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.

Organizations often struggle to collaborate across analytic silos. By treating metrics as data products as part of a franchise delivery model, data leaders can improve the quality and consistency of analytic content across the organization.

We will discuss the latest issues that heads of data governance must address and provide recommendations to manage and capitalize on emerging opportunities. By understanding key drivers and trends, leaders will be equipped to optimize their data governance strategies.

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.

Snowflake and Databricks have become significant vendors to many enterprises, with increasing product offerings and broad array of use cases. This session provides attendees with strategies and tactics to negotiate the best contractual deal with Snowflake or Databricks and to pose questions when it comes to dealing with them. D&A leaders need to understand how their organization can best estimate future usage needs, mitigate risk when negotiating with the vendor, discover how to best attain leverage, what is and is not leverage in Salesforce's view, etc.

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.

Data lakehouses continue to be hyped, but do they replace or complement data lakes and data warehouses? Where do we stand from an architectural perspective? What is hype, and what is real? What should be expected in the coming years?

To position their teams as a must-have discipline that can fulfill cross-functional use cases, D&A leaders must optimize their organizational model. The optimal model balances enterprisewide capabilities focused on enablement with decentralized needs focused on outcomes. Discover the design principles that underpin the ideal model and learn how to strike a balance between centralized consistency and decentralized agility.

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.

This session will explore the challenges faced by data management leaders and offer actionable recommendations to address them. By understanding and overcoming these obstacles, leaders can optimize their data management strategies and drive more informed decision making.

This workshop will cover the end-to-end process of managing a data product, from ideation to launch. It will include product roadmap development, stakeholder management and go-to-market stra tegies.

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.

This session covers the use and output of Gartner’s AI-Ready Data Toolkit, which includes practices for both structured and unstructured data. The process develops metrics that “stack” as you progress from POCs to multicontext data use, operationalization and production support. The session also explains how to customize the toolkit with your own thresholds and readiness analysis.

AI is accelerating new possibilities for data and analytics everywhere. Success isn’t always about being the fastest, but about finding your own path to value, while managing risk and cost. Join our Gartner’s Opening Keynote to discover how a thoughtful approach to speed and direction helps you prepare for what’s next, no matter where you are today.

Keynote on the multi-agent landscape.

AI is having a huge impact, but is not the only thing with societal, technological, and organizational implications driving change in data and analytics. We examine trends in areas such as complexity, trust, and empowerment facing leaders and teams as they make decisions in all aspects of their bet-the-business D&A strategy.

Metadata, data quality and data observability tools provide significant capabilities to ensure good data for your BI and AI initiatives. Metadata tools help discover, and inventory your data assets. Data quality tools help business users manage their data at sources by setting rules and policies. Data observability tools give organizations integrated visibility over the health of data, data pipeline and data landscape. Together the tools help organizations lay good foundation in data management for BI and AI initiatives.

Data and analytics leaders can use the concept of a “franchise” to communicate the optimal organizational model, data architecture and governance framework that large enterprises require to scale self-service analytics programs.

Data and analytics leaders need to support the opportunities and challenges of today’s digital business with the right competencies. This is the time to evaluate data and analytics roles and skills that are fit for now and the future. This session will provide key considerations for D&A and AI roles and skills.