Know before you go: Gartner Data & Analytics Summits 2026

Turn your AI momentum into measurable results

Generative AI and agentic systems have moved from innovation labs to the center of enterprise strategy. AI is on every board agenda, roadmap deck, and nearly every strategic plan. Yet many organizations are still wrestling with the same foundational questions: Why isn’t AI delivering measurable enterprise value? Why does every pilot feel harder to operationalize and scale than expected? Why is governance always catching up to innovation?

One truth underpins the answer to these questions: AI success depends on data quality. Without trusted data, AI initiatives cannot scale.

As you prepare for the Gartner Data & Analytics Summits in Orlando, London, and Sydney, this year’s theme—Value at AI Velocity: Navigating the Now and Next—couldn’t be more timely. Most organizations already have momentum. Velocity isn’t the hard part anymore. The challenge is converting that momentum into productivity, trust, and measurable business outcomes.

If you’re heading to one of the Summits, here’s what to understand before you go.

Agentic AI is here to stay, so governance must catch up

The rise of AI agents and multi-agent systems are front and center for this year’s Summit agenda.

Agents are no longer confined to isolated workflows or sandbox environments, but are being embedded into enterprise applications where they orchestrate decisions and trigger actions autonomously. This autonomy offers enormous potential for real-time decision making. At the same time, it introduces new risks, including those posed by nondeterministic behavior, reduced human oversight, cross-system error propagation, and ambiguity around roles, responsibilities, and accountability.

Sessions at Gartner Orlando, London, and Sydney on governing AI agents will underscore a growing understanding: Control mechanisms need to be in place before AI agent autonomy expands beyond what the organization can safely oversee. The question leaders should bring is not whether to adopt agentic AI but how to govern it at scale without slowing innovation.

If your organization is looking to better understand how your governance practices can keep pace with agentic AI innovation, here are two sessions we’d recommend you attend:

ROI is the new AI maturity benchmark

Greater focus on calculating the value and cost of AI agents in this year’s event agenda sends a clear signal to data leaders and their teams. In earlier waves of agentic AI adoption, experimentation budgets were often exempt from rigorous value tracking, but that tolerance has faded.

AI investments are growing, and scrutiny is growing alongside them. Teams are increasingly expected to demonstrate:

• Total cost of ownership across infrastructure and data preparation
• Quantifiable productivity gains
• Cost avoidance or revenue generation
• Risk mitigation impact and time-to-value

AI ROI is rarely driven by the model alone. More often, it’s driven by operational readiness, including clean, trusted, well-governed data, a clear prioritization of use cases, and strong architecture.

To better understand how to measure and prove the business value of your AI initiatives, add the sessions below to your agenda:

Data foundations determine AI success

This year’s Summit agenda shines a spotlight on data architecture and unstructured data management.

Nearly every agentic AI use case requires extracting and governing large volumes of unstructured data. Text, documents, and images are messy, inconsistently labeled, and often poorly cataloged. Without orchestrated workflows for entity extraction and semantic enrichment, agentic AI remains incomplete. And that lack of full context doesn’t allow for scale. 

Organizations that struggle with incomplete metadata, limited lineage, or siloed architectures will feel those gaps even more sharply in an AI-driven environment.

If your business still centers data quality and related practices on structured data only, the session below will offer important insights.

Designing the AI organization of the future

AI represents an organizational shift as much as a technological one. At the Summit, sessions on designing the AI organization and adapting operating models for an agentic era highlight the fact that leadership itself needs to evolve and adapt to the new reality.

Today’s leaders grapple with questions such as:

• Where AI sits in the organization
• How centralized AI hubs interact with federated business teams
• How to integrate agents into data and analytics workflows
• Who owns agent performance and risk

Data and AI leaders have to become value architects, not just technology sponsors. They need to champion governance while translating between business ambition and technical feasibility. AI is also reshaping workforce expectations and career paths, and the summit offers space to reflect on how leadership roles are evolving.

Prepare strategically for Gartner Summits

From the hundreds of individual sessions on offer at the Gartner Data & Analytics Summits in Orlando, London, and Sydney, a broader, unified narrative takes shape for teams evaluating a Gartner data platform. Gartner is signaling that scaling AI is more complex than launching it, and that data trust is the foundation of generating sustainable value with AI.

This year’s Summit isn’t about generating buzz and excitement, but about successful and sustainable execution. AI velocity without operational discipline leads to fragmentation and stalled transformation, and the goal is to help leaders understand how to best execute on goals in a dynamic, unprecedented environment. 

We’d love to chat further about transforming momentum into real, measurable results, and how we can help your organization go from data desert to data trust oasis. Stop by our Engagement Zone on site to learn more, or schedule a meeting with our team.