Executive Summit

As data leaders navigate the agentic era, the focus shifts from reactive analytics to orchestrating proactive, high-ROI agentic workflows that bridge infrastructure and business value. This framework invites discussion on integrating operational intelligence, proactive security, and board-level governance to accelerate enterprise AI transformation. 

In this keynote, we'll explore how to:

• Automate high-impact, low-risk workflows to realize agentic value
• Integrate security and trust with centralized, transparent oversight
• Align agentic initiatives with strategy via structured governance and monitoring

Delivering value with AI at pace and scale requires addressing the tension between speed to market and risk management, especially in regulated industries. This session explores how to implement enterprise class risk controls while moving at the speed of an AI native.

This session will focus on:

• Delivering AI solutions with both speed and rigor through agile, multi-disciplinary governance
• The growing importance of agentic solutions and FinOps to deliver ROI at scale
• Critical success factors in the implementation of a comprehensive and real time agentic control plane

Organizations have made significant investments in data and AI, but many continue to struggle with adoption and value realization. The challenge is no longer access to technology. It is ensuring employees have the confidence and skills to incorporate data and AI into everyday decisions. This session explores how organizations are using data literacy programs to accelerate adoption and drive measurable business outcomes.

Join this session to learn:

• How to build workforce capabilities that accelerate AI adoption
• Strategies for embedding data-driven decision making into daily operations
• Methods for demonstrating the business value of data literacy investments

As AI moves into production, the token bill is only the most visible cost. Most spend is decided further down: which model handles a request, how much data an agent pulls to answer it, and how many times the same question gets asked across the enterprise. Leading CDAOs are treating AI cost as a governance problem, putting a gateway layer in place that meters usage, routes requests to the right model, and controls what agents can access and do inside core systems. How do organizations scale AI without losing control of spend or the data behind it?

 In this boardroom, we'll discuss:

• Strategies for monitoring token consumption across models, agents, and the enterprise systems they reach 
• Tactics such as model routing, caching, prompt optimization, and governing the requests and actions agents run against enterprise data, where much of the waste     originates
•  Approaches for tying AI spend to business value and keeping cost controls, policies, and context intact when the model underneath change

AI has moved from experimentation to an executive priority. Enterprise adoption now depends on trust, consistency and sustainable economics. Business and technology leaders will discuss how strong business context and governance can help AI deliver reliable value at scale.

Join this session to discuss:

• Building trust in AI decisions and outputs
• Establishing consistent business context and governance
• Scaling AI while managing cost and value

Connect with like-minded peers in a one-on-one setting through Peer-to-Peer Meetings. You will be matched with peers in your community based on your shared interests and priorities.

Organizations are investing heavily in AI, yet many struggle to prove measurable business impact. Technical metrics and adoption statistics rarely satisfy executives looking for tangible outcomes. Leading CDAOs are addressing this challenge by treating value realization as an operational discipline, not a reporting exercise. Learn how organizations are defining business outcomes, establishing accountability, and creating repeatable frameworks that connect AI investments to enterprise performance.

This session will explore:

• Define outcome-based metrics before AI initiatives begin 
• Embed accountability and value tracking throughout the lifecycle
• Demonstrate business impact beyond adoption and technical measures

As organizations focus on Generative AI, predictive analytics has remained complex, resource-intensive, and difficult to scale. The right strategy can change this dynamic, enabling organizations to move beyond bespoke models and apply predictive intelligence more broadly across the enterprise, resulting in faster decision-making, greater accessibility, and a more scalable approach to delivering AI-driven business outcomes.

Join this session to discuss:

• Why predictive analytics has lagged behind Generative AI, and what is driving its next wave of adoption
• How organizations can shift from bespoke model development to scalable predictive decision-making
• What leaders should consider when embedding predictive intelligence into enterprise-wide AI strategies

The biggest challenge facing many CDAOs is not technology, it's translation. As AI and data investments accelerate, leaders must clearly demonstrate data and analytics results to senior leadership and stakeholders. Yet differences in language, incentives and perspectives often create barriers to alignment with CEOs and CFOs. Organizations that bridge this gap are better positioned to build trust, influence decisions and maximize the value of their data and AI investments.

This Gartner led discussion explores:

• Clearly connecting data and analytics results with business objectives 
• Communication strategies to strengthen trust and influence
• Effectively communicating the impact of data and analytics investments to the enterprise

As AI adoption accelerates, CDAOs face a growing challenge: scaling AI across the enterprise as demand outpaces organizational capacity. Success increasingly depends not on individual use cases, but on the operating models that enable AI to scale effectively. Leading organizations are standardizing capabilities, prioritizing investments, and balancing centralized oversight with business-led innovation to establish AI as a sustainable, enterprise-wide capability.

Join this boardroom discussion to explore:

• Designing operating models that enable AI adoption at enterprise scale
• Balancing centralized oversight with business-led innovation
• Prioritizing AI investments to scale efficiently and sustainably

Finish the day sharing lessons learned with your peers over light fare and drinks at this closing reception hosted by your governing body members.