CDAO Washington D.C. | Enterprise Data & AI schedule

During this 5-minute networking session, the aim of the game is to go and meet two people you don't already know. 

• How senior data, analytics and AI leadership roles have evolved since AI became a board-level priority
• What leaders are now expected to own that was outside their remit two years ago
• Where the pressure is highest: delivery, talent, trust, vendor noise or proving value
• What leaders would redesign in their operating models if starting again
• Whether senior leaders are shaping AI strategy or being brought into the conversation too late

• Where decisions really happen and why data still is not there
• Why AI sits outside live workflows
• What leaders changed to influence earlier and closer to the point of action
• What to stop doing if you want decisions to shift
• How data gets embedded into actual business action

Jitin K. Singhal, Senior Director - Portfolio Analytics and Credit Strategy – Sallie Mae

Priyo Chatterjee, Chief Analytics Officer - Excelsior University

Trevor Teason, Head of the Talent Intelligence & Innovation Lab -Capital One

Marcos Oliveira, Technology & Digital Senior Manager –Reckitt

Shaurya Tripathi, Senior Data Scientist - Munich Re

Zoe Hsu, Data Scientist- Lasa Health

Niveditha Mogali, Director, Enterprise Omnichannel Analytics Capabilities -GSK

An interactive session where attendees put their questions directly to an expert on AI ethics, privacy, governance, and regulation. Designed to give practical, real-world answers on operating responsibly under growing scrutiny.

Focus Areas: AI ethics, data privacy, regulatory challenges.

Navin Kumar, Director, Data Engineering and AI Risk – CITI

Harish Vundavalli, Senior Information Technology Architect - Strategic Education

AI is expected to move faster, deliver value sooner and work across the business. The catch? It wants access to your most valuable data.

Join Kaycee Lai, VP of AI at Everpure, to explore how organizations can move beyond experimentation and scale AI across the enterprise—building a modern data foundation that accelerates time to value, supports business agility and keeps production AI secure and adaptable, without exposing sensitive data to model training.

Challenge: 
Melt (dropping out of course registration before term start) rates have been historically high 

Approach: 
Leverage StIR (Student Intervention Recommender), a proprietary AI platform we have developed, to mitigate the impact 

Work closely with Partners to make sure that deployment is seamless and effective 

Results: 
After deployment of Machine Learning Module, our Melt rates have seen historic lows for 6 consecutive terms 

Priyo Chatterjee, Chief Analytics Officer - Excelsior University 

What Still Slows Delivery When the Team and Tools Are Already There

• Where delivery stalls between priorities, dependencies, and decision rights
• Why adding AI into the mix often creates more noise, not more speed
• How leaders decide what capability to build, buy, or drop
• When vendors help and when they create more complexity
• What operating discipline improves time to value

Vaidehi Rao, Director of Enterprise Data Program – Capital One

Marcos Oliveira, Technology & Digital Senior Manager –Reckitt

Abhinav Shankar, Global Head – Reporting and Analytics - Tradeweb

• How senior data, AI and technology leadership roles are changing as AI moves into live operations
• Whether leaders are now expected to steward models, decisions and risk—not just data and infrastructure
• Where responsibility begins and ends across the CIO, CDAO, CAIO, CTO, risk and business leadership
• Whether existing titles and reporting lines still reflect the realities of AI ownership
• What capabilities senior leaders need to remain relevant and effective in AI-led organisations

Elena Alikhachkina, Chief Data and AI Officer - TE Connectivity

• Where data quality breaks across ownership, pipelines, and definitions
• Why no one owns trust end-to-end
• How poor data quietly kills adoption and confidence
• What the business cost really looks like
• What leaders should fix first

Abhinav Shankar, Global Head – Reporting and Analytics - Tradeweb

Chetan Desai, Head of Data Governance and Operations – Guardian Life

• What happens when AI and data decisions go wrong
• Who is accountable when outcomes fail
• How leaders think about risk versus speed
• Where liability sits across business, risk, and data leadership
• How organisations protect the business and themselves

Brandy O'Shields, Sr Manager, Enterprise Data Governance – PayPal

Navin Kumar, Director, Data Engineering and AI Risk – CITI 

 Subha Madhavan, Vice President & Head of AI/ML & Digital Sciences, Global Product Development – Pfizer  

• Hiring for a landscape changing faster than job descriptions can keep up
• Retaining strong people when tools, priorities, and expectations keep shifting
• How leaders are redesigning teams around data, analytics, ML, and GenAI
• Whether AI is reducing headcount pressure or simply changing where talent is needed
• How leaders keep teams motivated when the market narrative is “AI can do this now”

 Trevor Teason, Head of the Talent Intelligence & Innovation Lab -Capital One  

The pilot looked great. Then it met messy data, legacy systems, security reviews, unclear ownership and the people who have to use it.

Join Kaycee Lai, VP of AI at Everpure, and a small group of technology leaders for an honest conversation about what it really takes to move AI into production.We’ll get into where projects tend to stall, what’s working, and how teams are turning early momentum into measurable business value.

• What it means to run data with ownership, accountability, and outcomes
• What “data as a product” looks like in practice
• Internal pricing, chargebacks, and funding models
• How leaders justify long-term investment
• What changes when data owns measurable business outcomes

• How to spot a real business problem where AI can actually help.
• What the first PoC looked like and how feedback shaped the next steps.
• How the solution was built into the existing CDP dataflow, without making life harder for users.
• What mattered in practice around prompts, consent, privacy and audience discovery.

Nataliya Portman, Senior Data Scientist - CBC/Radio-Canada

• • What works across centralised, federated, and hybrid models
  • Where ownership breaks across data, models, and decisions
  • Why duplication and friction keep happening
  • How decision rights are defined, enforced, or avoided
  • What leaders are redesigning right now

Navin Kumar, Director, Data Engineering and AI Risk – CITI

Shaurya Tripathi, Senior Data Scientist - Munich Re

Babu Thomas, Associate Director, Product Management - Verizon

Alejandro Zarate, Chief Data & AI Officer –Santander

Harish Vundavalli, Senior Information Technology Architect - Strategic Education

 

• Where governance slows or blocks delivery in practice
• Where teams bypass controls to get things done
• How leaders balance speed against risk
• What gets enforced versus what only exists on paper
• How organisations operate under growing scrutiny without freezing progress

 Abhinav Shankar, Global Head – Reporting and Analytics - Tradeweb  

• How AI urgency is changing internal power dynamics across data, tech, risk, and the business
• Where good work gets blocked internally even when the case is clear
• Why alignment breaks across business, tech, risk, and operations
• How leaders build influence without formal control
• What resistance looks like at senior level
• How CDAOs move priorities forward in complex organisations

Moderator: Dr. Andrew Omidvar, Vice President Government – Philips

• How CDOs can effectively partner with Boards to secure AI investment and align on value realization, not just experimentation
• Practical governance models that balance innovation with risk oversight, including what Boards expect from data leaders
• The evolving CDO-to-Board pathway. What it takes to influence, educate, and build long-term trust at the executive level
• Lessons from the field. Insights from a study of 150+ CDOs and Board directors on what is working in large, complex organizations

Elena Alikhachkina, Chief Data and AI Officer - TE Connectivity 

Go behind the scenes as Reckitt reveals how it collaborated with a major retail partner to deploy AI-powered insights directly in stores. How AI guided specific shelf-level actions and the lift that followed.

Marcos Oliveira, Technology & Digital Senior Manager -Reckitt

• How the data and AI ecosystem has changed over the last 3–5 years
• Which categories are consolidating, blurring, or becoming harder to separate
• What leaders are still buying, what they are rationalising, and what they regret
• How GenAI changed vendor strategy, budgets, and internal expectations
• What capabilities now matter more than shiny tooling

 Daniel Chen, Senior Data Scientist -The Hartford