New Delhi, July 30 -- As enterprises in India race to modernize customer experience (CX), many are still grappling with the basics, from system availability and data security to effective self-service. In a wide-ranging conversation with TechCircle, Anil Chawla, Managing Director - Customer Engagement Solutions at Verint India, shares insights on the hidden roadblocks to scaling CX, the real-world limits of automation, and why a platform-first mindset matters more than ever. He also explains how AI is quietly moving beyond chatbots in regulated sectors like banking, and why the next 6 to 12 months will separate leaders from laggards in enterprise CX. Edited Excerpts:
What's the most overlooked challenge Indian enterprises face when scaling CX technology across diverse customer segments and regions?
There are a few major challenges that stand out. The first is ensuring application availability. This goes beyond infrastructure uptime, it's about making sure the application itself is accessible and functioning consistently, 24/7, across all geographies. For example, if a customer tries to make a banking transaction and the app is down, even for a minute, that disruption can have a direct impact. As users increasingly rely on digital platforms for critical tasks, any downtime, however brief, becomes a major issue.
The second challenge is information security. Enterprises need to protect sensitive customer data, especially personally identifiable information (PII). With stricter compliance requirements and rising concerns about privacy, organizations have to ensure that such data remains confidential and doesn't leave their systems. Maintaining strong controls around how this data is stored, accessed, and shared is essential.
The third challenge is enabling effective self-service for users. As consumer behavior shifts, especially among younger users who prefer digital-first, low-touch interactions, businesses are under pressure to offer tools that let people resolve issues or complete transactions on their own. This means providing intuitive interfaces, access to relevant information, and systems that support independent use without needing to contact support. Delivering this kind of experience requires thoughtful design and a clear understanding of what users need to succeed on their own.
Where do most tech-led CX initiatives go wrong, even when the right tools are in place?
Different organizations approach customer experience (CX) in different ways. However, those that succeed consistently tend to have a dedicated leader responsible for CX. The title may vary, CX officer, AI officer, or something else, but what matters is clear accountability for driving CX across the organization.
Having this leadership is essential because customer expectations are changing rapidly. CX strategies must evolve in response. A CX leader helps ensure the organization keeps pace by understanding shifts in customer behavior and aligning internal processes accordingly. This role is key to sustaining CX initiatives and ensuring they deliver the intended outcomes, rather than fading out over time.
The second critical factor is choosing the right tools. That means selecting a platform that is agile and adaptable, not a fixed, one-off product. The platform must support ongoing change, allowing the organization to add or modify applications as customer needs evolve. A flexible platform enables organizations to keep CX relevant and effective.
Do you think banks will continue to limit AI use in CX to chatbots, or are you seeing a shift toward broader applications?
Conversational bots represent the most basic stage in the AI maturity curve, especially when it comes to customer engagement. They're often the first point of contact, but their capabilities are limited. For example, if you call a bank today, you're likely to encounter an IVR system, pressing buttons to navigate menus, or a voice bot, which just replaces the IVR with speech input. These bots usually handle only simple queries and rarely resolve issues.
In regulated industries like banking, most deployments are on-premise, not on the cloud. That makes it difficult to integrate advanced AI, since large language models (LLMs) rely on cloud-based compute power. But this is changing. Banks are now exploring "AI as a Service" models. In this setup, personally identifiable information (PII) isn't stored on the cloud. Instead, the cloud is used purely for compute, and results are pushed back to the on-prem system.
For example, real-time agent assist is now possible. During a call, the system can analyze customer sentiment, suggest empathetic responses, and guide the agent on next best actions, all in real time. The analytics are powered by Verint's Da Vinci platform on the cloud, but nothing is stored there.
Another example is post-call summaries. After a conversation, agents are usually required to write a summary. This can now be automated through a "wrap-up bot." It runs in the cloud but functions as a service, with no data retained on the cloud. This helps eliminate manual errors and speeds up the process.
These are practical, compliant ways to apply AI in regulated environments, going beyond simple chatbots and helping both agents and customers in real time.
Where do you think automation is hurting the customer experience? In an enterprise setup, what's the root cause, is it limitations in the AI itself, or something else?
On why some customer experience strategies often fail, it's usually due to the tools being used. A common example is FAQ-based chatbots. These bots only respond if there's a predefined answer. If they don't understand a query, they simply say they can't help. I'm sure you've encountered this when shopping online or calling support.
The problem isn't just with the bot's limitations, it's that many of these tools are outdated. They haven't evolved to match current customer expectations. As a result, they often create frustrating experiences.
To avoid this, companies need to invest in platforms, not standalone tools. These platforms should be flexible and configurable so they can adapt to changing needs. If not, businesses may face a complete replacement later, which is costly and time-consuming. More importantly, it impacts customers, who deal with these tools regularly.
So the key is to choose adaptable solutions, avoid fixed-function tools, and continuously review and update based on user feedback and behavior. That's how companies can prevent poor experiences and keep up with customer expectations.
In enterprise CX stacks, where are banks still relying on patchwork or "duct tape" integrations, and what problems are hiding beneath them?
Banks still run on legacy systems, and due to regulatory requirements, many of these systems will remain in place. That's why the newer technology stacks banks adopt need to be API-enabled.
API-enabled systems allow easy integration. Their interfaces are openly available, not locked into proprietary formats. This matters because many older systems don't give banks the flexibility to update or modify their applications. Once implemented, those systems are hard to change. This rigidity keeps banks stuck in outdated patterns just to maintain stability.
In contrast, open API-based systems give banks more control. They allow updates to the application stack without disruption and help avoid hidden integration costs.
From a regulatory standpoint, banks must comply with authorities like the RBI and meet strict information security standards. They also need to ensure the systems they integrate with are under their control. At the same time, they want to offer better user experiences. Balancing compliance and innovation is not easy, but open API systems help banks move in that direction without sacrificing control or security.
You've been part of many long digital journeys with your clients. What's an early sign that tells you whether a CX transformation for a bank or organization will succeed or stall?
These are early signals we look for. One clear signal is ongoing curiosity and engagement. For example, among the 15 top banks in India, we work with about 12. A common theme in our conversations is: what more can be done with the solution, how can we go further? That kind of thinking shows the engagement is strong. The customer isn't just using the product, they're actively exploring how to improve.
Another signal is when organizations start focusing on improving their own internal processes. Our CX platform gives them visibility into how they use customer data internally. This often reveals silos. Since CX depends on seamless data flow, friction usually means broken processes. When clients start asking for insight into these gaps, it's a clear sign they're working on internal improvement too.
The third signal is governance. We see active involvement from the C-level, with regular reviews of metrics like customer satisfaction, compliance, and complaint volumes. When senior leaders track these indicators closely, it's a strong sign that the engagement is heading in the right direction.
How are enterprises misjudging AI readiness, especially for customer-facing applications?
Startups often approach banks or enterprises with point solutions, products designed for a narrow use case. These solutions are usually focused on a single area, not the broader needs of the enterprise. As a result, enterprises may adopt one of these tools, expect significant outcomes, and then realize its limited scope. This can lead to failure in implementation and slow down progress toward improving customer experience.
It's important not to be misled by narrow business use cases. Instead, consider how one use case can drive broader business value and connect to others that can also be optimized. Focusing on just one outcome can limit progress in other areas of the organization.
Enterprises should evaluate providers that offer solutions applicable across the business. The goal is to support large customer bases-such as those in India, where banks serve millions. This requires platforms that can scale, offer round-the-clock support, and are built around measurable business outcomes.
We know data is everywhere, but real insight is rare. So what are leading enterprises doing differently to turn customer interaction data into real business value?
Enterprises need a consistent, structured approach to implementing AI and customer experience (CX) strategies. CX often relies heavily on AI, and Verint supports this through Da Vinci, our AI engine. Da Vinci helps enterprises gain insights into customer conversations, spot trends, improve agent productivity, and maintain compliance. It supports a wide range of use cases through around 50 prebuilt bots, all powered by the Da Vinci platform.
Da Vinci is at the core of Verint's platform. All AI capabilities stem from it, but we also support integration with a customer's own large language models (LLMs) or AI systems. This gives enterprises the flexibility to use Verint's tools or plug in their own, while maintaining a consistent view across consumer, employee, and survey data. These insights are delivered in real time.
We follow a platform-based model that avoids the need for a full system overhaul. Enterprises can continue using their existing tools and applications, and we integrate with those. If they want to start small, they can, whether that means using a specific data set, their own data lake, or running on-premise, cloud, or hybrid infrastructure. Verint supports all of these options.
This approach gives enterprises flexibility to scale at their own pace, within their budget, and with their existing systems intact. We help them move forward step by step toward their defined goals.
Looking ahead, what's one shift in enterprise customer experience you see as inevitable in the next three to five years, yet most organizations still aren't ready for?
AI adoption is no longer a future consideration, it's already happening. The key question for enterprises now is not if, but when they'll deploy it, today, in six months, or in two years.
Starting early gives companies the chance to experiment, learn, and generate real business outcomes. The focus should be on using AI for customer experience in ways that deliver measurable value, not just running pilots or proofs of concept that drag on without impact.
Many organizations start AI projects that stay in the test phase too long and never scale. That delay puts them behind. There's no room anymore for long trial-and-error cycles. Companies that adopt AI now and use it effectively will gain a clear lead over the next 6 to 12 months.
Those that wait risk falling behind. The ones deploying AI with a focus on outcomes today will define the next phase of customer experience.
Published by HT Digital Content Services with permission from TechCircle.