Customer Data Platform Are Dead. Long Live the New CDP

Risto

Jan 15, 2026 — 3 min read

David Raab introduced the concept of the Customer Data Platform (CDP) in 2016 to unify customer data, but the rise of AI demands a new chapter. The principles of CDPs are now being taken to the next level, evolving into a more generic, holistic approach to data platforms that integrates all business entities, not just customers. We call this the fact-sourcing platform, enabling AI to access full context, answer complex questions, and power intelligent decision-making across the entire business.

TL;DR:

Traditional CDPs focused on customer data are no longer enough in the AI era, which demands answers spanning products, warranties, and post-sale processes. CDPs are evolving into holistic systems that integrate all business entities, not just customers, creating a unified source of facts and context. This evolution enables AI to provide accurate real-time answers, support decision-making, and power intelligent applications across the entire business.

Imagine this: a customer asks your virtual agent: “Can I return a product, and if so, when and where should I send it?” or: “As a loyal customer, do I have any discounts?” The agent goes silent. Not because it’s “stupid” – simply because it lacks context. Product data, warranties, returns, post-sale interactions, and the customer’s history are scattered across multiple systems. Even the most sophisticated AI cannot provide a complete answer.

This is the reality facing many businesses today: in an era where AI is expected to answer increasingly complex questions, traditional CDPs are no longer enough.

The Limits of Traditional CDPs

Customer Data Platforms (CDPs) revolutionized the way companies store and integrate customer data. They reduce silos in customer information, connect purchase history with interactions, and allow some AI personalization. Yet, the classical CDP has significant blind spots:

Products – features, versions, availability
Warranties and returns – terms, statuses, links to purchases
Post-sale service – tickets, complaints, resolutions
etc.

Without integration across these domains, a virtual agent is forced to hop between systems or, worse, provide incomplete answers.

Real-world examples:

“What is the warranty status of my recently purchased product?” – a traditional CDP cannot link the purchase with warranty data.
“Can I return a product, and if so, when and where should I send it?” – requires product, return, and post-sale service data.
“As a loyal customer, do I have any discounts?” – requires segmenting the customer by status and purchase history.

CDPs as a Blueprint – But Not the Destination

Traditional CDPs opened our eyes to how effectively we can store and connect customer data. The same principles can be applied to all business-critical entities: products, warranties, transactions, and post-sale processes. This evolution transforms the concept of a CDP from a “customer-only” platform to a holistic fact-sourcing platform.

Such platforms become an ideal foundation for AI memory, allowing intuitive access to information and the ability to connect facts across the business. AI can now answer complex questions in real-time, support executives with analytics, and enable sophisticated customer service.

Segmentation and Analysis Across Entities

The segmentation ideas in CDPs—traditionally applied to customers—can now extend to products, warranties, and processes. For example:

“What percentage of products are returned?”
“Which products are most frequently returned, and why?”

These insights are critical not only for customer service but also for executives and analysts, and demonstrate how the platform can power decision-making agents for management.

Moreover, this approach enables the construction of personal AI memories, storing entities such as people, locations, and events. The system can remember contextual facts and answer complex, personal questions—essentially becoming a platform for storing and retrieving structured knowledge across domains.

Next-Generation Platforms – Integrating All Business Entities

For AI to answer complex questions effectively, businesses need to move beyond traditional CDPs to platforms integrating all critical entities: customers, products, transactions, warranties, post-sale processes, etc will all its changes. With this evolution:

AI gains full context, connecting information across entities (e.g., product purchase ↔ warranty ↔ post-sale service)
Support agents can respond in real-time, without hopping across systems
Data analysis becomes holistic, supporting operations, strategy, and executive decision-making

Conclusion

Traditional CDPs were indispensable for customer data integration, but in the age of AI, their limitations are clear. The new generation of CDPs—fact-sourcing platforms—integrates all business-critical entities, provides intuitive data access, supports AI memory, and enables holistic analysis for both operational and strategic needs.

The lesson is clear: the CDP has evolved. What started as a customer-centric data hub is now a platform for facts, context, and intelligent decision-making. Businesses that embrace this evolution gain a competitive edge and can finally unlock the full potential of artificial intelligence.