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 the CDAO role has shifted since AI became a board-level priority
• What data leaders are now expected to own that they did not own two years ago
• Where the pressure is highest: delivery, talent, trust, vendor noise, or proving value
• What leaders would redesign in their operating model if starting again
• Whether data leaders are shaping AI strategy or being pulled into it 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

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 

• Where progress stalls and why momentum fades
• How leaders decide whether to fix, fund, or stop
• Why weak initiatives survive beyond their value
• How trade-offs are made across competing priorities
• Where investment is being redirected for stronger return

• Who shapes AI policy and decision-making inside the organisation
• Why data leaders are often critical to AI success but absent from AI governance forums
• Whether the CDAO should co-own AI risk, model trust, and implementation standards
• What happens when AI decisions are made without the data leader in the room
• Where the line sits between data stewardship, AI governance, and executive ownership 

• 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 

• How the CDAO remit is changing as AI moves into live operations
• Whether data leaders are now expected to steward models, decisions, and risk, not just data
• Where responsibility begins and ends between CIO, CDAO, CAIO, risk, and the business
• Whether the title still fits the job: CDAO, CDAIO, or something else
• What capabilities data leaders now need to stay relevant in AI-led organizations 

• 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

• 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 

• 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”

• 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

• • Where legacy architecture still blocks progress
  • What leaders are modernising first and what they are deliberately leaving alone
  • Rebuild versus integration trade-offs
  • How technical debt slows scale, adoption, and execution

• • 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 

• 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

• 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 

• 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