The AI arms race in customer experience—and the gap no one else is solving

Adobe, Databricks, and a turning point for the industry

Opinion

by Bob Hale | CEO, Alterian

April 30, 2026

The industry is getting faster, but is it getting smarter?

Two announcements landed last week that are worth reading together:

At Adobe Summit 2026, Adobe unveiled its CX Enterprise CoWorker, a significant move toward agentic AI that brings orchestration, automation, and decisioning closer together across the enterprise.

Databricks published its vision for Data Intelligence for Marketing—a unified data environment where AI models trained on governed, enterprise-wide data can finally move from insight to action without friction.

Both announcements represent genuine progress. And together, they reveal something important about where our industry is—and where it still needs to go.

These announcements don’t stand alone. Across digital marketing and customer experience, the same pattern is emerging.

• Salesforce is embedding AI copilots across CRM and marketing workflows, expanding their Data Cloud.

• Microsoft is integrating Copilot across enterprise data and productivity layers.

• At the same time, Google is reshaping how customers discover brands entirely, with AI-generated search experiences that reduce reliance on traditional click-based journeys.

The direction is clear. AI is becoming the interface to digital marketing and customer experience, and data is the foundation it depends on.

The AI advance is real

Adobe is solving a problem that has frustrated marketers and CX leaders for years: the gap between insight and action.

Agentic AI that can orchestrate, automate, and make decisions across connected systems—bringing intelligence into execution at enterprise scale—is not incremental. It’s a structural shift in what’s possible.

Like many platforms, Adobe’s approach is built around unifying and activating first-party data. This makes it more powerful, but still inherently limited to what brands can already see.

Databricks is solving a different but equally fundamental problem: fragmentation. A unified data environment where AI models are trained on governed, complete, enterprise-wide data—and where the entire stack moves from analysis to activation without friction—is exactly the foundation the industry has needed.

This reflects a growing belief across digital marketing: that better infrastructure and unified data will lead to better customer understanding. But even the most advanced data platforms can only analyze the data that exists within the organization.

These aren’t competing visions. They’re complementary ones. And the fact that two of the industry’s most significant players are moving in this direction in the same week tells you something about the pace of change we’re entering.

But both share the same blind spot

Here’s what neither announcement addresses.

Every system in our industry, however sophisticated, is ultimately constrained by one thing: it can only work with the data it has access to.

Adobe’s CoWorker is a powerful engine for executing on connected data. Databricks’ unified environment is exceptional at activating signals from within your ecosystem.

But the majority of your customers’ journey doesn’t originate in your ecosystem.

It happens before the click. Before the visit. Before the record exists. In competitor ecosystems. In independent research. In conversations, comparisons, and moments of influence that never become structured data in your platform.

So while we’re getting dramatically faster and smarter at acting on known data, we’re still largely blind to where decisions are actually made.

AI is changing the journey itself

Customers are exploring more broadly, comparing more dynamically, and forming preferences earlier than ever before. At the same time, discovery itself is shifting.

With AI-driven experiences from platforms like Google, fewer journeys engage with brand websites at all, meaning less of the decision-making process is ever captured in traditional analytics or marketing systems.

If your AI strategy is confined to your existing data footprint—however unified, however well-activated—it will always be reacting to a decision that’s already been shaped elsewhere.

The shift that still needs to happen

The industry has made enormous progress on three things:

1. Connecting data

2. Accelerating execution

3. Closing the gap between insight and action

These are the right problems to solves, but there’s a fourth shift that hasn’t happened yet—and it’s the one that changes the competitive equation:

From optimizing what we know → to understanding what we don’t.

This isn’t about having more data. It’s about having a fundamentally broader view of reality; one that includes the 70%+ of the customer journey that never enters your stack.

That requires a different kind of intelligence. Not an analytics tool. Not a dashboard. Not another layer of connected data.

Something that observes actual customer behavior across the entire digital ecosystem—your brand, your competitors’, and other influences—and surfaces the dynamics that are shaping decisions before they become visible in any platform.

What journey intelligence adds

At Alterian, this is what we’ve spent years building. Alterian Journey Insight is not a platform to replace what Adobe or Databricks offers. It’s the intelligence layer that makes both more powerful.

It’s built on a proprietary dataset of billions of real digital signals across millions of consumers—structured around journeys, tuned to 300+ vertical markets, and observed rather than surveyed or inferred. At that scale, statistical validity isn’t a question. You’re not sampling behavior. You’re seeing it.

And because it’s designed to live inside existing ecosystems, integrated at the agent-to-agent level via MCP, it changes how the rest of the stack operates. Adobe’s CoWorker doesn’t just act faster. It acts with a fundamentally broader understanding of what’s actually happening in the market. Databricks’ models aren’t trained in isolation anymore. They’re grounded in how customers actually make decisions in the real world.

The effect is a multiplier, not an addition:

• Segmentation becomes more meaningful, built on real behavior, not just observed transactions.

• Predictions become more accurate, because the model includes the influences that actually drive decisions.

• Personalization becomes more relevant, because you understand the journey, not just the touchpoint.

• Strategy becomes aligned with reality, not just with the data you can capture.

And every insight connects directly to the outcomes that actually matter to the business: acquiring customers, improving retention and lifetime value, or reducing costs. Not intelligence for its own sake — intelligence tied to a commercial outcome.

Platforms across the ecosystem—including Snowflake, HubSpot, SAP, and Twilio—are advancing this broader vision of AI-powered marketing and CX. But they all operate within the same constraint: the data organizations already own. Journey intelligence extends that boundary.

The real competitive question

Adobe and Databricks are helping organizations execute better on what they know. That’s valuable. The organizations that move fastest to adopt these capabilities will have a real advantage.

But the deeper competitive question isn’t how quickly you act on what you know. It’s how well you understand what you don’t—and how effectively you act on it.

Because the customers you’re losing to competitors didn’t disappear from your data. They were never in it. Their decision was made somewhere you couldn’t see, shaped by dynamics you weren’t tracking, in a journey your AI never had access to.

The organizations that win the next era of AI in digital marketing and customer experience won’t just be the ones with the fastest execution or the most unified data. They’ll be the ones who see further.

If you’re only optimizing the fraction you can see, now is the time to ask a bigger question:

What critical moments in the journey are you missing?

See what’s really happening beyond your visible channels.