As AI becomes more central to customer experience, enterprises face a difficult question: are the legacy CRM systems they rely on still built for the pace of modern AI-native engagement?
In this CX Today interview, Nicole Willing speaks with Rafael Flores, Chief Product and Growth Officer at Treasure AI (formerly Treasure Data), about why legacy customer relationship management (CRM) stacks may be reaching their limits.
Flores argues that the challenge is not a lack of software; for many enterprises, it is the opposite. Businesses have invested heavily in complex platforms that promise breadth, but those same systems can become difficult to adapt when teams need speed, flexibility and practical AI support.
“CRM systems have historically been what I call very stoic systems. They’re very rigid. There’s a lot that comes with them. You only use about 10 percent of it typically, but you pay for 100 percent of it.”
That rigidity matters when CX and marketing teams are under pressure to deliver more personalized, timely and governed engagement. Flores gives a practical example: a campaign is ready to launch, but a broken data pipeline creates a delay. In a legacy environment, the marketer may need to wait for IT support, identify the issue, secure missing creative assets, and finally deploy the campaign days or weeks later. In an AI-native environment, he suggests, agents could help identify the broken pipeline, recommend a fix, and support the creation of on-brand assets much faster.
This is where the definition of “AI-native” becomes important. Flores warns that buyers should look beyond surface-level claims and ask whether a vendor’s AI is something they can actually use today. A chatbot, mock-up, or simple AI interface does not automatically make a platform AI-native. The technology must be embedded into how the system works, how users get value and how the vendor builds and improves the product.
The conversation also tackles the data challenge behind enterprise AI. Flores compares data to coal powering industrial machinery, as without the right foundation, AI lacks the context it needs to work properly. That means enterprises need strong governance, clear access rules and prompt guidance to avoid inconsistent answers and rising token costs.
Watch the full interview to hear why Treasure AI believes a new platform layer is emerging for customer data, decisioning and engagement.
