Share with your CMO
Tata Communications is betting that the next CX battleground isn’t which AI model you deploy, it’s whether your architecture can coordinate all the AI you’ve already bought. Gaurav Anand, global head of the company’s Customer Interaction Suite, frames the core problem as context collapse: enterprises have layered voice AI, chatbots, and automation onto legacy contact center infrastructure that was never designed to share state across channels. The company’s answer is a product called Interaction Fabric, an orchestration layer meant to unify customer identity, conversation history, and enterprise data in real time.
What this means for your business
The problem Anand describes is real and widespread, but the diagnosis lands differently depending on where you are in the deployment curve. If your CX stack still runs on a single contact center platform with bolt-on AI, the context collapse risk is mostly ahead of you. If you’ve spent two years saying yes to every AI pilot, you’re probably already living it: human agents toggling between five interfaces, AI systems that lose the plot the moment a customer switches from chat to voice, and customer satisfaction scores that don’t reflect the AI investment on the books.
The argument that orchestration has overtaken automation as the strategic priority is correct, and the reason is structural. Automation optimizes a task in isolation. When you chain enough isolated automations together without a shared context layer, an enterprise-wide model of who the customer is and what they’ve already done, you get efficiency theater. Faster wrong answers. The fraud example Anand cites is the sharpest illustration: an AI can block a card in milliseconds, but if it can’t hand the emotional state of a panicked customer to a human agent with full context intact, you’ve automated the easy part and abandoned the customer at the moment of highest stakes.
The piece is sponsored content from Tata Communications, which sells directly into the architecture gap it’s describing, so the prescription unsurprisingly points to a single integrated platform rather than, say, a best-of-breed integration strategy. That tilt is worth naming because it forecloses a real alternative: many enterprises will assemble orchestration capabilities through API-first vendors like Salesforce, Genesys, or Five9 rather than replacing the stack wholesale. The “enterprise ontology” framing, a shared business vocabulary connecting customer data, policies, and workflows across platforms, is genuinely useful thinking regardless of vendor, but Anand’s version assumes you centralize rather than federate.
The decision this reframes isn’t whether to invest in AI orchestration. That call is effectively made for any enterprise that’s been deploying agents since 2023. The real question sitting on your plate is whether your current contact center contract renewal is a consolidation opportunity or a trap. Signing another multi-year deal with a platform that can’t expose a shared context layer to external AI systems means you’re locking in the fragmentation Anand describes, and paying for the privilege.
Concept deep-dive: Enterprise Ontology
An enterprise ontology is a shared vocabulary that defines how a business’s key concepts, customer, product, policy, transaction, relate to each other in a way every system can read. Think of it as the Rosetta Stone sitting between your CRM, your contact center, and your AI agents, so when a customer says “my recent order,” every system agrees on what that phrase means. Without it, AI agents answer accurately within their own silo and incorrectly across the customer’s actual journey.
Based on reporting from Orchestration is the new challenge for CX in the age of AI agents, originally published 2026-08-26 10:30:00.
