The Evolving Vision of Customer Data
In my previous article, “The Death of Traditional CDPs: Why AI Agents Are the Future”, I explored how static, batch-driven customer profiles are no longer sufficient for today’s dynamic customer journeys. I argued that specialized AI agents—each trained to learn, infer, and act continuously—would transform how enterprises engage with customers.
This vision of AI agents working collaboratively remains our North Star. But as we’ve partnered with leading enterprises, we’ve uncovered a critical insight: AI agents require more than just data access—they need relationship intelligence to truly transform customer experiences.
The Composable Revolution: A Foundation for Transformation
The composable CDP movement has transformed enterprise thinking about customer data management—and with good reason. By leveraging existing data warehouse investments rather than creating another data silo, composable CDPs align perfectly with modern data architectures.
Marketing leaders at a leading global grocery retailer with complex omnichannel operations spanning physical stores and online shopping have embraced this vision enthusiastically. Their digital executive explained: “Moving beyond monolithic CDPs that created another data silo just makes sense. By leveraging our existing data warehouse investments, we’ve created a more flexible foundation for our customer data strategy.”
Composable CDPs deliver real advantages:
- Elimination of redundant data storage
- Greater flexibility through modular tools
- Better alignment with enterprise data strategies
- Reduced vendor lock-in
This approach is undoubtedly the right direction—but as we advance toward the agentic future I described in my previous article, we need to enrich our understanding of what true composability means.
From Composable to Relationship-Driven
The challenge isn’t with the composable concept—it’s with how we define composability itself.
True composability isn’t just about where data lives or how systems connect. It’s about creating an architecture optimized for the unique demands of customer experience:
- Real-time processing at scale
- Unified identity across touchpoints
- Contextual understanding of behaviors
- Efficient activation without performance penalties
“We fully committed to the composable approach,” the grocery retailer’s digital leader explained, “but realized we needed specialized components designed specifically for customer experience use cases. Our warehouses excel at analytics, but providing real-time, relationship-driven customer experiences required more thoughtful architecture.”
Relationship Intelligence: The Missing Link
Consider Sarah, a loyal customer who browses meal kits on her phone, abandons her cart, then returns later via an email link on her laptop to complete a purchase.
A truly composable architecture needs to see this as one connected journey, not three disconnected events. This requires a specialized layer that understands relationships:
- Between identities (connecting Sarah’s phone, laptop, loyalty account)
- Between behaviors (understanding browse-to-abandon-to-purchase patterns)
- Between products (recognizing meal kit preferences and ingredients)
- Between touchpoints (mapping the customer journey across channels)
As the retailer’s technology leader noted: “The warehouse has all the data points, but we needed to add a purpose-built relationship layer to create the storyline that gives meaning to the raw data.”
The Bridge to the AI-Agent Future
This is where my vision for AI agents and the evolution of composable CDPs converge. As I wrote in “The Death of Traditional CDPs,” the future demands specialized AI agents working collaboratively—each solving different aspects of the customer experience puzzle.
But these agents can only function effectively with relationship intelligence. Without it, they become isolated specialists lacking the holistic view needed for truly transformative experiences.
The grocery retailer enhanced their composable strategy with a specialized relationship intelligence layer that serves as the foundation for their AI agent ecosystem:
- Their warehouse remained the trusted data foundation
- The relationship layer provided real-time identity resolution and contextual intelligence
- AI agents leveraged this relationship-rich environment to deliver coordinated experiences
“This enhanced architecture was the missing link between our composable data foundation and the AI-driven future we were working toward,” explained their CTO. “It gave our AI agents the relationship context they needed to collaborate effectively.”
The Five Pillars in Action
In my previous article, I outlined the five pillars of AI-driven marketing. With an enhanced, relationship-driven composable foundation, these pillars become even more powerful:
- From Monolithic AI to Agent Ecosystems The specialized agents I described—audience, engagement, and analytics agents—now operate with shared relationship intelligence, creating truly cohesive experiences.
- Human Strategy, AI Execution Marketers set the strategy while AI handles execution—but now with relationship intelligence enabling more nuanced understanding of customer context.
- Build Your Own AI Marketing Brain The composable approach extends to AI capabilities, allowing organizations to assemble specialized agents for their unique needs—all sharing a common relationship foundation.
- Privacy-First Personalization Relationship intelligence allows for deeper personalization with less data, preserving privacy while delivering relevance.
- The Evolution from Co-Pilot to Auto-Pilot With relationship intelligence, AI agents can progress more confidently from recommending actions to autonomous execution.
Technical Foundation: Knowledge Graphs and Multi-Agent Frameworks
For the technically inclined, this relationship-driven approach enhances composable architecture with specialized technologies:
- Knowledge graph structures that efficiently store and traverse relationships
- Real-time identity resolution services that maintain unified customer profiles
- Multi-agent frameworks that enable collaborative intelligence
- Domain-specific contextual models that encode industry expertise
These technologies complement traditional data warehouses, adding capabilities specifically designed for customer experience use cases.
The Path Forward: Enhanced Composability
The composable CDP approach is indeed the foundation of the future—but success requires thoughtful implementation that goes beyond simple warehouse connections and prepares for the AI agent revolution.
For organizations implementing composable customer data strategies:
- Embrace composability as your foundation, leveraging warehouse investments
- Enhance your architecture with specialized components for relationship intelligence
- Design for the real-time, contextual requirements of customer experience
- Begin experimenting with domain-specific AI agents that leverage relationship context
Conclusion: The Connected, Collaborative Future
The future of customer data isn’t just composable—it’s connected, contextual, and collaborative. It brings together the best of composable architecture with the relationship intelligence needed to power truly transformative AI agents.
As our leading grocery retailer discovered, true composability isn’t just about connecting systems—it’s about creating an architecture that understands the relationships that make customer experiences meaningful and enables the AI-driven future I outlined in my previous article.
By enhancing your composable foundation with relationship intelligence today, you’re building the bridge to the multi-agent AI revolution that will transform customer experiences over the next 18 months.
In this future, your competitive advantage won’t come from having the most data or the most connections—it will come from having the deepest understanding of the relationships that drive customer behavior and enable AI agents to collaborate intelligently across your enterprise.
