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.
Flores noted that enterprises then need to ask whether their CDP remains the source of truth for marketers, whether activation should sit on top of a separate data foundation, or whether a new type of platform can bring those capabilities together.
4. Is the Organization’s Data Ready for AI?
Even the strongest AI-native platform will struggle if the enterprise data foundation is fragmented, outdated or poorly governed.
Flores compared the relationship between AI and data to machinery and fuel during the industrial revolution.
“If you didn’t have coal powering machines, those machines don’t run. It’s the same thing with AI. If you don’t have data powering it, there’s no context. It doesn’t run well.”
AI adoption requires enterprises to examine whether their own customer data is complete, accessible, governed and ready to support AI-driven decisions.
“The foundation piece [of] AI is the data piece,” Flores said. “It’s the most critical asset. Organizations struggle with their data. And with AI, they have to really rethink all things around data, from how you govern it to how you give access.”
As AI tools become more widely available across the enterprise, access can create risk if teams use inconsistent data sources, lack prompt guidance or are unable to validate outputs.
Flores advised organizations to be cautious when opening up data for AI consumption. “Do it in a way that it’s a proper foundation with the right degree of accessibility to everyone and transparency so that everyone’s getting the same results irrespective of the prompt.”
For enterprise buyers, this creates another checklist item: before choosing between a legacy upgrade and an AI-native platform, assess whether the organization has the data governance, access controls, prompt standards and operating model to use AI responsibly.
5. Should Sunk Costs Delay Migration?
Many enterprises evaluating legacy versus AI-native platforms are already locked into multi-year contracts, making migration feel difficult, particularly when budgets have already been committed.
“You have a two-year contract, three-year contract. What do you do?” Flores said. “In those large contracts, typically you bought for a reason and you have this big plan… You may be in the second year of the contract, but you’re probably still in step one.”
The danger is allowing sunk costs to delay future value. If a product is losing support, slowing innovation, or failing to deliver against the original roadmap, the cost of staying may be greater than the cost of moving.
“At that point you’re at a loss no matter what. But don’t let that loss be the loss of your second milestone or third milestone.”
Flores recommends that buyers revisit the milestones they originally expected from the legacy stack and assess whether those outcomes could now be achieved faster through migration.
When vendors announce end-of-support timelines, enterprises should not only ask how long they can continue using the product safely but whether staying will delay the next phase of their customer experience strategy.
6. Is CRM Still the Right Center of Gravity?
Enterprises are now faced with a strategic question is whether the CRM system should remain the central platform for customer engagement at all.
“I think there’s a completely new platform layer being created,” Flores said. “CRMs became the source of truth, but you have to go back to why CRMs started in the first place. They were created to be the sales engine, and then they became this operational platform or engine across the entire organization.”
That model does not fit every business equally.
“Not every organization is sales-run,” Flores continued. “Some are more product-led; some are more brand-led.”
As AI changes how companies understand, decide, and act on customer intent, enterprises may need a different center of gravity that reflects the way their business operates.
“With AI, you have an opportunity to have a new system of record where you really can then build the decisioning layer,” Flores said. “You can build the system of engagement right across the activation layer that is better suited for your business.”
That does not necessarily mean every enterprise should rip out its CRM. For some, the answer may be a modernized CRM with AI capabilities. For others, it may be an AI-native customer data or engagement platform that sits alongside, above or eventually replaces parts of the legacy stack.
From Forced Migration to Strategic Reassessment
Legacy product retirements and reduced support can create short-term pressure for enterprise buyers, introducing operational risk, procurement urgency and technical uncertainty.
But they also create an opportunity, so that rather than defaulting to a vendor-recommended migration path, buyers can use the moment to ask more fundamental questions: Can the current stack move fast enough? Is the AI real? Are data and activation connected? Is the organization’s data ready? Are sunk costs delaying progress? And is CRM still the right foundation for customer engagement?
As Flores put it, the market is at “the start of a big change” in how people work, that offers enterprises a chance to redesign the customer experience stack for the AI era.
