The first version of an AI project is often the easy part.
A team identifies a repetitive task, runs a pilot, and sees early signs of promise. The hard work begins when other departments want in, exceptions start appearing, and customers expect the experience to work across every channel.
That is when many organizations discover that instead of an operating model, they have built a useful but isolated tool.
This point came up during a recent discussion with Gaurav Anand, Vice President and Head of Customer Interaction Suite at Tata Communications.
Anand argues that “a successful AI program is going to be built around repeatable operating models, not individual use cases.”
He explained how businesses pursuing touchless resolution should not aim to simply automate every conversation; instead, they should focus on creating a dependable way for AI, employees, data, and systems to work together around a defined outcome.
To help CX and transformation leaders deliver an impactful touchless resolution system, we have broken down the five key steps:
1. Start With the Outcome, Not the Technology
The best starting point is not ‘where can we put an AI agent?’ It is “where is the friction, and what result would make a measurable difference?”
Lead qualification, appointment booking, renewals, and routine service requests can all be useful candidates. They are often high-volume and contain clearly defined actions. But the right use case will vary by industry, customer base, and existing processes.
Anand recommends beginning with “one or two high-value, measurable business outcomes.”
That could sit on the acquisition side of the business, such as faster lead response or better-qualified opportunities. Or it could sit later in the lifecycle, improving retention, service, or loyalty.
Before deploying anything, establish a baseline. If the aim is to improve appointment booking, measure current completion rates, no-shows, time to confirmation, and the amount of manual follow-up required. If the aim is better service resolution, understand the current repeat-contact rate, escalation volume, and customer effort.
Without a baseline, it is difficult to tell whether AI is changing the outcome or simply adding another layer to the journey.
2. Map the Journey Beyond the First Conversation
A customer interaction rarely ends where the conversation ends:
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An appointment may require access to availability, customer records, location data, and confirmation tools.
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A renewal may involve entitlement checks, pricing, payment, and exception approval.
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A service issue may need information from several systems before it can be understood and resolved.
This is where many automation projects become brittle. They work in one department but cannot access the intelligence or actions needed from another.
Anand believes that businesses often reach a point where individual teams have made progress with AI, only to ask: ‘Okay, now what?’
The answer is to map the whole journey, including structured data, unstructured information, policies, approvals, system updates, and the human decisions that cannot be automated.
It is also worth looking for important knowledge that has fallen through the cracks. An exception approved by a senior leader may exist only in a CRM note. A similar case may arise later, but nobody can quickly find or apply the previous decision.
A connected context layer can surface that information, enabling AI to identify the pattern and route it to the right decision-maker where necessary.
