CDO Magazine’s Chicago Leadership Summit, held September 17 at the Renaissance Chicago Downtown Hotel, brought together executives across data, analytics, AI, technology, security, and governance to tackle a question many organizations are confronting now: What does it actually take to scale AI across the enterprise?

The answer throughout the day was rarely just more technology.

Across keynotes, panels, focus groups, and conversations between peers, executives kept returning to business value, trusted data, governance, security, organizational readiness, and the changing nature of work. AI experimentation continues, but the expectations surrounding it have changed. Leaders are under increasing pressure to show measurable outcomes while building the foundations necessary to deploy AI responsibly at scale.

Those conversations spanned five tracks: Data & AI Strategy, Value & Business Impact; Data Architecture, Enablement & Foundations; Generative AI: Opportunities, Impact & the Future; Responsible AI, Ethics, Regulation & Governance; and Leadership, Talent & Organizational Transformation.

Executives represented industries including financial services, healthcare, consumer goods, manufacturing, professional services, higher education, and technology, bringing different perspectives to many of the same enterprise challenges.

CDO Magazine thanks the Co-chairs of the Chicago Leadership Summit for their leadership:

• Patrick Chew, Burtch Works CDAIO
• Ivana Donevska, BMO Financial Group Head, Enterprise Data & Analytics Risk
• Anusha Dwivedula, Morningstar Director, Product, Analytics
• Sabrykrishnan Loganathan, Peloton Sr. Director, Enterprise AI Engineering

CDO Magazine Chicago Leadership Summit Co-chairs Patrick Chew, Ivana Donevska, Anusha Dwivedula, and Sabrykrishnan Loganathan were recognized for their leadership and contributions to Chicago’s data and AI community.

The pressure is on to prove AI value

One theme surfaced repeatedly in Chicago: deploying AI is not the same as creating value from it.

As organizations move past proofs of concept, data and AI leaders are being asked tougher questions about where investment is paying off, which use cases deserve to scale, and what business processes need to change along the way.

Kelley Conway, Northern Trust EVP & Chief Data & Analytics Officer, heard that message consistently in the sessions she attended.

“From CDO Magazine’s Chicago Summit, it’s clear AI has moved beyond experimentation, and to scale, we need continuous governance, defined semantics, business process transformation, organizational change management, and tangible benefit,” Conway says.

She also points to what that means for the Chief Data Officer (CDO) and other senior data and AI roles.

“It’s also clear that the role of the CDO/CDAO/CAIO as an orchestrator is pivotal and deserves a seat at the executive table.”

That description of the CDO as an orchestrator reflects how quickly Chief Data Officer responsibilities are expanding. The job increasingly requires leaders to connect data and AI strategy with business priorities, technology, governance, risk, and organizational change rather than operating those functions in isolation.

Suma Nair, JPMorgan Chase Executive Director, Strategy, Data & AI, also puts value at the center of the equation.

“AI transformation is ultimately about value creation, not technology adoption — the organizations making progress are connecting AI investments directly to revenue growth, better decisions, productivity, and measurable business outcomes,” Nair says.

Her takeaway points to a practical approach: start with the business problem, use capabilities already available, pilot quickly, learn from what works, and concentrate investment on opportunities that can demonstrate real value.

Roney Soloman, Ignitho CEO, echoed that emphasis on getting more value from existing capabilities through what he described as “Frugal Innovation.”

“The CDO Magazine Chicago Leadership Summit was an incredibly rewarding experience, bringing together an impressive community of data and AI leaders to exchange ideas and learn from one another. Our discussion on Frugal Innovation reinforced an important message: innovation isn’t always about adding more technology. It’s about maximizing what you already have, using AI thoughtfully, and finding smarter ways to deliver greater business value with less,” Soloman says.

Sid Raina, Medline VP, AI & Data Analytics, heard a similar emphasis in his conversations with other leaders.

“It was a great experience engaging with fellow data, AI, and analytics leaders and exchanging perspectives on where the field is heading,” Raina says. “One key takeaway for me was that everybody highlighted the importance of staying grounded in practical value while continuing to push innovation forward, especially as organizations scale their AI and data capabilities.”

David Dixon, TextQL EVP, Solutions & Services, moderates Dominic Bardele, CIBC Director, Automation & Applied AI; Oliver Ganschar, Kraft Heinz VP, Data, AI & Digital Innovation; Suma Nair, JPMorgan Chase Executive Director, Strategy, Data & AI; and Kader Sakkaria, Marsh Global Head of Data Strategy, during “Beyond the Pilot: Turning Enterprise AI From Activity Into Measurable Value.”

AI is putting new pressure on the data foundation

The more ambitious the AI use case, the harder it becomes to ignore what sits underneath it.

Trusted data, architecture, semantics, security, identity, and governance surfaced throughout the Chicago discussions not as separate data initiatives, but as requirements for scaling AI.

“The foundation matters more than ever — trusted, AI-ready data, strong governance, security, and identity are becoming essential infrastructure as organizations move from experimentation to scaled AI and agentic capabilities,” Nair says.

For CDOs, that creates a familiar challenge with higher stakes. AI can expose weaknesses in data quality, accessibility, ownership, and context that organizations have lived with for years. At enterprise scale, those issues can directly affect whether an AI system produces useful results and whether employees trust those results enough to use them.

Ryan Trimberger, 4MINDS Founder and CEO, sees that connection between trust and adoption becoming increasingly important.

“Enterprise AI is moving into a new phase. The question is no longer whether organizations can deploy AI, but whether they can do it in a way that protects their data, reflects how their business actually operates, and earns the confidence of the people using it,” Trimberger says.

“The conversations at CDO Magazine’s Chicago Leadership Summit made clear that security, governance, and context aren’t obstacles to AI adoption. They’re what make adoption possible.”

Barry Morris, Couchbase Chief Product & Strategy Officer, explores the operational data infrastructure required to move enterprise and agentic AI from promising prototypes to reliable production during the keynote “Navigating the AI Jungle: The Operational Data Plane for Enterprise AI.”

AI Governance is becoming continuous

AI governance was not discussed in Chicago as something that happens after an AI system has been built.

As AI becomes embedded in business processes and organizations explore more autonomous agentic capabilities, executives are confronting questions about accountability, data access, identity, security, business context, and human oversight much earlier in the process.

Conway’s call for “continuous governance” captures an important part of that change.

An AI Governance framework designed for a smaller collection of models and use cases may need to evolve as AI reaches more employees, systems, and decisions. That puts pressure on organizations to think about AI Governance strategy alongside implementation rather than treating governance as a final approval step.

It also expands the number of people involved. AI Governance roles increasingly cross data, technology, security, risk, legal, and business functions, making alignment around ownership and shared outcomes critical.

Nair describes that organizational readiness as essential to scaling responsibly while maintaining the speed of innovation.

Kristin Foster, 84.51° / Kroger Vice President, AI / Data Science; Kelley Conway, Northern Trust EVP & Chief Data & Analytics Officer; and Parul Cheriyan, BP Chief Data Officer, Customers & Products, discuss enterprise AI readiness during the closing Super Keynote, “Building the AI-Ready Enterprise: Data, Governance, Business Capabilities & Leadership.”

The operating model has to change, too

Some of the most consequential AI questions raised in Chicago had little to do with models themselves.

What happens to jobs and responsibilities? What skills will teams need? How should people work alongside AI? Who owns decisions that cross business, data, technology, security, and risk?

“AI is fundamentally changing the way organizations operate and how work gets done,” Nair says. “The Human + AI model will require new skills, evolving roles, AI literacy, and operating models designed for continuous learning and adaptation.”

That shift helps explain why Chief Data Officers and other senior data and AI executives are being pulled into broader enterprise conversations. Scaling AI requires coordination across functions that historically may have operated independently.

Nair describes the next phase of AI leadership as one of “organizational readiness,” requiring business, technology, data, risk, and security leaders to work together around common outcomes.

Conway’s characterization of the CDO, CDAO, and CAIO as an “orchestrator” points in the same direction. Increasingly, the role is not simply to own data or AI. It is to help the enterprise connect the pieces required to turn those capabilities into business results.

Patrick Chew, Burtch Works CDAIO, moderates Michael Baker, NielsenIQ VP & Global Head of Data Science; Dan Cromer, Data Product Management; and Christy O’Gaughan, AbbVie VP & Head, US Commercial Analytics, Insights & Decision Sciences, during a discussion on career durability, AI resilience and the changing AI job market.

Chicago’s data and AI community comes together

Not every important conversation happened on stage.

Breakout sessions and networking gave executives room to compare experiences, talk about what is and is not working inside their organizations, and hear how peers are approaching similar problems.

Anthony Losanno, Managing Director, of CDO Magazine says the depth of those conversations stood out throughout the day.

“What stood out to me at the Chicago Leadership Summit was the depth of the conversations happening on stage, in the breakout rooms, and throughout the networking space,” Losanno says. “Leaders are moving beyond AI experimentation and asking much harder questions about value, governance, trust, and how AI will fundamentally change the way their organizations operate.”

For Losanno, those exchanges are central to the purpose of the CDO Magazine community.

“Bringing this community together to have those candid conversations, share what’s working, and learn from one another is exactly what CDO Magazine is about. Chicago continues to be an incredibly strong community for us and the energy at this year’s Summit reinforced just how important these opportunities for connection have become.”

Members of CDO Magazine’s Top 25 Chicago Data & AI Leaders 2026 who attended the Chicago Leadership Summit were recognized for their leadership and contributions to Chicago’s data and AI community.

If one idea connected the conversations in Chicago, it was that scaling AI forces organizations to work on several problems at once.

Proving value without trusted data is difficult. Moving quickly without the right governance creates risk. Strong technology alone does not guarantee adoption. And none of it scales easily when business, data, technology, security, and risk teams are moving in different directions.

For Chief Data Officers and the growing number of executives responsible for enterprise AI, that makes the leadership challenge much broader than deploying the next generation of technology. It is about creating the conditions for AI to deliver value and earn trust across the enterprise.

Thank You to Our Sponsors

Special thanks to Burtch Works, Couchbase, Cyera, 4MINDS, Google Cloud, Ignitho, Immuta, Redis, TextQL, Tiger Analytics, BBI, Datadog, The Data Lodge, Data Society, i.c.stars, and WisdomAI for making this event a success.

CDO Magazine’s Chicago Leadership Summit was made possible with the support of sponsors and partners helping bring Chicago’s data and AI leadership community together.

Executives speaking at the CDO Magazine Chicago Leadership Summit included:

• Mir Ali, Hershey Head of Data & Analytics
• Allyson Alston, Mars Veterinary Health Global VP, D&A
• Michael Baker, NielsenIQ VP & Global Head, Data Science
• Dominic Bardele, CIBC Director, Automation & Applied AI
• Dhruv Baronia, Northern Trust SVP, Head of WM Analytics
• Arnab Bose, University of Chicago Faculty Director
• Michael Butts, Burtch Works CEO
• Shelby Cannon, OneMain Financial VP Data Protection & Governance
• Parul Cheriyan, BP Chief Data Officer, Customers & Products
• Patrick Chew, Burtch Works CDAIO
• Michael Colella, University of Chicago; USC; AXS Senior Director, Global Data Strategy & Analytics
• Kelley Conway, Northern Trust EVP & Chief Data & Analytics Officer
• Dan Cromer, Data Product Management, formerly Walker & Dunlop, Revantage, Goldman Sachs
• Dennis Demandaco, Gallagher Business Intelligence Specialist Lead
• Karan Dhawal, ZS Associates AI & Technology Leader
• David Dixon, TextQL EVP, Solutions & Services
• Ivana Donevska, BMO Financial Group Head, Enterprise Data & Analytics Risk
• Anusha Dwivedula, Morningstar Director of Product, Analytics
• Don Fleschut, Ryerson VP – Chief Data Officer
• Kristin Foster, 84.51° / Kroger Vice President, AI / Data Science
• Jon Fritz, Redis Chief Product Officer
• Oliver Ganschar, Kraft Heinz VP, Data, AI & Digital Innovation
• Rick Holland, Cyera Data & AI Security Officer
• Narayanan Krishnan, Athletico Vice President – Digital, Data and AI
• Lara Liss, GE HealthCare Chief Privacy & Data Trust Officer
• Sabrykrishnan Loganathan, Peloton Sr. Director Enterprise AI Engineering
• Robert McElherne, Varsity Healthcare Partners Operating Partner, Technology & Data Science
• Shuen Mei, Harbor Capital Advisors VP, Director of Software and Data Engineering
• Gokula Mishra, Former Direct Supply VP, Data Science & AI/ML
• Roger Moore, University of Chicago – Masters of Applied Data Science Associate Clinical Professor
• Barry Morris, Couchbase Chief Product & Strategy Officer
• Suma Nair, JPMorgan Chase Executive Director – Strategy, Data & AI
• Christy O’Gaughan, AbbVie VP & Head, US Commercial Analytics, Insights & Decision Sciences
• Sid Raina, Medline VP, AI & Data Analytics
• Dr. Vijay Rajandram, Northern Trust Asset Management Chief Data Officer
• Dushyant Remivasan, Alight Solutions Director of Engineering, Data Platform
• Bradley Rosenberg, Hightower Advisors Manager of Enterprise Data Governance and Stewardship
• Farhan Sabzaali, University of Chicago Data & Analytics Leader
• Kader Sakkaria, Marsh Global Head of Data Strategy
• Prashanth Seetharaman, HGS CX Associate Vice President & Head – BFSI
• Lori Sherer, BetterFuture-AI Founder
• Scott Simony, Google Cloud Director of Data Analytics, North American Regions & SAISV
• Kamlesh Singh, Tiger Analytics Regional Sales Head
• Roney Soloman, Ignitho CEO & Co-Founder
• Frank Sung, Loop Capital VP of Data Management
• Ryan Trimberger, 4MINDS Founder / CEO
• Shawn Tumanov, Alight Solutions Head of Data & AI Governance
• Anurag Voleti, Xsolis VP of Data Science
• Kalpana Yendluri, Great Lakes Water Authority Director, Emerging Technology