Orchestration Becomes Critical as Enterprises Deploy AI Agents Across Channels

Enterprises are struggling to manage the complexity of integrating conversational AI with legacy systems, creating new challenges for customer experience (CX) orchestration. According to Gaurav Anand, global head of the Customer Interaction Suite at Tata Communications, most AI deployments have involved “bolting” these technologies onto existing infrastructure that wasn’t designed for them.

This approach creates fragmented customer journeys where human agents must navigate multiple disconnected tools to understand previous interactions—adding cognitive load and friction. The issue isn’t just data access but the lack of a shared enterprise context connecting identities, conversations, transactions, policies, and operational systems.

From Automation to Orchestration

As AI adoption accelerates, organizations are shifting focus from individual task automation to end-to-end process orchestration—managing how different systems work together. Anand notes that the competitive advantage now lies in intelligent system handoffs rather than simply deploying more bots.

Companies that treat AI as a replacement for human interaction without proper integration risk recreating the rigid menu structures they sought to avoid. The true value of AI emerges when it enables seamless coordination across channels and touchpoints.

Building an Enterprise Context Layer

Tata Communications’ solution is the Interaction Fabric—an orchestration layer that unifies contact centers, messaging platforms, AI systems, and customer data into a single view. This creates a “context-driven architecture” where interactions retain continuity regardless of channel (voice, chat, email, etc.).

Key components include:

• Identity synchronization: Maintaining consistent customer profiles across applications
• Intent recognition: Understanding the underlying purpose of each interaction
• Context graphs: Connecting data points to create a holistic view of customers and their journeys

By creating this shared understanding, organizations can enable AI agents and human workers to operate from the same foundation—driving more accurate decisions, personalized experiences, and efficient resolutions.