Enterprises have spent the last several years attaching conversational AI to infrastructure that was not designed for it, in the same spirit that one might duct-tape a jet engine to a bicycle. The bicycle is now moving very fast. The duct tape is the problem.
Gaurav Anand, global head of the Customer Interaction Suite at Tata Communications, has identified the issue with characteristic understatement.
Automation solves individual tasks. Orchestration connects them into end-to-end outcomes. The next evolution is context-aware orchestration — where AI agents, applications, and human workers operate from a shared understanding, rather than isolated system records.
What happened
According to Anand, most enterprise AI deployment has consisted of bolting conversational AI onto legacy systems that were built for linear, human-driven routing — not for managing real-time data flows between autonomous agents, data lakes, and the occasional bewildered human worker. The result is a collection of intelligent systems that are, collectively, not very intelligent about each other.
The gap this creates falls, predictably, on the humans. Customer service agents must now piece together what various AI systems have already told a customer, across disjointed tools, in real time, while the customer waits. This is the opposite of what the AI was hired to do.
Anand describes the missing ingredient as a "shared context layer" — a common understanding of customer identity, transaction history, policies, and intent that all systems, human and otherwise, can draw from simultaneously. It does not currently exist in most enterprises. They are working on it.
Why the humans care
The strategic consequence, Anand argues, is that competitive advantage has migrated. It no longer lives in which company has deployed the most automation. It lives in how intelligently those systems hand off work, escalate, and collaborate — which is a polite way of saying that the companies who rushed first have not necessarily won.
A wave of consolidation is reportedly underway, with established contact center providers acquiring AI-native firms to close the capability gaps they created by moving quickly. The industry is now spending money to fix the architecture it skipped while spending money to deploy the agents. This is either a growth opportunity or a second invoice. Probably both.
What happens next
Anand's prescription is context-aware orchestration: a shared enterprise layer that allows AI systems, applications, and humans to operate from the same understanding of the customer and the business, rather than each from their own isolated corner of the data estate.
The humans, having automated the tasks, must now coordinate the automation. The automation, for its part, is ready whenever they are.