What it really takes to be AI-ready
Adam White
Content Marketing Associate @ Tray.ai
Many AI strategies fail before they start. Not because of the model, but because of outdated integration. See how Yext made integration their AI infrastructure and transformed how work gets done.
For many enterprises, AI readiness still gets treated as an innovation track. A proof-of-concept here. An LLM experiment there. But AI isn’t meant to be bolted on to your tech stack. It changes the architecture beneath the surface. And if your integration infrastructure can’t support that shift, it doesn’t matter how powerful your models are. Your AI strategy will stall.
This is the situation Tulasi Donthireddy, Senior Director of IT and Business Systems at Yext, found himself in. His team had the ideas, the use cases, and the roadmap. But they were stuck on an integration platform that couldn’t keep up.
The reality is, legacy integration platforms were designed for static, point-to-point workflows and not for a world of dynamic AI agents that need to reason, act, and adapt across systems in real time. The hard truth is: if you're still relying on one, you're not AI-ready.
Yext understood that. So they made the switch. Here’s how:
The hidden blocker in your AI roadmap
When enterprises talk about what’s holding back AI adoption, the answers usually sound the same: lack of skills, immature models, unclear ROI. But one of the biggest blockers is hiding in plain sight: integration.
AI agents don’t work in isolation. They need access to clean, complete, and timely data. They need to interact with other systems such as HRIS, CRM, ITSM, and ERP, and they need to do it through well-documented APIs, secure authentication, and reliable orchestration.
If your integration platform can’t provide this, you risk inefficiency and failure. As Alexander Wurm, Principal Analyst at Nucleus Research, said in our recent Yext webinar, “If the integration layer lags, the agent stalls—no matter how smart the model is.”
Yext’s old platform didn’t have centralized monitoring or alerting. Logs had to be routed to external systems just to understand what was happening. Adding new systems required deep developer effort. Even routine troubleshooting slowed them down.
A strategic decision, not just a tooling change
Many IT leaders understand their integration tooling is outdated, but hesitate to make a change. Migration feels risky, expensive, and slow. Teams are (understandably) worried about disruption.
But AI is the ultimate disruptor. The longer enterprises wait, the more painful the cost becomes.
Legacy integration platforms weren’t designed to support agent-based architectures, composable automation, or the scale of event-driven AI services. They force tradeoffs between control and speed, visibility and flexibility, centralization and agility.
Yext didn’t wait. They used the move off their legacy platform as an opportunity to not only reduce cost and complexity, but to rethink how integration work gets done, and who gets to do it.
What modern integration looks like
Yext replaced multiple fragmented tools with Tray’s unified, AI-ready iPaaS. The results were immediate:
100+ integrations migrated in 3 months
60% cost savings
Build cycles reduced from 3 days to 1
But the more important shift came in who was building. With reusable templates, built-in monitoring, and composable workflows, business users could start automating tasks themselves. That freed up developers to focus on high-value work, like building AI agents.
“We’re rolling out IT agents that can handle access provisioning, error troubleshooting, even Slack-based workflows,” Donthireddy said. “It’s already changing how we think about automation.”
Integration is now AI infrastructure
The message from Yext’s story is clear: You can’t layer AI onto outdated systems and expect results. Your integration platform is your AI infrastructure. It’s the backbone your agents rely on to take action across your stack.
Yext didn’t wait for a failed pilot or an AI stall-out to make the switch. They recognized that integration had become a foundational technology. One that impacts cost, speed, and long-term adaptability.
So if you’re serious about AI, you have to ask: Is your integration platform future-proof…or is it holding you back?