CDPs in 2026? Delivering trustworthy customer context for AI

The Customer Data Platform (CDP) category is probably one of the most confusing spaces in SaaS. For example, Salesforce and RudderStack both show up as “CDPs,” yet we have very little overlap, across product capabilities, underlying architecture, or the personas who buy and use the product.

To make sense of this sprawl, the CDP Institute famously classified CDPs into four layers: Data CDPs, Analytics CDPs, Campaign CDPs, and Delivery CDPs. That taxonomy itself is a signal of how broad and overloaded the category had become.

Over time, the market has only fragmented further. Data warehouses absorbed parts of the traditional CDP stack (like profile storage and transformation). Composable CDPs as a category emerged—for example, Hightouch, Census, GrowthLoop, RudderStack, Snowplow. But even within that label, the differences are stark. Infrastructure-first platforms like RudderStack and Snowplow look fundamentally different from activation-led platforms like Hightouch, Census or GrowthLoop.

This post is not another attempt to complain about the category. Instead, it’s a prediction, grounded in working with thousands of customers, about where CDPs (specifically data-focused CDPs) are headed.

The future of CDPs is “trustworthy customer context for the AI era”

At their core, data CDPs like RudderStack started as infrastructure for delivering trustworthy customer data.

“Trust” is a broad word, but in practice it shows up very concretely: duplicate users, missing events, stale attributes, schema drift, broken joins, silent data loss, broken SQL transformations, ML model drifts and so on. The downstream impact is real. Teams can’t trust dashboards. Marketing campaigns misfire.The business starts second-guessing its own numbers.

The original pitch of data CDPs was simple: DIY data stacks are fragile. Deploy us instead. That pitch was compelling in the pre-AI era, and it still holds. But the stakes have changed dramatically.

When broken data meant a dashboard was wrong for a few hours, it was painful but tolerable. When that same data is now powering customer-facing AI agents—deciding what to recommend, what to say, or how to act—the bar for trust is orders of magnitude higher. There is no room for “mostly right” context when an agent is interacting directly with a customer.

This is why, at RudderStack, so many of our product bets have converged on data trust: governance, infrastructure-as-code, real-time guarantees, and increasingly, using AI itself to automatically debug and heal data issues. Trust is no longer a nice-to-have. In the AI era, it’s table stakes.

From unification to customer context

The second pillar is customer context.

Customer context is the process of taking everything you know about a customer (e.g., event data, warehouse data, and derived attributes) and distilling it into something coherent, accurate, and usable. Historically, we called this “unification.” It involved identity stitching, attribute creation, predictive models like LTV, and ultimately producing a Customer 360 (C360).

The primary use case for that C360 was segmentation or passing profiles into downstream SaaS tools.

That’s changing.

Increasingly, the consumer of the C360 is not a human or a static workflow. It’s an AI system or an autonomous agent. In that world, context engineering becomes critical. AI systems don’t just need data; they need high-quality, well-structured, explainable, and versioned context they can reason over and yet that fits into their context-windows.

This is where RudderStack Profiles lives. The goal is not just to assemble data, but to prepare customer context that downstream systems can reliably act on, and to do so in a way that is observable, debuggable, and trustworthy. Fragile, hand-rolled pipelines are one of the biggest hidden sources of mistrust, especially when AI systems are involved.

What does “AI Era” actually mean?

Finally, there’s the question everyone asks: what does the AI era really imply for CDPs and SaaS more broadly?

Despite the doomsday narratives, SaaS isn’t going away anytime soon. What is changing is pricing pressure and competitive intensity. AI enables incumbents, as well as new entrants, to build equivalent functionality faster and cheaper than ever before.

But regardless of whether the interface is a dashboard, a workflow, or an autonomous agent, all of these systems depend on one thing: trustworthy data and reliable customer context.

Agentic applications don’t eliminate the need for data infrastructure. They amplify it. If anything, they make the cost of bad data far more visible and far more damaging.

That’s why we believe the future of data-focused CDPs is becoming the system of record for trustworthy customer context, and the foundation that both SaaS applications and AI agents can safely build on.