AI agents are permeating applications and systems across organizations. Their flexibility is valuable in unpredictable environments where real-time monitoring and control aren’t practical. However, their adaptive strength comes with nondeterministic weaknesses. Increased autonomy and complexities in multiagent systems, compounded by GenAI models, dangerously raise uncertainty, prompting the question: are multiagent systems worth it?

Unlocking the full value of AI requires more than just technology. It demands a fundamental shift in how organizations are structured and how teams collaborate. This session equips D&A leaders and CDAOs with proven strategies to navigate the complexities.

Implement your target AI governance operating model by mapping governance pillars to key AI components and differentiating AI capabilities. This model should connect with other governance bodies and extend existing governance models to AI-specific considerations of trust, transparency and diversity.

Discover the essential steps for developing a successful AI strategy. This session will provide guidance on aligning AI initiatives with business goals to drive innovation and gain a competitive advantage.

As the CDAO role continues to evolve in the AI era, organizations face critical decisions about the direction and impact of their data leadership. This session explores the future of the CDAO through three distinct archetypes: Expert, Collaborator and Pioneer. Attendees will learn how to assess and choose the archetype that best aligns with their organization’s AI ambition and strategic goals. Attendees will leave with actionable insights to define their CDAO journey and position themselves and their organizations for success in a rapidly changing digital landscape.

Overlooked risks and unintended consequences of GenAI can disrupt operations, ethics, security and value realization. CDAOs should understand the second- and third-order effects of GenAI adoption and proactively address hidden challenges to safeguard their organizations’ competitiveness and resilience.

Prioritizing data and AI literacy programs directly impacts the core measures of a firm’s financial performance. Investing in data and AI literacy is the fastest way for D&A leaders to achieve payback in their D&A maturity journey, especially in early stages of maturity.

AI is impacting everything, though often more slowly than expected. Its adoption is shaped by company politics, which leaders cannot avoid. This session offers practical insights and candid answers on navigating AI’s realities in a political environment.

Trust in data, strong risk management, robust trust models, and agentic AI are reshaping how enterprises handle data, analytics, compliance, and business value. This session explores the impact of trust on data governance and agentic AI.

Organizations struggle to scale AI governance, mitigate risk, and effect compliance while delivering value in AI deployments. AI governance programs only check the box if they are not adaptive and embedded into the fabric of the AI lifecycle. In this session, you will learn how to define your role in AI governance, assess what technology capabilities are required to govern AI at scale, and make an investment decision in an AI Governance Platform or other technology tool.

AI agents have become one of the most hyped, and most abused, terms in the technology market. Like any good counterintelligence officer, you need to be able to spot agents and assess their impact, risk and value. This session cuts through the hype to the insights you need and a framework for assessing AI agent use cases, tools and risk to help AI agents deliver value for your organization.

GenAI solutions include several choices and trade-offs. A critical decision is: should you build custom AI solutions in-house or buy off-the-shelf products? This session brings together a debate on the trade-offs, risk and rewards of each approach. The session will be based on scenarios and use-cases to highlight key considerations such as cost, reliability , flexibility and speed for different decisions such as LLMs vs. SLMs, RAG vs. AI agents, packaged platform capability vs. bespoke custom solution, packaged vs. open-source.

This session details how Agentic AI will impact existing data management architecture and technologies, which new use cases it enables in data management and engineering, and which skills will be needed or become obsolete. We’ll also cover how to prepare budgets, teams, and operating models for these changes. These are now valid, frequently debated questions as Agentic AI evolves.

Most metadata in 2025 will remain passive in approaches with stats, reports, schema and business-developed glossary terms. Yet, organizations must grow their maturity in metadata management. We start with traditional metadata techniques — passive. With AI undergoing confidence issues and the demand to reduce risk, grow AI confidence and provide data assurance, active metadata becomes key.

AI agent adoption transforms data and analytics strategy and operations. In this session, we will examine the top trends across three key themes: agentic D&A, semantics at the core and D&A p latformization.

Productivity of knowledge workers is not a path to financial value. Process recalibration is a prerequisite to value creation. True, sustainable value is trapped behind legacy processes, outdated team structures and the business driven transition approach. This session provides insights to understanding practical approaches of unlock the ROI and how to lead teams through the transition.

42% of organizations state they want to be more data-driven. But to what end? The next megatrend for D&A leaders to prepare for is “autonomous business,” where operations run unattended, management is heavily augmented with analytical technology, AI agents are customers, products and services are data-driven, all within a programmable economy. Attend this visionary session and walk away with a better understanding of your future.

Urgent investments in AI-ready data and operational use cases have put the spotlight on foundational data management. The Data Fabric has emerged as a long-term data management architecture that you should now pursue for sustained data, analytics, and AI success. This session will help participants understand what data fabrics are and their implications for your data architecture. It will also address how to build and where to buy data fabrics.

This session will explore how generative AI technology will evolve over the next decade, moving from large, general-purpose models to agentic, multimodal and deeply personalized systems. IT leaders will gain foresight into the architectural, ethical, and strategic shifts that will define the next wave of enterprise AI.

AI agents can upend traditional AI use cases and unlock transformative potential, but the real danger isn’t overhyping their power — it’s overestimating your organization’s readiness. D&A leaders must see past the hype, sidestep common pitfalls, and build the foundation needed for true AI success.