Every customer service call now produces a measurable outcome, which is exactly why customer experience has become the place where agentic AI either proves itself or falls apart. Most enterprise AI hype centres on generation and productivity. But according to Sneha Iyer, who leads AI value delivery and analytics at Observe.ai, the real test of agentic AI is happening somewhere less flashy, which is the contact centre. In a recent episode of Tech Transformed, host Ravit Jain asked Iyer why customer experience (CX) has become the industry's default stress test and what that means for enterprises still sorting isolated chatbots from genuine AI agents. Iyer has spent nearly a decade watching CX change from manual quality assurance to a blended workforce of human and AI agents. This shift, she says, has quietly rewritten the metrics, tooling, and governance decisions every enterprise leader now has to make.

Why Customer Experience Became AI's Testing Ground

Iyer explains that CX generates more human-AI interaction data than almost any other business function. Every conversation resolves in a clear outcome, resolved or not, retained or churned, and it spans every modality a company touches, from voice to chat to email. Solve AI reliability here, and the lessons transfer everywhere else.

What's actually changed, she notes, isn't that AI got smarter; it's that AI stopped just watching and started acting. Where AI agents once flagged patterns in a transcript after the fact, they now issue refunds, verify identities, and book appointments in real time. This has split the workforce in two, namely, AI agents absorb the high-volume, low-risk interactions, while human agents get pushed toward the complex, judgment-heavy calls machines shouldn't handle alone. Old metrics like containment and deflection no longer capture that split; leaders now need blended resolution measures that account for both.

Fragmented AI Stacks

If you ask most contact centres what their AI stack looks like, you'll find what Iyer calls a "Frankenstein stack": one tool for analytics, another for telephony, another for conversational intelligence, stitched together and barely talking to each other. That setup was tolerable when each tool did one isolated job. It breaks the moment you add agentic AI, because these types of systems depend on shared context to function.

Without that context, a customer verified by an AI agent has to repeat everything to the human agent who picks up next, which turns an already frustrating support call into a worse one. Fragmented tools also make it nearly impossible to prove ROI, since every vendor tracks success differently and costs stack with each new integration.

This is the case for unified CX platforms: not as a nice-to-have, but as the only way to preserve context across a customer's full journey. Iyer points to "companion agents" as a concrete example of real-time, in-context support for human agents that shows them exactly why an AI agent escalated a call and what happened before they picked up.

Build vs. Buy

On the build-versus-buy question, Iyer highlights that building in-house looks cheap upfront and rarely stays that way. Models change every few weeks, which turns any custom build into a permanent maintenance commitment most teams underestimate, including in regulated industries like healthcare and finance, where leaders often assume building keeps data safer. In practice, established vendors have already solved the compliance groundwork, data residency, model governance, and audit trails that a custom build would take months to replicate.

Trust, she argues, isn't really about doubting AI's competence. It's the fear of the one catastrophic interaction happening at scale. That's what simulation testing and evaluation frameworks are for, stress-testing AI agents against the hardest, most ambiguous scenarios before launch, not just the easy paths, backed by governance frameworks that can roll back a deployment fast if something breaks.

Looking six to twelve months out, Iyer expects three shifts, continued consolidation around unified platforms, evaluation frameworks becoming the real competitive edge (not raw model performance), and pricing models moving from seat-based to outcome-based. Her advice to leaders is to build auditability and governance into your AI strategy now, before regulation forces the issue.

Building Customer Experience

The throughline of Iyer's conversation with Jain is that agentic AI in customer experience succeeds or fails on infrastructure and trust, not on which model you're running. Enterprises that unify their data, rethink their success metrics, and choose vendors who can prove reliability through thorough testing are the ones positioned to scale AI-driven CX without breaking it. If you would like to find out more about this visit observe.ai or connect with Sneha Iyer on LinkedIn.

Takeaways

Shift from isolated AI tools to integrated agent systems.
Importance of simulation testing and evaluation frameworks.
Benefits of unified customer experience platforms.
Build versus buy decision in enterprise AI.
Future trends in AI-powered customer experience.

Chapters

00:00 Introduction to Tech Transform and AI Evolution

02:01 Sneha Iyer's Role and Background in AI

09:35 The Shift in Customer Experience with Agentic AI

13:24 The Move Towards Unified Customer Experience Platforms

18:55 Build vs. Buy: Navigating AI Solutions in Enterprises

20:59 The Cost of AI Maintenance and Vendor Insights

22:20 Navigating Compliance in Regulated Industries

24:52 Building Trust in AI Adoption

30:00 Use Cases Driving AI Value

34:31 Future Trends in AI Customer Experience