As legacy vendors retire, consolidate, or reduce support for older products supporting customer interactions, enterprise buyers are being forced into decisions they may not have planned for.
But according to Rafael Flores, Chief Product and Growth Officer at Treasure AI, this should not be treated as a simple replacement cycle.
Instead of asking which platform most closely replicates the outgoing system, enterprises should take the opportunity to reassess whether their existing stack can still support the speed, governance and intelligence now required in customer experience.
For many organizations, the question is whether their customer relationship management (CRM) or customer data platform (CDP) can keep pace with AI-driven customer engagement, governed data activation and real-time decision-making.
“The reality is that the world has changed in how we work,” Flores told CX Today. “And so what that means [is] that there are limitations in the systems you’ve used in the past.”
As long-standing enterprise platforms evolve, sunset products, push customers toward newer offerings or reduce support for older tools, the risk for buyers is being locked into a technology model built for a different operating environment.
So what should enterprises consider as they weigh legacy platforms against AI-native alternatives?
1. Is the Legacy Platform Still Moving at the Speed the Business Needs?
For Flores, one of the biggest limitations of traditional CRM and CDP stacks is speed that reflects how businesses now need to operate.
“CRMs have historically been in a place where they’re 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 becomes a problem when customer engagement teams are expected to launch campaigns faster and respond to changing behavior in near real time.
Flores argued that legacy platforms may be governed, but they are often not governed in a way that supports “the speed of now,” which can slow teams down.
“It’s never fun to get a system and those legacy systems where you have to go into a community page or you have to reach out to support [and it takes] three days to get a response,” Flores said. “By the time three days have gone by, somebody else has already rolled out a new campaign, in the matter of minutes. So how can you compete with that if you stay in this old-era stack?”
For enterprise buyers, this creates a critical evaluation point. If a vendor is retiring support for a product, moving to the nearest successor may not solve the underlying issue. The question is whether the replacement will allow teams to operate at the pace customers and competitors now expect.
2. Is the Vendor Offering Real AI, or Just AI Branding?
The rise of AI has added another layer of complexity to vendor evaluation. Nearly every enterprise software provider now claims to offer AI capabilities, but Flores warned buyers to distinguish between AI-native products and AI messaging.
“Everyone has AI. Is it credible AI or is it not?”
Flores’ test for credibility is simple: can the buyer use the AI capability immediately in a real product environment?
“If... you are a vendor and you tell me, 'I have the best-in-class AI product,' my question is going to be, 'okay, can you open it up and let me actually use it?'”
Often, the answer is not a working product.
“The answer is, let me show you a mock of it… so you actually don’t have an AI product. You have an AI website.”
For buyers evaluating a migration from a legacy system, a platform with AI features added to the roadmap may not offer the same value as one built around AI-driven workflows, automation, decisioning and product development from the start.
Flores also argued that AI-native should describe how the vendor builds as much as what the product does.
That should prompt buyers to ask vendors harder questions: Can they demonstrate live AI use cases? How quickly do they ship customer-requested capabilities? Are AI capabilities embedded in the workflow, or presented as a chatbot, mock-up, or thin interface on top of another model?
3. Can the Platform Connect Data Foundation and Activation?
Legacy CRM, CDP, marketing automation and customer engagement environments have often been assembled through multiple specialized tools. As enterprises pursue AI-driven personalization, such fragmentation becomes harder to sustain.
Flores said buyers should begin with the business outcome they are trying to drive, rather than simply following market consolidation trends.
“Don’t just do what everyone else is doing. Just because somebody is consolidating, it doesn’t mean you have to consolidate. However, you have to look at it. You have to be aware of market trends. But it boils down to what are the business results you’re trying to drive.”
For example, if the goal is cross-channel personalization, the connection between data and activation becomes essential.
“If you are trying to go and drive cross-channel personalization at scale that’s governed, well, you probably want your data foundation and your activation layer to be tied together,” Flores said.
When buyers are forced to reassess legacy products, a discontinued platform might expose a deeper architectural issue, with customer data, decisioning and engagement spread across too many systems to support AI effectively.
