Analytics has always been "ground zero" for citizen developers and the low-code/no-code movement. However, the shift toward generative AI and the avalanche of analytics content will overwhelm current analytics operating models, which are already struggling to scale delivery.

As AI shapes business decisions, making unstructured data AI-ready is a key governance priority. The quality, accessibility and security of unstructured data directly determine the performance of AI applications, particularly for GenAI. To unlock its value for AI initiatives, data and business leaders should evolve their governance strategies to effectively manage, protect and utilize unstructured data, ensuring it is AI-ready while meeting compliance and security requirements.

Despite advancements in GenAI, self-service analytics continues to fail at delivering its expected impact to organizations due to governance, trust, accuracy, scalability and adoption challenges. This presentation provides CDAOs with strategies to derive more value from analytics investments.

Generative AI technologies and techniques are evolving at an unprecedented pace, accompanied by significant hype, making it challenging for leaders to navigate this dynamic landscape. AI leaders can leverage this Hype Cycle to identify the innovations most worth pursuing as they shape and execute their AI strategies.

Many organizations are wondering how GenAI will impact their D&A governance and management tools, and by extension, their D&A governance operations. This research provides an overview of how key D&A markets are incorporating GenAI capabilities and how these will change the nature of D&A governance.

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?

The buzz: “Dashboards are dead.” Yet, they remain the backbone of enterprise analytics for operational oversight. GenAI is revolutionizing the space, promising to streamline migration and inject intelligence, but only 8% of new reports use it today. Explore the “death of dashboards” narrative, along with the opportunities and pitfalls of migrating to next-gen analytics.

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.

Almost every GenAI use case requires organizations to extract, qualify and govern significant volumes of unstructured data. Data management leaders must deliver workflows that orchestrate entity extraction, vector data embeddings and semantic data enrichment with structured data pipelines to deliver GenAI-ready data. Join this session to learn more.

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 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 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.