Chief AI Officer Summit New York schedule

• Shaping enterprise strategy through AI-driven vision and executive leadership.
• Turning innovation into measurable business outcomes and competitive advantage.
• Championing ethical, trusted, and scalable AI across the organization.
• Inspiring a culture of collaboration, agility, and continuous learning in the AI era.

• 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:

Rahul Kashyap, Medical Director Research, WELLSPAN HEALTH

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

Sara Green, Vice President, Content Operations & AI Innovation, WEBMD IGNITE

• Why many AI initiatives stall after early success, and the practical barriers that prevent enterprise-wide adoption.
• How leading organizations engineer AI into everyday workflows, driving consistent usage rather than one-off wins.
• What AI leaders must put in place to sustain adoption at scale, across technology, processes, and teams.

• 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 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.

• What a realistic enterprise AI architecture looks like today — foundational components vs hype.
• Crucial architectural decisions that determine long-term scalability (data layer, orchestration, governance touchpoints).
• How to align data, engineering, and business teams without slowing delivery.
• Lessons from scaling AI across complex organizations and legacy environments — what to standardize vs. where to stay flexible?

• 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

Aggie Prakash, AI Integration Specialist, MCKINNEY

Ferhat Yilmaz, Director - Enterprise and Pipeline Insights & Analytics, NOVARTIS