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 True, sustainable value is trapped behind legacy processes, outdated team structures and the business driven transition approach.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.

Data governance has traditionally encompassed analytics governance, managing most risks and value in traditional analytics. However, AI introduces new risks and considerations that D&A governance may not be equipped for. Should D&A governance evolve to govern AI or is it time for a separate discipline with a fresh mandate? This session explores conflicting accountabilities, leadership and operating models between these disciplines.

Data quality and data observability tools provide significant capabilities to ensure good data for your BI and AI. Data observability tools give organizations integrated visibility into the health of their data, data pipelines and data landscape. Data quality tools enable business users to manage data at its sources by setting rules and policies. Together, these tools help organizations build a strong foundation in data management for BI and AI initiatives.

Leaders responsible for AI must evaluate the potential benefits and costs of new agentic AI use cases. This insight offers a framework, model and specific numbers for building and simulating total cost and key value drivers at scale for AI agent use cases.

National AI sovereignty is a nation-state’s desire to control its own AI, reducing reliance on foreign innovation, talent, and vendors. Ambition and maturity vary globally, creating commercial opportunities and risks. This session explores various nation state approaches of sovereign AI and the public policy initiatives, including global AI regulations, that support them. Attendees will leave with a blueprint to identify value creation strategies while confronting geopolitical uncertainties.

This hands-on workshop is for D&A leaders seeking to unlock AI’s full potential as a professional co-pilot. Learn why mastering prompts is the first step to true AI fluency. Through live exercises focused on real-world D&A scenarios, you will learn the core techniques to build a more efficient and impactful analytics practice. Leave with a tactical toolkit of prompt engineering skills you can use immediately and a clear vision for your strategic AI value.

AI spending continues unabated and so is the pressure on leaders. CIOs need to demonstrate the value of AI. CFOs need to calculate it. The c-suite needs to collaborate to create real value. Here we present Gartner's framework for a rigorous, repeatable approach to financial operations (FinOps) of AI initiatives. The focus is on the economics of business value, cost and risk of AI, GenAI and agentic AI.

AI agents are fundamentally transforming traditional D&A operating models. D&A leaders must recognize this shift and adapt their operating models to integrate AI agents, ensuring AI-ready operations, enhanced value delivery and improved decision making. In this session, we will share three real client cases from Mastercard, Danone and Inspur that demonstrate how organizations have successfully made this transition.

No data? No AI! No metadata? No data! Gartner codified AI-ready data concepts and crafted the language in mid-2022. Practices have evolved for the four AI-ready data modes and 30 expected data evaluation and preparation steps. In this session, we recap the approach combined with how organizations are using AI data readiness as a foundation to evolve toward standardizing data for production AI.

AI leaders must strategically structure their organizations to maximize the business value of AI. This session equips you with practical guidance to build effective and scalable AI operating models and organizations.

Analytics and BI platforms and data Science and machine learning platforms are important technologies that drive insight-driven decision making and allow AI systems to be built and operationalized throughout the enterprise. This session unpacks the Magic Quadrants of both markets and gives inisght on the trends that you should be aware of.

The enormous potential business value of AI will not materialize spontaneously. AI leaders should guide their organizations toward an era in which AI is not only creating tangible business value but goes beyond to become a critical competitive differentiator and industry disruptor.

AI is drastically changing how organizations do business and operate. As a result, data, analytics and AI must be approached differently, with a focus on value — the organizational capabilities, competencies and processes required to utilize AI and data to redesign workflows and enable decisions orchestrated by both people and machines in near real time. This session gets you grounded on the capabilities of the intelligent enterprise, why it is important and how to pivot toward this model.

RAG has emerged as a powerful approach for building advanced AI systems that combine the strengths of large language models with external knowledge sources. However, RAG solutions struggle with reliability and require a lot of experimentation. This session will address key questions to help determine the best design pattern and optimization for RAG implementations.

AI agents are becoming a critical AI trend as they enable levels of business adaptability, flexibility and agility that can’t be achieved with traditional AI systems trained for a specific task. Their flexibility is valuable in unpredictable operating environments and real-time monitoring and control aren’t practical. Autonomous behaviors have significant societal, legal and ethical implications, but are the answer to the increasing complexity paralyzing our enterprise systems.

Join an interactive executive discussion on emerging trends from the 2026 CDAO Agenda Survey, highlighting cost optimization for D&A, scalable design patterns and the use of AI to improve D&A operations. Discover how these insights can drive efficiency and innovation across your organization.

This session will provide a blueprint for the comprehensive data, analytics, and AI capabilities and services that leaders need to build to enable their organization's AI ambition. It will explore the implications of your AI ambition archetype on your buying, deployment, and organization strategies.