As customer expectations continue to rise across digital channels, businesses are under growing pressure to deliver seamless, context-rich and proactive experiences.
Yet many organisations still rely on traditional automation systems that struggle to meet these demands.
Rigid IVR flows, generic chatbot scripts and siloed customer data often create more frustration than value, leaving customers repeating themselves and brands losing control of the customer journey.
According to Gaurav Anand, VP and Head of Customer Interaction Suite at Tata Communications, many companies suffer from what he calls the “customer journey black hole” – a gap where context and customer history fall through the cracks, resulting in broken experiences and unnecessary friction.
“Think about a typical banking interaction,” Anand says.
“A customer fills in a loan application online, then calls the contact centre for support, only to be asked to provide the same information again. It’s no surprise that customers become frustrated.
The consequence isn’t just dissatisfaction – 92 percent of customers say they’ll leave a brand after two or more poor experiences.
The Limits of Traditional Automation
Even as businesses invest in automation to manage scale, traditional systems are increasingly showing their age.
Script-based chatbots struggle to interpret nuanced intent.
IVR systems force customers into predefined paths that rarely reflect what they actually want.
And behind the scenes, data remains fragmented across CRM systems, ticketing platforms, and communication channels.
“Legacy automation solves tasks, not outcomes,” Anand explains. “It might complete a form or look up an account, but it doesn’t understand the end goal of the interaction. It doesn’t collaborate with other systems.
“It doesn’t adapt when the customer deviates from the script. Ultimately, it can’t orchestrate a full journey.”
As customer journeys become more complex and decentralised, these limitations are becoming untenable.
Organisations are now looking for a more intelligent and adaptive approach that can engage customers in real time, maintain continuity, and drive tangible results.
Agentic AI in Action
This is where agentic AI comes into play.
Unlike traditional automation, agentic AI is built around autonomous, outcome-driven agents that can reason, collaborate and take contextual decisions.
These agents can be trained for specific use cases such as cart abandonment recovery, KYC completion, proactive service notifications or multi-step issue resolution. This helps brands transition from basic automation to autonomous actions and AI decisioning.
“Agentic AI is purpose-built,” Anand says. “Each agent understands the goal it needs to achieve, but it also knows how to work with other agents throughout the journey.
“So you may have one agent focused on customer onboarding, another handling verification, and another coordinating follow-ups – all sharing context in the background.”
This type of orchestration is increasingly essential for large enterprises. In e-commerce, for example, an agentic AI flow can detect a customer abandoning a cart, trigger hyper-personalised reminders across SMS, WhatsApp or email, and follow up based on engagement. If the customer expresses confusion or dissatisfaction, the agent can switch channels or escalate to a human agent with full context.
“You’re no longer relying on one-size-fits-all automation,” Anand adds.
You’re creating a dynamic loop that adapts to each customer’s needs and behaviours.
Voice AI: Transforming Real-Time Interactions
The rise of voice AI is taking things a step further.