Takeaways from Gartner Data & Analytics Summit 2026 London: Make Your Data AI-Ready

Round two of the Gartner Data & Analytics Summit 2026 is officially in the books, and London did not disappoint. We picked up right where we left off in Orlando, continuing conversations that focused on the growing importance of high-quality, trustworthy data. After all, with AI and AI agents fundamentally changing the ways in which we work, it’s no surprise that the need for AI-ready data dominated conversations throughout the event. 

But in addition to insights gathered in Orlando, which included abolishing dashboards, the importance of context and trust, and the continued need for strong governance, the dialogue in London shifted toward agentic strategy—specifically, ensuring that enterprise data is not just high-quality, but is also fit for use in AI systems and AI agents. This shift brings solutions like AI-native master data management (MDM) to the forefront, providing the advanced capabilities needed to deliver truly AI-ready data. 

What Is AI-Ready Data? 

AI-ready data is more than just golden records. While golden records provide the foundation by helping AI systems and agents to understand “what” the entity is, AI-ready data also needs context—the “how” and the “why” of an entity’s relationship across the organization. 

Think of it like a sentence. A golden record is the noun. On its own, it’s just a label. But when you add verbs, adjectives, adverbs, and other parts of speech—the context—it becomes a full sentence.

‍How to Make Data AI-Ready

To make enterprise data AI-ready, organizations need to move beyond publicly available and fragmented organizational data to provide AI systems and agents with a rich web of entity relationships that define how the business actually functions. This is context.   

True reasoning in AI requires more than just identifying patterns; it demands a structured, governed foundation of organizational knowledge. Context bridges this gap by providing data with situational awareness, data provenance, and underlying business logic. By explaining the “why” behind the information, context equips agentic AI with the depth needed to generate trustworthy insights and execute sound decisions.

Without context, AI runs the risk of misinterpreting signals and producing inaccurate or misleading insights. And because AI moves so fast, these misinterpretations and potential hallucinations can quickly propagate throughout business processes, exposing fissures and cracks across operational systems and leading to outcomes that are problematic or worse.

The Role of AI-Native MDM 

An AI-native MDM solution like Tamr helps organizations translate the need for AI-ready data into operational reality. With the powerful combination of advanced AI models, agentic data curation, and select business rules, Tamr delivers the clean, trustworthy, AI-ready data that systems and agents need to support accurate business decisions. 

Using Tamr’s agentic data curation capabilities, organizations can advance their agentic strategy by using AI agents to help solve the “last mile”—the 5-10% of data that remains unresolved after the mastering process—that requires a higher level of knowledge, precision, and data preparation. 

This final phase of data curation is often the most challenging and most time-consuming for humans, filled with anomalies, idiosyncrasies, and edge cases that are difficult to decipher. AI agents help to streamline this part of the process by automatically cleaning, curating, and refining the data close to consumption. Agents compare entity matches and determine if the records do—or do not—match, providing humans with the initial analysis they need to decide if they trust the output or if they need to tune the models further. 

Further, using enterprise knowledge graphs, organizations can utilize Tamr to deliver connected, contextualized views of their data that highlight the relationships among and between key business entities. By identifying these cross-entity relationships, organizations can reveal meaningful connections that surface valuable, actionable insights and enable AI systems and agents to deliver better outputs. 

AI-Native MDM: The Necessary Modernization to Deliver AI-Ready Data

The discussions in London highlighted a definitive shift in conversation, with data leaders aligning on the urgent need for AI-ready data. As advanced agentic strategies take hold, organizations can no longer rely on traditional MDM solutions to deliver the high-quality, contextual foundation that AI systems and agents demand. Instead, they must embrace a modern, AI-native MDM solution like Tamr to supply the clean, curated, contextual data necessary for producing truly trustworthy AI outputs.