Provided by Tata Communications
Businesses are adopting AI agents, voice AI, and intelligent automation across messaging, voice, and digital channels faster than their existing technology architectures can support them. According to Gaurav Anand, global head of customer interaction suite at Tata Communications, one of the biggest implementation challenges is connecting conversational AI to legacy systems that were never designed for intelligent automation.
“In the rush to adopt AI, organizations are primarily bolting conversational AI onto legacy systems,” says Anand. “As a result, many companies are adopting digital tools, but few have a platform that is truly integrated, scalable, and seamlessly orchestrated.”
This technology gap increases the cognitive load on human customer service agents. Employees often need to assemble customer context from multiple disconnected applications to determine what an AI system has already communicated. The challenge extends beyond data access. Organizations need a shared enterprise context that connects customer identities, interactions, transactions, policies, journeys, and operational systems.
Traditional customer experience architectures were built around linear, human-led routing. They were not designed to manage real-time data flows between autonomous AI agents, data platforms, enterprise applications, and human employees.
“Operational complexity today is no longer about adding intelligence,” Anand says. “It is about aligning existing intelligence across the enterprise so customers never experience the friction created by internal silos. This requires a shared context layer that enables AI systems, applications, and people to operate from the same understanding of the customer and the business.”
Why AI orchestration is replacing automation as a CX priority
As coordination challenges increase, many businesses are shifting their focus from standalone automation to end-to-end AI orchestration, Anand says.
“Automation solves individual tasks, whereas orchestration connects tasks to end-to-end outcomes,” he explains. “The next evolution is context-aware orchestration, where AI agents, applications, and employees work from a common understanding of customers, processes, and business intent instead of isolated system records.”
As organizations deploy more chatbots, voice AI agents, and intelligent automation tools, managing these technologies becomes increasingly complex. Competitive advantage now depends less on simply deploying AI and more on how effectively systems collaborate, transfer work, and escalate issues to the right human expert.
The risks of adding AI to legacy customer service systems
Businesses that place voice AI agents in front of disconnected legacy systems risk recreating the same rigid phone menus that AI was intended to replace. The real value of AI lies in its ability to deliver speed, scale, personalization, and intelligent orchestration across the customer journey.
Anand points to industry consolidation, including contact center providers acquiring AI-native companies to strengthen their capabilities and improve customer experience delivery. These developments reflect a broader industry recognition that businesses need more than communication channels and task-based automation.
They also need an intelligence layer capable of coordinating AI agents, human employees, enterprise data, and business workflows across the organization. The long-term goal is to make AI the connective layer between customers, employees, and enterprise systems.
Achieving this requires a common enterprise ontology: a shared business vocabulary that links customer information, products, policies, standard operating procedures, transactions, and workflows across multiple platforms.
Tata Communications addresses this need with Interaction Fabric, an orchestration layer designed to unify contact center operations, messaging, collaboration, AI, and customer data. The platform coordinates AI agents, communication channels, and enterprise systems in real time.
Its context-driven architecture continuously connects identity, conversation history, transactions, and operational data to maintain continuity across customer interactions and channels.
As a result, AI systems and human agents can move between voice, WhatsApp, chat, email, and CRM workflows without losing important customer context. Customer identity, intent, and AI-generated insights can move continuously across channels instead of remaining isolated within separate applications.
The next phase of AI orchestration will involve coordinating work through a shared understanding of the enterprise, not simply connecting different systems. Context graphs built on enterprise ontologies can link customers, interactions, products, policies, decisions, and outcomes across organizational silos.
This shared context enables AI agents and human employees to make more accurate decisions, complete smoother handoffs, and deliver more consistent customer experiences. However, synchronizing customer intent, conversation history, business data, and AI decisions across channels requires an agile network infrastructure.
Legacy networks that are not designed for modern data volumes and interaction speeds can create what Anand describes as “data gravity,” causing delays and inconsistent information as customers move between channels.
“The underlying network must be designed to be as agile as the AI systems running on top of it,” Anand says. “When interactions remain synchronized, the technology becomes invisible and the customer experience becomes the focus.”
How AI can improve the human agent experience
Successful collaboration between AI and human customer service agents starts with the employee experience, not with a single technology purchase. The most effective implementations allow AI and human agents to work from the same contextual understanding of each customer.
Information gathered during one interaction should inform the next interaction, regardless of the channel or system involved. AI-powered call summaries, real-time sentiment analysis, agent assistance, and next-best-action recommendations can provide employees with immediate insights directly within their workflows.
This approach allows AI to manage routine, high-volume requests such as password resets, delivery tracking, and account updates. Human agents can then focus on complex situations that require empathy, judgment, and relationship management.
“If a customer experiences a crisis, such as a fraudulent transaction, AI can block a card immediately, but it cannot provide the reassurance or sensitive communication needed during a moment of panic,” Anand says. “The answer is not simply choosing one system over another. It is intelligent orchestration.”
In practice, AI can complete technical transactions, analyze customer sentiment in real time, and route urgent or sensitive interactions to specialized human agents. The objective is to combine AI efficiency with the empathy and trust that strengthen customer loyalty.
Building a unified customer experience architecture
Moving from fragmented AI experiments to coordinated orchestration requires both technology and organizational change. Anand says businesses should begin by consolidating data and disconnected point solutions within a unified, cloud-first customer experience platform.
“IT and CX teams need to work more collaboratively,” he explains. Greater alignment between technology and customer experience teams is essential for creating a consistent enterprise-wide view of the customer.
At the architectural level, communications APIs should be embedded into the core of the enterprise so each business function can work from the same customer context. This means moving beyond basic system integration toward a context architecture built on shared ontologies and context graphs.
Such an architecture can create a common understanding across customer experience, operations, sales, service, and AI systems. The broader organizational shift is from reactive customer support to proactive, predictive, and personalized engagement—the three priorities Anand refers to as the “three Ps.”
How AI agents will shape the future of customer experience
The future of customer engagement will be shaped by real-time intelligence, greater AI autonomy, seamless orchestration across channels, and persistent enterprise context. This context will follow customers, employees, and AI agents throughout every interaction.
Rather than analyzing customer conversations after they occur, businesses will increasingly use intelligence to influence and improve those conversations in real time.
“The future of CX will be defined by aligning and simplifying data, infrastructure, and operating models around clear customer outcomes, rather than adding more models and tools,” Anand says. “The rise of AI-powered agent-to-agent interactions is a defining trend. AI systems will not only assist human employees but also manage and resolve interactions independently, creating an almost invisible layer of engagement that improves speed and efficiency.”
Human agents supported by real-time conversational intelligence and next-best-action recommendations will increasingly work alongside AI to deliver what Anand calls a total experience. This model brings together customer, employee, and AI-driven experiences within a connected operating environment.
Tata Communications is developing this model through its Voice AI, AI Workers, and Total Experience Hub solutions.
“Ultimately, customer engagement will evolve from reactive to predictive and increasingly generative,” Anand says. “Businesses will not only respond to customer needs but also actively shape and improve customer journeys in real time.”
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Source: venturebeat.com