Chief AI Officer Summit New York schedule

• How do organizations move from AI principles to practical, enforceable governance, without slowing innovation?
• What “trust” looks like in practice, including the metrics and signals leaders rely on to ensure transparency and accountability.
• How are emerging regulations reshaping enterprise AI strategy, and what leaders must prepare for now?
• The evolving role of AI leadership in owning accountability, supported by automation and continuous monitoring.

Panellists:

Garvita Thakral Kapur, Senior Director Software Engineering and Artificial Intelligence, THE NEW YORK PUBLIC LIBRARY

Harish Vundavalli, Senior Information Technology Architect, STRATEGIC EDUCATION, INC.

Craig D'Rozario, Senior Director, CURE AUTO INSURANCE (Citizens United Reciprocal Exchange)

Rakesh Singh, Global Customer Technology Director, LSEG (London Stock Exchange Group)

Moderator: Lev Yatsemyrskyi, Quantitative Technology Director, QUBE RESEARCH & TECHNOLOGIES/ NASDAQ (former)

• Aligning AI initiatives with business objectives to deliver measurable value.
• Establishing strong data foundations and governance for scalable innovation.
• Embedding ethical, transparent, and responsible AI practices across the enterprise.
• Fostering a culture of experimentation, agility, and continuous improvement.

 

• How can organizations move beyond short-term personalization to build AI systems that drive sustained engagement and long-term business outcomes?
• What does it take to shift from static customer profiles to dynamic, state-aware intelligence that understands where users are in their journey?
• How can companies successfully embed AI across marketing and product functions while creating the governance, ownership, and operational structures needed to scale?
• Where does traditional experimentation fall short, and how can organizations balance rapid learning with the stability and trust users expect?

Organizations invest heavily in AI, but without skilled people, those investments stall. DataCamp's two-track model drives real transformation: broad AI literacy for all and deep technical training for builders, backed by insights from 18M learners across 6,000+ organizations.

Right now many AI initiatives are still stuck in various pre-production stages. Without explainability, trust, and context, AI projects get stalled. Discover what leading organizations are doing to successfully overcome barriers to adoption, deploying and scaling agents in production enterprise environments.  

Agentic AI is transforming enterprise AI from answering questions to executing operational workflows. As a result, real-time, governed business context is becoming the new bottleneck for enterprise AI. In this session, K2view will show why traditional data architectures fall short, and how Business Operational Context becomes the data foundation for AI execution, validation, and continuous improvement. Attendees will learn a practical architecture pattern for decoupling AI agents from data handling, reducing token cost and governance risk, and enabling enterprise-scale agentic AI.

• How agentic AI is moving beyond rule-based systems to make dynamic, context-aware decisions — transforming how organizations trade, manage risk, and operate.
• Why the future isn’t just about smarter models, but networks of AI agents working together to forecast, adapt, and respond in real time.
• How quantum breakthroughs are unlocking unprecedented speed and complexity in areas like optimization, portfolio management, and compliance.
• What leaders must do today to leverage agentic intelligence and quantum technologies to stay ahead in an increasingly autonomous and unpredictable landscape.

• Popular AI in regulated industries can't be audited, reproduced, or corrected — and frontier reasoning models are making it more expensive and energy-intensive, not less.
• ExCITE (Explainability, Computability, Interpretability, Traceability, Editability) is a five-principle framework for any AI deployed where trust and compliance matter.
• KATO is a deterministic, CPU-based prediction engine that satisfies all five — traceable to source data, editable via SQL, and FAA-certified in its prior life as GAIuS.

• How AI is Transitioning from Copilot Assistant to Pilot Navigator
• Beyond Agentic AI: What Does Physical AI Look Like, and When?

• Assigning a business owner and P&L targets to every AI initiative with clear KPIs and SLAs.
• Stack-ranking bets by ROI and time-to-value, time-boxing pilots, and doubling down on compounding wins.
• Productizing models into workflows/agents and driving adoption with enablement, incentives, and change management.
• Instrumenting value end-to-end: establishing baselines, unit economics, TCO, and risk-adjusted ROI with continuous optimization.

• How operating models and ownership structures are evolving as AI moves into core business workflows.
• Who is accountable for AI — and how responsibilities shift across data, technology, and business teams.
• The talent, skills, and cultural changes required to move from experimentation to sustained adoption.
• Embedding incentives, governance, and execution discipline to ensure AI delivers consistent business impact. 

Panellists:  
Nishit Dhilen Mehta, Vice President, Analytics Solutions Manager, JPMORGAN CHASE 
Bin Yu, Ph.D., VP of AI & Data, STARBUCKS 
Ferhat Yilmaz, Director - Enterprise and Pipeline Insights & Analytics, NOVARTIS 

Moderator: John Shaw, Chief Executive Officer, PRAXIS IQ