Shalima Bhalla Podcast Transcript
Shalima Bhalla joins host Brian Thomas on The Digital Executive Podcast.
Brian Thomas: Welcome to The Digital Executive. Today’s guest is Shilama Bhalla. Shalima Bhalla is a global customer experience and AI executive with more than 20 years of experience helping enterprises navigate digital transformation. She currently leads global customer experience solutions and go-to-market strategy for the telecommunications sector at a Fortune 500 cloud and enterprise technology company, partnering with some of the world’s largest organizations on AI, cloud, and customer engagement initiatives.
Prior to her current role, Shalima held senior leadership positions at Cisco, Afiniti, and Avaya, giving her a rare cross-industry perspective on how technology transformation plays out at enterprise scale. She’s also the host of the Customer Signal podcast, where she interviews CEOs, authors, and innovators shaping the future of customer experience and artificial intelligence.
Her expertise focus on helping business leaders understand how generative AI and agentic AI can drive meaningful business outcomes while keeping the customer at the center of transformation. Well, good afternoon, Shalima. Welcome to the show.
Shalima Bhalla: Good afternoon. Thank you for having me on the show.
Brian Thomas: Absolutely, my friend. I appreciate it. Really do. I know you’re hailing out of New York. I’m in Kansas City, so just an hour difference today, and I appreciate you making the time and traversing the time zones. Shalima, if you don’t mind, let’s jump right into your first question. You started your career with hands-on, literally in the machinery of contact centers, configuring agent routing systems, and writing the code that determined how customer calls flowed through organizations before moving into customer-facing roles, partnerships, and now global customer experience and AI leadership at a Fortune 500.
That technical to strategic arc is very rare. How did you– how did starting in that engine room shape the way you approach customer experience today?
Shalima Bhalla: Absolutely. That actually the technical background shape the way I think about customer experience and my leadership strategy. So starting in the engine room actually taught me that the customer experience is built through thousands of decisions that customer actually never sees.
So when you configure routing systems and you’re deciding who gets help first, which agent will receive the call, and what happens when the right person is not available. On a screen that might actually look like technical decisions, but for the customer, you’re actually designing how the customer will feel, whether they feel understood or whether they feel frustrated.
Now, let’s take something as simple as customer calling about incorrect bill in for a telco company, right? You can route that call based on what agent is available next, but does that agent have the right skills, or do they have access to the customer’s account history and even authority to correct that bill?
And if they don’t have that decisioning power and access to the right information, then you have probably answered the call without actually solving the customer problem, and that exactly shapes how I’m approaching leadership today. I always wanted to understand that the promises that we are making to our customers, what’s actually happening underneath and how we are making it real to the customers.
Can the systems exchange information? Can someone take the action? And actually, who owns the outcome if process breaks? And on the other hand, when I moved to the customer-facing roles and partnerships, I also learned that technology is only part of the equation, right? You need to understand the commercial alignment.
You need to understand operational ownership and People who understand the customer situation better, right? And now coming into AI, this background is really helpful because a system can sound incredibly intelligent and still leave the customer stuck, right? You- they can answer and they can talk to customer beautifully, but in the end they’re not able to solve the customer problem, or they come up…
they hallucinate and come up with the wrong answer. So my starting point is always what is customer trying to accomplish, and what actually needs to be done behind the scenes to make it happen?
Brian Thomas: Thank you. I appreciate that. Your perspective, obviously working in the trenches early on gave you that, as you said, that technical background shaped how you look at support and strategy in your role today.
And it’s important that, when you’re working with customers in these busy contact centers, obviously, is understanding and finding that customer sentiment. You did talk about that it was important that you were delivering on the promises to the customer, but to do that, you have to actually, again, behind the scenes, in the background, work on those technical side, technical side of things to really deliver on those.
And of course, we all know this, we’ve both been in tech a long time, Shalima, it’s only part of the equation. There’s a lot more to that. We talked about customer sentiment, operational alignment, et cetera. So I really appreciate the backstory. And Shalima, you’ve argued that customer experience is the missing layer in most companies’ AI strategy, that AI strategy too often gets drafted far from the customer, living in innovation hubs and executive steering committees.
Why does AI strategy drift so far from the actual customer, and what breaks when it does?
Shalima Bhalla: Yeah, I think the strategy drifts away from the customer because we talk about tech. Lot of leaders are obsessed with technology, and they begin with what technology they are going to deploy rather than the problem that they will be solving for the customers, right?
The conversation generally that I hear is, “Hey, which model should we be using? How many use cases can we launch, and how much cost can we remove?” I mean, these are not bad questions, but who is asking whether how and what problems are we solving for the customers? How we, how we can make customers’ life easier?
So, let’s take an example. I, I mean, I’m working very closely with telco, so most of my examples are stemming from the telco companies. Imagine a telco company introducing an AI assistant for billing question, because majority of the telco queries actually stem from the, the billing questions. Now, the assistant, the AI assistant for billing, can explain beautifully that why these charges happen.
But if the customer is calling because you promised a discount and that’s missing in the bill, they need the bill to be corrected. And if the AI cannot apply the correction or transfer them to someone with the full context, the company has created a more articulated version of the same frustration.
Another example is a lot of companies, especially in the contact center space, they measure success through fewer calls reaching to the agent. They wanted to minimize and increase the containment rate. That’s that’s one of the metric that almost everyone is asking, how we can increase the containment rate and how we can reduce the number of calls being handled by an human agent.
That number of calls reaching to the contact center might improve. You might be able to reduce those calls. And there co- there could be two reasons for that. B- One, that, awesome, you’re able to answer customers’ questions, and customer gets their answer. But there might be a possibility that the customers gave up along the way because your self-service loops are so frustrating that customer didn’t even want to engage.
So, you need to understand which actually, which cause actually lead to the, to reduced number of calls, and therefore optimize it, right? Now, i-in a larger company, individual teams, they optimize their own piece. What that, what I mean by that is, for example, sales is optimizing for their own KPIs, and the services are optimizing for their own th- their own KPIs, and so is the support.
And then burden lies on the customer to bridge everything together. And if you’re not building an infrastructure that bridges that journey together, you are leaving the customer frustrated. So the question I want to ask every executive is, after this AI investment, what can your customer a- accomplish more easily than they could yesterday?
Brian Thomas: Thank you. Appreciate that. You talked about, I think it’s interesting, strategy does in, in a tech space, I see this all the time, but strategy drifts away from the customer a lot of times ’cause we’re always focused on, hey, tech can solve everything. Well, tech c- really can’t solve it. It’s doesn’t have that magic bullet all the time.
So the question should be focused on, how do we make the customers’ lives easier, as you talked about. We, we have amazing platforms and AI agents now that can, like you said, that billing example, right? We can automate that whole thing, but if the customer is calling because they’re frustrated because you promised them a discount, and we can’t handle that, how do we mitigate that, right?
You talked about reducing call containment might be the focus of the day, but if we can’t get to that customer satisfaction and elevate their priorities, we’re just lost. So I, I, I love the, the sentiment and the insights there. Shalima, one of the, your more forward-leaning ideas is that the best experience often means not waiting for customers to contact you at all.
If systems can recognize failure patterns early, you reach out with a workaround or a resolution already in motion. How close are enterprises to delivering that kind of proactive service, and what’s standing in the way?
Shalima Bhalla: Yeah. That’s like my North Star. I mean, ultimately, I want the businesses to anticipate customer problems or predict the customer problems before customer actually experiences it, and able to fix it.
Or if not able to fix it, at least reach out to the customer, l- and let them know that, “Hey, this, th- we, we know that you’re experiencing this. We are working on it,” etc. And we have technology in place today to, to deliver that. But the question is, are we ready? Now- Today we are close enough to deliver proactive service in specific situations today, right?
For example networks can identify that the customer might be experiencing some issues, bandwidth issues and can apply can reach out to the customer proactively. So in bits and pieces and pockets, we, we have been delivering and we are, or we are close enough to deliver. But delivering it consistently acro- across the entire customer journey, that is the harder challenge.
Right? So example of the company that is doing it right is Delta. So when a flight is canceled or delayed it can attempt to rebook the customer automatically and notify them. That’s actually the principle in action. And I, I already shared the example of the, the telco customers. But Applying that end-to-end, so anticipating, predicting that, hey, the network is experiencing issues or the customer might have some bandwidth challenges.
But then the reaching out to the customer and then applying the credit back to the customer bills for the outage cause, it requires coordination among different teams, different systems. So delivering that end-to-end, we are still not quite there, right? Companies are attempting it, and we are not too far.
And with technologies like context where we can bridge the customer journey together without moving the entire databases, we are getting very, very close to having that north star of predicting the problems and solving it. But it’s… But again, as I said before, it’s not just the technology challenge, right?
Like we are very, getting very close to the technology, but you have to build your processes and design your teams in a way that you’re able to deliver that seamless experience to, to, to your end customer. But on the other hand, there’s also a judgment question. What I mean by that is, how confident are we that there is a problem, right?
Like your system might predict that there’s a problem, but are you sure? And what action is appropriate? Can we take it automatically or should we bring customer to approve it or human in the loop? A helpful intervention can become intrusive if you get those decisions wrong, right? So where I would start is that figure out those problems that you know the customers experience frequently and you have clearly defined solutions and responses to those problems.
And, and start with that. Once you build the confidence, you know that how these systems are working and where the process loopholes can be then you can scale it out across the customer journey
Brian Thomas: Thank you. Appreciate that. You talked a little bit about, again, tech, and we do some great things in tech and delivering these AI solutions, but delivering it consistently across this full spectrum, the full customer journey is the challenging point.
And again, you brought a, a great example up in the airline industry. If, if something, a flight needs to be rescheduled, crediting to the, to the customer, et cetera. We’re getting there, but it requires a lot of multiple touch points, and I know that’s a pain point for customers because it’s gotta go through different departments, and we, again, have to have that human in the loop, and, and there’s just so much that can fall through the cracks.
And I know we’re getting better at that, but if we don’t really listen to the customer find their pain points, def-define what that solution is, then, we’re gonna be back to square one, and our customer satisfaction scores will not be where we need them. So thank you. Shalima, the last question of the day, you host the Customer Signal podcast specifically because you believe the most important AI and customer experience ideas come from practitioner conversations, not white papers.
From all those conversations and your own vantage point, where do you see generative and agentic AI taking customer experience over the next five years, and what will separate the companies that get it right from those that lose the customer in the process?
Shalima Bhalla: Absolutely. Yeah, I was, The, the reason I started Customer Signal is because I was tired of those marketing slides, and, and whoever I talked to from the, the customer’s perspective, they wanted to understand what is actually happening on the ground, what works and what doesn’t work.
So that’s the reason I started Customer Signal, to bring that practitioner perspective to the cust- to whoever wants to implement the real technology and make a difference in the customer journey. So now over the next five years I expect three, three major shifts. The very first one is AI will move further from just answering the questions, and that has already started.
Like, agentic AI can use tools and take the next step and, and can do actions on behalf of you or for you. And as I said, that th- this is happening in bits and, and pockets, but we are getting closer to transforming that customer experience where the c- the experience and the service becomes more proactive throughout the, throughout the journey.
The second is companies are increasingly, Wanted to connect those operational signals and customer context, and wanted to act on the, the problems. So, combining that operational intelligence directly into the frontline, and then take that loop and feed it to make it better. Again, some companies are doing…
have started to do it, but they’re not quite there yet. And third, I think AI will become a stronger partner for human employees. It can gather the history customer context, and handle routine steps, and free up the, the human to do what they are best to do. For example, judgment and reassurance and complex decisions.
The companies that get this right will combine the reliable execution with the customer control. They will know when they can apply AI and when it needs permission from the customer to take an action, and when to involve human to help out to answer those questions. So a- and also the important part is carrying the context across all of these conversations.
So when the customer is moving from one step to the other step so that the customer doesn’t have to repeat themselves all over again. That’s like the… my biggest pest- pet peeve. And my prediction is that AI will make competent service more widely available. We have, we, we say that white glove service only for the premium customers, but now we have this technology available where we can have this white glove service available to almost every every customer.
I’m working on one of the project where we can have one agent available to each and every customer so that when they reach out to the companies, it feels like they’re talking to their old friend who knows their history, who knows their purchasing decisions, who know what they have called six months ago or a year ago, and then provide more personalized recommendation as they get from, from their friends
Brian Thomas: Thank you. Appreciate that. Just to highlight a few things, Shalima, again, what you would, a-again, share on the podcast and through your career journey, of course, is talking to the customer is the fastest way to finding root causes of good or poor customer experience. You talked about agentic AI as advancing the process rapidly, including a more proactive process to stay ahead of customers’ needs.
I thought that was, that was interesting. And of course, improving that feedback loop from the customer we know AI will become a better partner for the employees that support the customer. And your… The last thing I took away was your prediction. We know AI will make a more competent service, kinda like a white glove type service.
Again, more proactive freeing up employees that support the customer will only make it better. But again, there’s a lot of work yet to do to get there, and I appreciate the insights. Shalima, it was such a pleasure having you on today, and I look forward to speaking with you real soon.
Shalima Bhalla: Absolutely. Thank you for having me, Brian. Appreciate it.
Brian Thomas: Bye for now.
Shalima Bhalla Podcast Transcript. Listen to the audio on the guest’s Podcast Page.
