The Gist<\/p>\n
What’s changing? Hightouch’s rebrand as an “Agentic CDP” and Databricks’ entry with CustomerLake are redefining what a customer data platform does. What’s the shift? CDPs are moving from storing unified customer profiles toward supplying AI agents with trusted, real-time context for decisions. What should buyers watch? Governance, data quality, real-time activation and open architecture matter more now than traditional feature checklists. <\/p>\n
For more than a decade, customer data platforms promised to give businesses a unified view of the customer. Since then, the category has evolved from standalone platforms to warehouse-native architectures, while cloud data providers have steadily expanded their own customer data capabilities. <\/p>\n
Now, with Hightouch rebranding itself as an agentic customer data platform (CDP)<\/a> and Databricks entering the market with its own customer data offering, the definition of a CDP is changing once again. The question is no longer whether businesses need customer data, but what role a CDP should play in an AI-driven enterprise. <\/p>\n This article examines how the category is evolving and what that means for marketing and customer experience<\/a> leaders.<\/p>\n FAQ: Agentic CDPs and the Future of Customer Data Platforms <\/p>\n Editor’s note: These questions address the shift from traditional CDPs to AI-ready customer intelligence platforms.<\/p>\n Do warehouse-native CDPs still matter if cloud providers add CDP features?<\/p>\n Yes, but competition is intensifying. Warehouse-native vendors like Hightouch popularized activating data directly from the warehouse, and now data infrastructure providers such as Databricks are moving into that same space.<\/p>\n How is Databricks’ CustomerLake different from a standalone CDP?<\/p>\n CustomerLake operates natively inside the Databricks lakehouse, letting businesses manage identity, segmentation and activation where their data already lives instead of copying it into a separate CDP platform.<\/p>\n What is an Agentic CDP?<\/p>\n An Agentic CDP is a customer data platform built to give AI agents trusted, real-time customer context so they can make and act on decisions across marketing, sales and service \u2014 beyond just storing unified profiles.<\/p>\n What should enterprises prioritize when choosing a CDP for AI initiatives?<\/p>\n Industry sources point to open architecture, interoperability, governance, data quality, AI readiness and real-time activation as the criteria that matter most for long-term AI support.<\/p>\n<\/p>\n Why Is the CDP Category Being Redefined Again? <\/p>\n For years, the primary role of a customer data platform was to consolidate customer information from marketing, sales, commerce and service systems into a trusted customer profile. By resolving fragmented identities and centralizing customer data, CDPs helped businesses improve audience segmentation<\/a>, personalization and campaign activation. Those capabilities remain essential today, but AI is changing what enterprises expect customer data platforms to do.<\/p>\n What Is Driving the CDP Category’s Latest Redefinition? <\/p>\n Hightouch’s rebrand as an Agentic CDP<\/a> and Databricks’ entry with CustomerLake<\/a> are forcing the market to redefine what a CDP does, shifting focus from unifying customer records toward supporting AI-driven decisions.<\/p>\n How AI Is Reshaping the Traditional CDP <\/p>\n Customer data platforms are expanding beyond their traditional role of unifying customer records. As AI becomes more deeply integrated into customer experience, CDPs are evolving into platforms that provide trusted context, support intelligent decision making and enable autonomous customer engagement.<\/p>\n Traditional CDPEmerging Agentic CDPStores unified customer profilesProvides trusted customer context for AICollects and organizes customer dataSupports real-time decision makingSegments audiencesRecommends next-best actionsActivates marketing campaignsCoordinates AI-driven customer journeysServes marketers and analystsSupports AI agents across marketing, sales and serviceFunctions primarily as a system of recordFunctions as an intelligence layer for enterprise AI\u00a0How Does an Agentic CDP Differ From a Traditional CDP? <\/p>\n A traditional CDP stores unified profiles and activates campaigns, while an emerging agentic CDP provides trusted context for AI agents, supports real-time decisions and coordinates AI-driven customer journeys.<\/p>\n Hightouch Bets on the Agentic CDP <\/p>\n Hightouch’s decision to rebrand itself as an Agentic CDP<\/a> reflects a broader change in the way customer data platforms are being positioned in the AI era. Rather than serving primarily as repositories for unified customer profiles, vendors are increasingly describing their platforms as systems that can provide trusted customer context for AI agents<\/a> capable of making decisions and taking action across marketing, sales and customer service.<\/p>\n In Hightouch’s vision, the CDP becomes more than a platform for audience segmentation and campaign activation. Instead, it serves as the foundation that enables AI agents to access current customer information, personalize interactions, recommend next-best actions<\/a> and automate portions of the customer journey. The emphasis moves from simply collecting customer data to activating that data through autonomous AI systems<\/a>.\u00a0<\/p>\n The debate extends beyond whether “Agentic CDP” becomes a lasting industry category. Many enterprise leaders are instead asking how AI is changing the purpose of customer data platforms themselves. <\/p>\n Derek Slager, co-founder and co-CEO at Amperity<\/a>, which offers a CDP, told CMSWire, “The bigger story isn’t the branding. It’s that AI is changing the role customer data plays inside enterprise systems. For years, CDPs helped teams better understand their customers and make better decisions. Increasingly, they’re providing the operational context that AI systems use to make decisions in real time.”\u00a0<\/p>\n Slager said businesses should focus less on whether vendors describe their products as traditional, composable or agentic CDPs and more on whether they can consistently provide accurate, trusted customer context wherever AI-driven decisions are being made.<\/p>\n What Does Hightouch’s Agentic CDP Positioning Actually Enable? <\/p>\n Hightouch’s Agentic CDP framing lets AI agents access current, governed customer information to personalize interactions, recommend next-best actions and automate parts of the customer journey, rather than simply storing profiles for later activation.<\/p>\n Related Article: Hightouch Unveils AI-Powered Identity Resolution for CDPs<\/a><\/p>\n Databricks Changes the Competitive Environment <\/p>\n Databricks’ entry into the customer data platform market signals a broader transition in enterprise data strategy. With the introduction of CustomerLake<\/a>, its new customer data platform, the company is moving beyond its traditional role as a cloud data and analytics platform, bringing customer identity, audience management and activation capabilities directly into the data lakehouse. Rather than requiring businesses to move customer data into a separate platform, CustomerLake enables businesses to manage and activate customer data where it already resides.<\/p>\n That approach reflects the growing popularity of warehouse-native architectures<\/a>, which have challenged many of the assumptions behind traditional CDPs. Instead of copying customer information into proprietary databases, warehouse-native platforms use cloud data warehouses as the primary source of truth while adding identity resolution<\/a>, segmentation and activation capabilities on top. The result is a more unified data architecture that reduces duplication while giving marketing, sales and customer service teams access to consistent customer information.<\/p>\n Jeff Hensel, broker associate at North Coast Financial<\/a>, said architectures built around scheduled synchronization may struggle to keep pace with AI-driven decision making. “Batch processing built CDPs and scheduled syncs are going to be constrained by the speed that AI agents actually run at,” he said, and stressed that AI systems increasingly require access to current information rather than periodically refreshed datasets.<\/p>\n Hensel said his lending business relies on current collateral valuations rather than historical projections because lending decisions depend on the freshest available information. He argued that the same principle applies to enterprise AI: autonomous systems are only as effective as the quality and timeliness of the data they receive.<\/p>\n For composable CDP<\/a> vendors, this represents both validation and increased competition. Companies such as Hightouch helped popularize the warehouse-native model by demonstrating that customer data activation could occur directly from cloud data platforms. Now, however, data infrastructure providers such as Databricks are expanding higher into the application stack, offering capabilities that overlap with those traditionally provided by dedicated CDPs.<\/p>\n The result is a market where the boundaries between data platforms, customer data platforms and AI platforms are becoming increasingly difficult to distinguish. As enterprise vendors continue adding customer intelligence, governance and AI capabilities, businesses that are evaluating CDPs may find themselves comparing fundamentally different architectural approaches rather than products within a clearly defined software category.<\/p>\n Why Does Databricks’ CustomerLake Threaten Traditional CDP Vendors? <\/p>\n CustomerLake brings identity resolution, audience management and activation directly into the Databricks lakehouse, letting businesses skip a separate CDP and challenging warehouse-native vendors like Hightouch on their own architectural turf.<\/p>\n Related Article: Why Databricks’ CustomerLake Just Rewired the CDP Space<\/a><\/p>\n Is the CDP Becoming an Intelligence Layer? <\/p>\n For much of their history, customer data platforms were designed to solve a data management problem. Their primary function was to unify customer records from disconnected systems, resolve identities across channels and create a single source of truth<\/a> that marketing teams could use for segmentation and personalization. While those capabilities remain essential, AI is changing expectations for what customer data platforms should actually do.<\/p>\n View All<\/a> <\/p>\n As businesses deploy AI across customer-facing applications, simply storing customer information is no longer enough. AI agents require persistent, trusted customer context<\/a> that helps them understand previous interactions, customer preferences, purchase history and business rules before generating recommendations or taking action. Rather than serving only as repositories of customer profiles, CDPs are beginning to provide the contextual foundation that enables AI systems to make more informed decisions.<\/p>\n That evolution also requires platforms to provide more than unified customer profiles<\/a>. As AI assumes greater responsibility for customer interactions, businesses increasingly need systems that continuously maintain trusted customer context rather than static customer records. Slager said, “The role of a customer data platform shifts from organizing customer records to maintaining a dependable understanding of the customer as that understanding evolves.” He added that AI systems require customer context that updates continuously, remains governed and allows businesses to understand how customer information influenced AI-generated decisions.\u00a0<\/p>\n What Makes a CDP an Intelligence Layer Rather Than a Database? <\/p>\n A CDP becomes an intelligence layer when it continuously maintains governed, up-to-date customer context that AI agents can reason over, rather than serving only as a static repository of customer records.<\/p>\n The Next Battleground: Data or Decisioning?\u00a0<\/p>\n AI is shifting the focus toward decisioning over data when it comes to CDPs. Enterprise technology leaders believe prospective buyers should now evaluate CDPs according to how effectively they support AI-driven decision making rather than traditional customer data management alone. <\/p>\n Abhijit Chanda, director and head of retail media at Tredence<\/a>, told CMSWire, “The next phase is to progress towards decisioning, orchestration and action with AI, and the CDP now acts as a customer intelligence layer to help make decisions on the next step in the system, rather than simply where customer data is stored.” Chanda said buyers should increasingly prioritize capabilities such as governance, AI-ready data quality, real-time identity resolution<\/a> and decision intelligence over traditional audience activation features.\u00a0<\/p>\n Why Is Decisioning Replacing Data Volume as the CDP Battleground? <\/p>\n With customer data now widely centralized in warehouses and lakehouses, the competitive edge has shifted from who stores the most customer data to which platform can turn that data into accurate, real-time AI decisions.\u00a0<\/p>\n Key Questions to Ask When Evaluating an AI-Ready Customer Data Platform <\/p>\n As customer data platforms evolve to support AI-driven customer experiences, buyers should evaluate more than traditional CDP capabilities. The table below highlights several areas that are becoming increasingly important when selecting a platform for long-term AI initiatives.<\/p>\n Evaluation AreaWhy It MattersOpen architectureSupports integration with existing enterprise platforms and future AI tools.InteroperabilityEnables customer context to flow across marketing, sales, service and commerce applications.GovernanceHelps ensure AI systems operate using trusted, policy-compliant customer information.Data qualityImproves AI accuracy by providing reliable customer identities and records.AI readinessProvides the contextual foundation needed for AI agents and intelligent automation.Real-time activationAllows AI systems to respond to changing customer behavior as interactions occur.FlexibilitySupports evolving architectures as AI capabilities continue to mature. Openness and Interoperability as Evaluation Criteria <\/p>\n AI systems are most effective when they can securely access trusted customer information across CRM, marketing automation, ecommerce, customer service and data platforms. Businesses should evaluate how well a prospective CDP integrates with existing technology investments, supports open standards and minimizes unnecessary data duplication. Platforms that operate effectively within broader enterprise architectures are likely to remain more adaptable as AI capabilities continue to improve.<\/p>\n Governance and data quality<\/a> should also become central evaluation criteria. AI agents are only as reliable as the information they receive, making accurate customer identities, well-governed data and clear access controls essential for generating consistent, explainable and policy-compliant decisions.<\/p>\n Industry observers also stress that enterprise buyers should prioritize architectural flexibility over feature checklists when evaluating AI-ready customer data platforms. <\/p>\n