How Can Banks in India Maximize the Value of Their AI Investments in Contact Centers?

A recent global study revealed that fewer than 30% of banks are deriving full value from their AI investments in contact centers.

That’s surprising, isn’t it?

Because in India, customers interact with their bank’s contact centre more often than they visit branches. Whether it’s blocking a lost debit card, resetting a UPI PIN, or checking loan eligibility, the contact centre has become the frontline of customer trust.

But here’s the truth.

While most banks have introduced some form of AI and automation, many aren’t seeing the real returns.

Why?

Because five critical aspects often go wrong.

Let’s look at each one, and more importantly, see how banks can get them right.

1. AI Without Process Maturity Is Just a Fancy Gadget

Many banks proudly announce they’ve launched AI chatbots or IVR self-service. However, without the backend, banks cannot ensure that customers have a smooth experience.

Let us look at a scenario.

Imagine Ramesh, an MSME trader from Coimbatore, calls his bank to check why a UPI payment failed. The IVR bot greets him, but then transfers him to an agent who has no context of the issue. Ramesh ends up explaining the story all over again.

This would be frustrating.

How Do You Fix This?

Instead of just plugging in AI, banks should focus on process maturity in AI adoption to truly connect the dots

For instance, when ICICI Bank’s iPal bot hands over to an agent, it instantly shares the chat history with them. The agent picks up from where the bot left off, and that’s process maturity.

2. AI’s Value Must Be Measured in Business Terms

Most banks look at AI purely as a cost-saving tool, and their objectives typically include:

• Reduce average call time
• Deflect calls to self-service.

But that’s just scratching the surface. AI is no longer just about cutting costs; it’s transforming how businesses approach customer engagement and proving AI’s Business Value Beyond Cost Saving

Let me give you an example.

One of our NBFC customers deployed voice bots to answer transactional queries. They saved significantly in call handling costs.

However, they never explored what else was possible, such as whether the freed-up agents could cross-sell other offerings.

How Do You Fix This?

Take HDFC Bank’s example. By automating routine card queries, it redirected agents to focus on explaining loan products to salaried millennials.

This led to measurable business gains, including increased loan conversions and lower call costs. That’s when AI shifts from a cost line to a revenue driver.

3. Reduce Friction for Both Customers and Agents

Would you agree that long wait times irritate customers?

Agents often struggle with switching between multiple systems at once, such as CRM, loan processing, and KYC verification, which slows response times, but this can be addressed by Reducing Friction with Unified Desktop.

Let me give you an example.

It takes around 10 minutes for an agent to switch between two systems to let a consumer change her address. During this period, the customer is waiting.

The customer is annoyed, the agent is stressed, and the overall experience suffers as a result.

How Do You Fix This?

A unified desktop changes the game.

One of our banking customers recently piloted this, where customer history, recent transactions, and even AI-generated “next best action” appear on a single screen.

With this, the customer’s address change request can now be resolved in under three minutes. This results in making both the customer and the agent happy.

4. Use AI Savings to Build Better Talent

Does AI automation help you save money?

You bet it does. However, if banks only use it to reduce headcount, are they missing an opportunity to build skilled and motivated teams?

Let me give you an example.

One of our banking customers initially thought of reducing staff after automating cheque status queries. However, they soon realized that people still required human assistance with delicate and complex conversations, such as refinancing a loan.

The bank didn’t lay off any workers; instead, it invested in training its agents on how to communicate with empathy and provide financial advice.

How Do You Fix This?

Think of it as reallocation, not reduction. AI handles the tedious and transactional tasks, while banks utilize the money they save to compensate their employees more, provide them with training, or hire agents with specialized skills.

This way, AI becomes a people enabler, not a people replacer.

5. Involve the Frontline in AI Decisions

Too often, AI tools are bought by top management and IT teams without asking the agents who actually use them.

This leads to the use of elaborate systems that don’t work in real life.

Let’s examine a typical scenario.

Imagine a cooperative bank implementing a new speech analytics tool. What if the interface is clunky and the analytics are fairly inaccurate due to the difficulty of regional accents? Obviously, the adoption will nosedive.

How Do You Fix This?

Involving your frontline agents in pilot testing your AI-driven speech analytics tool is essential. Agents will give feedback on accents, script recommendations, and usability. Because they feel heard, the adoption will be smooth.

Putting It All Together

When these five aspects are done right, the difference is huge. This is how contact center interactions will look:

• 40% of simple requests (balance, cheque status, EMI due date) will be resolved by bots, and when a call is transferred to an agent, the context will be transferred with it.
• Savings from AI-driven automation are tracked, but so is the 20% increase in new loan leads from redeployed agents.
• One screen displays the entire customer journey, enabling agents to resolve calls more efficiently.
• Agents undergo financial wellness training, funded by AI savings.
• Agents will be part of the decision-making process for every new AI rollout.

What does this lead to?

Customers will trust the contact centre, agents will feel empowered, and the bank will see real business growth.

Customers expect their bank to be available anytime, anywhere, in their language.

That makes the contact centre the beating heart of customer experience.

However, unless banks address the five aspects mentioned here, such as process maturity, measurement, friction, talent reinvestment, and frontline involvement, AI will remain just a shiny toy.

The banks that get this right won’t just solve customer problems, they’ll build loyalty, deepen relationships, and turn their contact centres into engines of growth.

So the next time your bank rolls out a new AI feature, ask yourself:

Is this just a tool, or is it a step towards truly transforming the way we serve our customers?