Gartner named AI-ready data as one of three pillars to support strategic AI opportunities in its CMO Leadership Vision outline for 2026 (See Figure 1). With AI “transforming the structure, strategy and scope of the marketing function,” success requires that CMOs shift to human-agent teams that redefine how marketing creates value, and earn trust through “emotionally relevant brand experiences.”
Content and experience orchestration were named the other two strategic pillars necessary to capitalize on AI, with the overarching objective of driving growth through multichannel personalization.
What is AI-Ready Data?
AI-ready data is data that is transformed, cleansed and enriched immediately upon ingestion to be contextually usable for any AI use case. To be considered truly AI-ready, data must both be “right” and “fit-for-purpose”.
The right data is data that is complete, accurate, and timely.
- Complete: It reflects a full understanding of a customer, household, or business entity using data from all sources, and of all types.
- Accurate: Identity is resolved at the individual, household or business entity level using deterministic and probabilistic matching.
- Timely: Unified profiles, attributes, and model scores are continually updated and made available across the enterprise in the cadence of an individual customer journey.
Fit-for-purpose data means that data is actionable, trusted and compliant.
- Actionable: It is in the appropriate form for consumption by downstream applications and users
- Trusted: It is observable and tunable so users can verify results
- Compliant: PII data is secure with managed permissions for sensitive information
The Data Readiness Process
The production of AI-ready data is the primary function of a data readiness hub, which cleanses, standardizes and matches data at the point of ingestion before it reaches downstream processes or third-party applications. The creation of a golden record – a unified customer profile – is a foundational data product produced by a data readiness hub. Validating that data are right and fit-for-purpose via autonomous data quality from ingress to activation, a data readiness hub gives data the needed context for AI.
Figure 1: Strategic pillars for driving growth with multichannel personalization.
A data readiness hub provides the strong data foundation that is a prerequisite for an effective AI strategy. AI-ready data improves the accuracy and reliability of models, reduces bias, and saves costs by eliminating the need for manual workflows for data standardization. With this foundation, AI agents have the needed precision and updated context required to execute intelligent, autonomous actions and to provide more relevant, trustworthy outcomes. That precision and context is required for both human-driven workflows as well as for agents making sense of the data itself.
Agentic AI in Action
Referring to Gartner’s three-pillar pyramid for driving growth through multichannel personalization, one purpose for AI-ready data is to identify new segments, which bleeds into the second pillar: content, where they claim AI makes it possible to generate 10X more content (for all those new segments!) at no additional cost.
The first two pillars are tightly intertwined, with AI-ready data as the common denominator. AI-ready data fuels more granular segmentation, which creates the need for more content to tailor to those segments and/or indivduals. Consider, for example, a segmentation agent as a key agentic AI application, where the intelligent agent recommends and creates dynamic segments that can be immediately used to orchestrate customer journeys. Unlike a static audience list, these AI-driven segments adapt as AI evaluates contextual signals and customer data in real time.
In a human-driven, prompt-based workflow (e.g., “build me a segment”), AI executes complex data queries and logic to generate an audience. In both human and data-driven use cases, the effectiveness of AI-powered segmentation is directly tied to the quality of the underlying data. The use of high-quality data (right and fit-for-purpose) powers a closed-loop cycle of improved outcomes; with high-quality data inputs, AI models generate more meaningful, trustworthy insights, which in turn lead to more accurate unified profiles and more precise audience targeting. In this manner, audience building is always based on the most current, accurate and “right” customer context.
A Composable Hub
Gartner posits that composable tech (including a CDP “hub”) is a foundational technology to support multichannel personalization.
The reason is that in the world of agentic AI, even with a data readiness hub providing AI-ready data, AI agents do not operate on an island or in a closed system. Rather, they work in concert with other agents from multiple sources. A composable data readiness hub allows the agents to work together in an interoperable way, with model context protocol (MCP) servers providing a common understanding – a single source of truth.
Ideally, agents will sit near the data connecting with the data readiness hub and thus have access to a solid data foundation without having to continually repeat core processes, such as identity resolution. In bringing the application close to the data, the data readiness hub adheres to the principles of data gravity while still recognizing that agentic AI changes the calculus, requiring the need for distributed data. A data readiness hub sits at the intersection of data gravity, MCP servers, localized agents and orchestration, offering agents a consistent framework for human-driven and data-driven tasks without requiring constant data movement.
A composable hub allows agents to orchestrate journeys using that solid data foundation. Just like humans interacting with GenAI, agents are asking questions of the data and retrieving answers where the decisioning is happening close to the data.
Agentic AI & Composability: a Framework for Growth
The combination of a composable data readiness hub and agentic AI technologies forms the backbone of effective multichannel personalization. By providing AI-ready data and fostering interoperability among agents, organizations can ensure that both human-driven and autonomous workflows are grounded in accurate, current and contextually rich information. This solid data foundation enables more dynamic segmentation and targeted content creation, and also supports the orchestration of customer journeys at scale, resulting in more relevant and trustworthy outcomes for businesses and their customers.
