Databricks has announced it’s decision to enter the marketing software market with CustomerLake.
The AI-native customer data platform uses autonomous agents to continuously personalize customer interactions, while keeping all customer data and AI workflows inside the Databricks Lakehouse platform.
This launch reflects a broader industry shift toward unified data and AI platforms where data and activation are consolidated into a single governed system.
Ali Ghodsi, Co-founder and CEO of Databricks, explained that marketing is now shifting from planned, campaign-based execution to always-on AI systems that continuously use unified customer data to personalize interactions in real-time.
“Marketers need to reimagine their entire foundation — not just the campaigns they run, but the customers they run them for, which now include agents,” he said.
“With CustomerLake, customer data, AI models, and agents live in one governed platform. Marketing stops being a series of campaigns and becomes a continuous loop — agents that constantly analyze, decide, and act on every customer in real time.
“For the first time, enterprises can deliver infinity campaigns and 1:1 personalization at scale.”
The Data Problem Comes First
AI systems are only as good as the customer data they can access, with information often spread across dozens of systems, each storing a slightly different version of the same customer.
As organizations add AI into various customer-facing workflows, these inconsistencies in customer history can become a much larger problem, as AI models depend on accurate, unified data to make decisions.
Disconnected tools can create several operational challenges as more teams spend significant time moving data between systems rather than focusing on customer engagement.
This adds unnecessary costs to a business as data duplication increases storage, and reporting becomes unreliable when departments use mismatched data.
Fragmented customer behavior can cause marketing teams to target the wrong customers and miss opportunities, and when AI models are trained on inconsistent or incomplete data, they can generate inaccurate predictions and ineffective personalization.
Furthermore, fragmented customer data can create compliance and governance risks as organizations struggle to understand where customer information resides and how it is being used.
In an AI-driven environment, AI needs immediate access to complete, trustworthy information, meaning if customer data is scattered across disconnected platforms, this often results in slower decision-making, lower accuracy, and reduced business value from AI investments.
As a result, many enterprises are trying to consolidate customer data into a single, governed foundation where customer data and AI models can operate from the same source of truth.
Speaking with CX Today, Martin Taylor, CEO at Content Guru, explained that CDPs solve this issue by creating an accurate view of the customer that AI models can trust.
“If you’re going to run AI, you’ve got to have your data straight,” he explained.
“Part of the role of omnidata and the customer data platform is to take a lawn roller over all of that [data] and make it nice and tidy so that it is consistent and accurate, all of that is enabled by these customer data platforms.
“No data, no AI.”
Before organizations can benefit from advanced models and autonomous agents, they must first establish consistent, accurate, and unified customer data.
CDPs and similar data foundations exist to solve that problem, creating a trusted data layer that allows AI systems to function effectively.
Built for What’s Next
Announcing the launch at the company’s Data + AI Summit 2026, Databricks new CDP, CustomerLake, is designed to help enterprises unify customer data, apply AI directly to that data, and activate personalized CX from a single platform.
This launch has enabled Databricks to go beyond its traditional role as a data and analytics platform and expand into the CX and marketing technology market.
CustomerLake aims to solve the widely recognized fragmented CRM system issue by creating a unified customer data layer directly within the Databricks Lakehouse architecture, enabling marketing and CX to operate from the same source of truth.
This resolution occurs by ingesting customer data from multiple systems and matching the correct information to the correct identities, making those profiles available to both marketers and AI agents.
Furthermore, the CDP keeps customer data within the Databricks platform, enabling it to be governed and analyzed in one place.
This reflects the industry-wide shift toward integrating CDPs directly into enterprise data platforms, as more vendors increasingly believe these capabilities should be consolidated.
CustomerLake embodies this approach by treating customer data as a core enterprise asset that powers both marketing operations and AI-driven customer engagement.
Today, more vendors are expected to deliver trusted, unified customer data foundations that is going to drive the next generation of CX for enterprises.
Databricks and Salesforce Join Forces
Databricks has also announced a partnership with Salesforce to solve the data, governance, and execution problem within the industry.
Moving beyond the question of ‘Do I have enough trustworthy customer context?’, the partnership now takes into question, ‘How do humans and AI agents use this completed context to get the work done?’.
By connecting Databricks’ governed enterprise data with Salesforce’s customer relationships, workflows, and business context, the vendors can build an environment where both employees and AI agents can access the same trusted information and take action within approved processes.
Andy Kofoid, President of Global Field Operations at Databricks, highlighted that enterprises can only use AI agents effectively when unification occurs.
“Customers consistently tell us they want AI agents to become a larger part of how work gets done across the enterprise. To make this a reality, they need access to trusted data, business context, and governance controls wherever that information lives,” he said.
“Together, Salesforce and Databricks are helping joint customers connect governed data and business context across platforms, giving humans and agents the shared foundation they need to search, reason, and act with confidence.”
