lessons from M&T’s data journey, including how to adapt when progress does not move as planned, why CDOs must rely on influence rather than authority, and how the bank’s Data Academy is helping employees build data skills while reframing governance as a value-add.A practical conversation for data leaders navigating execution, talent development, and organizational change.

Most AI challenges are not technology problems. They are foundation problems.In the final episode of a three-part conversation, Chris Goodale, VP of Enterprise Data and Analytics Enablement at Sun Life, sits down with Jason Sturgess of Denodo to unpack how semantics, governance, and value discipline shape successful AI at enterprise scale.The discussion explores why semantics act as a “Rosetta Stone” across business, technology, and AI, how Sun Life evaluates and prioritizes AI investments, and what it takes to consistently move from pilot to production in a highly regulated environment.This episode is a practical guide for leaders focused on scaling AI responsibly while delivering real business impact.Listen now and read the full conversation.

Andrew Foster, Chief Data Officer at M&T Bank, continues his conversation with Parker Thompson, Regional VP and General Manager, East at Denodo.Moving beyond strategy, this discussion dives into execution. Foster shares how M&T Bank operationalizes data and AI across a 23,000-employee organization through federated stewardship, disciplined governance, and realistic planning horizons.Hear insights on:💠Building credibility through delivery, not promises💠Why well-governed data is foundational to AI readiness💠How to pace organizational change without disruption💠Measuring value and sustaining momentum beyond initial winsA must-listen for CDOs and senior leaders focused on turning data and AI ambition into measurable results.Listen to part 1 here: [Link to first article]

Chris Goodale, VP of Enterprise Data and Analytics Enablement at Sun Life, speaks with Denodo’s Jason Sturgess about how the insurer is generating real, measurable GenAI impact.Key discussion points:✅The internal AI assistants powering more than 3 million enterprise queries✅Advisor-focused AI tools that reduce paperwork and enhance client conversations✅The Plan Sponsor Dashboard as a model for high-value data products✅How governance, OSFI E-21 readiness, and strong data hygiene support GenAI✅Why Sun Life’s hybrid architecture enables both scale and local agilityA must-listen for data, analytics, and AI leaders navigating enterprise transformation.