Key Takeaways
- Treasure AI rebuilt its platform to be fully AI-native in 12 months while holding onto 90% of customers by prioritizing governance, reliability and measurable ROI.
- An Agentic Experience Platform replaces the traditional CDP by letting autonomous agents act on first-party data around the clock, cutting insight-to-action time from days to minutes.
For years, customer data was treated as a storage problem. B2B companies poured millions into unifying records, betting that a single source of truth would unlock smarter marketing. That was the promise of the traditional customer data platform. But data sitting in a warehouse doesn’t drive pipeline. Data without context and the ability to act on it has become a liability, not an asset.
The rise of AI-native architecture is changing what marketers expect from their technology. Copilots that assist with manual tasks are giving way to autonomous agents that analyze signals, generate intelligence and execute the next best action on their own. This shift has pushed the industry beyond the CDP toward a new category: the Agentic Experience Platform. An AEP treats customer data as active intelligence, coordinating AI agents, humans and execution in one governed system that runs around the clock.
As Treasure AI celebrates its 15th anniversary, the company’s CPO Rafa Flores explains how his team rebuilt the platform to be fully AI-native in 12 months while holding on to 90% of its customers, why autonomous agents should replace manual workflows rather than simply assist them, and he details how Treasure AI connects a company’s first-party data strategy to measurable pipeline impact.
Demand Gen Report (DGR): Rafa, thanks for taking time to talk with us today. You’ve been in the B2B market for 15 years. How has the definition of ‘customer data’ changed, and how has that shaped the platform Treasure AI is today?
Rafa Flores: Glad to join you. Customer data used to be a storage problem. Companies spent millions just trying to put all their data in one place. That was the era of the traditional CDP. Today, data without context and execution is a liability. The definition has shifted from passive records to active intelligence. We realized that AI requires a persistent, evolving contextual memory, not just a database. This shaped Treasure AI into an Agentic Experience Platform. We moved from simply managing data to providing a governed foundation where autonomous AI agents can actually act upon that data 24/7.
DGR: How does Treasure AI’s approach to AI differ from vendors who bolt AI features onto an existing platform architecture?
Flores: Legacy clouds are optimizing old architecture by bolting on copilots. Copilots only assist humans with manual workflows. We believe AI should replace workflows rather than just assist them. Treasure AI is built natively for autonomous execution. Our agents continuously analyze customer signals, generate intelligence, and take action. Because we have 15 years of experience building trusted enterprise data foundations, our AI operates safely in production environments. It is a unified system of intelligence, rather than a fragmented collection of tools with AI features added as an afterthought.
Why an Agentic Experience Platform Replaces the Traditional CDP
DGR: ‘CDP’ has been a loaded term for years. Why does ‘Agentic Experience Platform’ better describe what Treasure AI has become, and what does it mean in practice for a marketing team?
Flores: CDP is a data management term that solves unification but leaves the execution to humans. Agentic Experience Platform describes a system that coordinates AI agents, humans, and execution. For a marketing team, this means a fundamental shift in how they work. Instead of manually orchestrating campaigns across a fragmented SaaS stack, marketers set the strategy and goals. Treasure AI’s autonomous agents handle the execution. They translate intelligence into cross-channel engagement with continuous feedback loops. It compresses the time from insight to action from days to minutes.
DGR: You rebuilt the platform to be fully AI-native in 12 months while maintaining 90% customer retention. What was the hardest product decision you made during that transformation, and what kept customers on board?
Flores: The hardest decision was burning down the status quo. We could have taken the safe route and just added a chatbot to our existing interface. Instead, we rebuilt the platform to make agents the primary execution layer. We told our customers we were fundamentally changing how they operate. What kept them on board was trust and measurable ROI. We have been a proven enterprise partner for 15 years, not an overnight AI startup. We prioritized governance and reliability from day one. When customers saw they could cut total costs by 40 to 50 percent while achieving 3x faster campaign execution, the transition became an easy choice.
DGR: How does Treasure AI help B2B marketing teams connect their first-party data strategy to measurable pipeline impact?
Flores: Most B2B marketing fails to drive pipeline because customer knowledge is siloed at the moment of decision. Treasure AI solves this by creating a persistent, always up to date understanding of each customer. Our AI agents use this exact context to orchestrate strategy, planning, and execution. When a high value signal occurs, the platform does not just generate a report. It executes the next best action immediately. This unified approach eliminates the latency that kills B2B deals. The result is smarter engagement, stronger retention, and a direct, measurable lift in pipeline conversion.
How Composability and Pre-Built AI Skills Reduce Enterprise Risk
DGR: Composability is increasingly a buzzword in enterprise data, but it means different things to different vendors. What does full composability actually give a Treasure AI customer, and why did Snowflake’s validation matter?
Flores: Composability to us means you do not have to rip and replace your existing infrastructure to get the value of AI. CIOs and CDOs are tired of vendor lock-in. Full composability gives a Treasure AI customer the ability to keep their data securely in their own warehouse while using our platform as the intelligence and execution layer. Snowflake’s validation mattered because it proved our architecture is enterprise ready. It demonstrated that we can deliver governed, production ready AI directly on top of the systems our customers already trust. It reduces architectural risk while dramatically increasing business efficiency.
DGR: How were the 50-plus pre-built AI skills in Treasure AI Studio chosen? Are they based on current trends, customer requests, or something else?
Flores: We looked directly at where enterprise teams waste the most time and money. The skills in Treasure AI Studio were not chosen based on hype. They were driven by our core principle to remove manual work and operational waste. We analyzed the workflows that bottleneck marketers, analysts, and engineers. We found the gaps where insights take days to become campaigns. Every skill we built targets a specific latency or cost center. They are designed to give lean teams the execution power of an agency, allowing them to do more with the team they already have.
How an AEP Lowers Costs and Acts on First-Party Data in Real Time
DGR: For a demand gen leader who’s skeptical about yet another AI-powered product, what’s the single proof point you’d lead with?
Flores: I would lead with our ability to lower total cost of ownership by 40 to 50 percent while lifting conversions by 2.5x. Skepticism around AI is justified because most AI initiatives fail to deliver business value. They fail due to fragmented data and slow workflows, not the models themselves. Treasure AI does not just give you a new tool to manage. It consolidates your fragmented stack and executes the work. When budgets shrink but expectations rise, we give you the power to move at the speed of now, with the definition of “now” being a “you” choice.
DGR: How does an AEP change a marketer’s ability to act on first-party data in real time?
Flores: Traditional marketing requires humans to query data, find segments, build campaigns, and push them to delivery tools. That process creates massive latency. An AEP changes this by introducing continuous, context driven action. The platform maintains an evolving memory of identity, behavior, and consent. When a customer interacts with your brand, an AI agent instantly analyzes that signal against their historical context and triggers a personalized response across any channel. It turns real time data from an analytical exercise into an immediate, autonomous customer experience.
DGR: Where do AEPs go from here, and what does Treasure AI need to build to stay ahead?
Flores: The future of AEPs is full cross-team collaboration where marketers, analysts, and engineers co-create with AI in shared workspaces. We are moving from single task automation to complex, multi agent orchestration across the entire enterprise ecosystem. To stay ahead, Treasure AI must continue advancing our AI Agent Foundry. We need to give enterprises even deeper control to build custom, governed agents that can safely execute highly specific business logic. The goal is to make autonomous marketing the standard, keeping the human firmly in the loop for strategy while AI handles the scale.
