Modernizing CX with Blended AI & Human Teams

[00:00:00] Arun Chandra:  Without question, in the next three plus years, we will be managing or brands will be managing a blended workforce of AI agents and human agents. I mean, that is a given. And so then it becomes a matter of how are you orchestrating and harmoniously managing the coexistence of AI agents and human agents? What are the role types? For example, those of us who are used to doing workforce planning, your workforce planning tools should be able to accommodate the ability to schedule AI agents and human agents. The biggest change that’ll happen is going to be that coexistence. It is happening today. You can see it. People have to start thinking of it a little bit more systematically because just like you have cost of wages with a human agent.

[00:00:40] Scott Kinka:  Technology is changing faster than ever. But the most important conversations aren’t about the tools. They’re about the ideas and the people shaping what comes next. On the Bridgecast, I sit down with the leaders driving change from AI and cybersecurity to the infrastructure powering modern business. Together, we explore how technology moves business forward, what they’re seeing, what they’re building, and what it means for the rest of us. I’m Scott Kinka, and this is the Bridgecast, presented by Bridgepointe Technologies. Hi, and welcome back to another episode of the Bridgecast. Today, we are joined by Arun Chandra. He’s the chief operating officer at NICE, where he leads company wide operations and AI driven business transformation. Arun joined NICE in a newly created COO role. We’re gonna talk about that to help organizations rethink how AI can modernize operations and improve customer experience. He comes to the role at NICE after extensive experience scaling customer operations at some of the world’s largest companies, including Disney, Meta, and HP. We are gonna get into it. Let me tell you. His work sits at the intersection of AI strategy, operational execution, and customer experience, helping organizations scale intelligently in an ever increasingly AI driven world. Arun, welcome to the Bridgecast.

[00:02:07] Arun Chandra:  Thanks, Farf. Pleasure to be here.

[00:02:09] Scott Kinka:  Indeed. First off, I we we did our little AI based, uh, bio, and then I modified it. How did we do?

[00:02:14] Arun Chandra:  That did just

[00:02:16] Scott Kinka:  fine. Well, awesome. Let’s start there. That’s quite a resume. So just take us back. How did you get started in this space in general? You don’t need to dwell there forever, but get us to where you landed at NICE.

[00:02:28] Arun Chandra:  The fastest way to describe this would be that I got to know NICE, uh, because I was a customer of NICE for four years at Disney. So at Disney, the last role that I had before NICE, the my my mandate was to modernize and transform the customer experience function, uh, particularly for the streaming business, which is the largest contact volume business, about, you know, 180 plus million subscribers. And the the ambition was very simple. You know, transform the function with two clear mandates. It’s almost like the dual mandate of the Fed. One was deliver exceptional experiences, but at the same time, deliver them at the lowest possible cost to serve so that, you know, obviously, you can support the product. The product is, you know, it’s it’s a streaming product. It’s a $15.20 dollar product per month. So that was the mandate. And in order to deliver against that mandate, um, technology was the very clear choice that we had to go drive across the entire organization. We can do that kind of transformation with our technology. And in that process, we ended up standardizing on the NICE platform, uh, for most of our work, if you would. And that’s how I got to know the company, which led to, you know, my being here today.

[00:03:30] Scott Kinka:  Fantastic. What got you started in technology in general?

[00:03:34] Arun Chandra:  That started pretty early on. I mean, I have I have two degrees in computer science, so I, uh, and that was just the natural path that we that I grew up into. Um, I joined Hewlett Packard as a software programmer very early on, and I wrote code for the first couple of years. So that’s one of my introduction to technology, but I also knew very early on that I wanted to have a broader aperture, a broader lens across the business. And so I was sort of actively looking for doing different things. And over time, within the HP itself, every two years, I ended up being in a different function, different geography, growing as the company grew.

[00:04:07] Scott Kinka:  You got it. Um, and and then walk us back to Meta, the Meta experience. That’s quite a name.

[00:04:12] Arun Chandra:  Yeah. So, Cynth, let me then then tie the whole story together since you’re asking for the pieces. If you would, maybe I’ll just string it together. So Hewlett Packard early on, software development, going into other functions, international assignments, being lived in Hong Kong for a while, came back to The US, uh, early nineties and grew up with HP, did my part time MBA at the same time. You know, I was gonna go my MBA full time, but realized, you know, HP said, hey. We’ll, you know, we’ll pay if you were to do it part time. I’m like, the math is really good in that equation. So did my part time MBA. I grew inside HP. Um, I actually then left after I was on the senior leadership team of HP. I did leave for six years, and I did three small companies. One was a vent CS CEO, venture backed company. Second was a small public software company, and then the third was actually a PE backed company. And during those six, seven years, I also spent time very closely with private equity, so I learned a lot about that operating model, which I know you guys are a part of as well. And then I returned back to HP in a leadership role that Meg Whitman became CEO to help transform the company and pull it out of its slump, if you were. Did that for a period of time. At Hewlett Packard, my last role was working for the CEO, Antonio Neri, running global operations, which was essentially the entire quote to cash life cycle for Hewlett Packard Enterprise, which included all the customer operations, all the supply chain and manufacturing that took place, you know, globally, and then, of course, the rest of the equation between the core to cash life cycle. So I did that, and it was a large organization. Had a lot of success doing that and enjoyed it. Um, and at that point in time, I got recruited by Facebook to go help sort out some problems that they were having, which I can get into. But that was a journey, and my motivation was mostly driven by learning curve. As my learning curve flattens out, I get more interested in trying to try take foundational skills and apply them to different problems. That’s what led me to Facebook.

[00:06:05] Scott Kinka:  I love that. So, you know, there are some big names in there. Give me some experiences that shaped how you think about leading change now. I mean, you you’re really a change agent. Give me some of the career experience that have gotten you to, you know, to where you are, and and how does that affect the way you you lead change today?

[00:06:23] Arun Chandra:  So one of the benefits that I’ve had is I’ve led transformations and change as, you know, as you rightfully call it, both in small organizations. So even the smaller companies that I said that I was leading and running as CEO, most of them required, you know, uh, transformation. One thing you realize after you you know, if you’ve been there on for Ohio is that anytime somebody’s hiring for a CEO for new I mean, a new CEO for a company, there’s clearly a challenge to be addressed. There’s some problems that and that normally means you gotta transform the business the way it is, whether it be growth or cost related, but you gotta transform the business. So that benefit of driving transformation both in a small company and an ultra large company like Hewlett Packard even before the spin off happened has given me a pretty good sense of where to push and where to press in transformations sequencing, timing, and pacing to drive change management is an art form that really requires very careful consideration beyond the spreadsheets that you get as to what the benefits of the transformation might be. So I would say that small company, large company, matting that into a in a more of an art form to look at when to drive change, how much to drive, and how quickly, I think those are important factors.

[00:07:35] Scott Kinka:  I’d venture to guess that most of our listeners and and we talked about this in the preshow. Our listeners are, you know, CIOs, directors of IT, you know, your peers, maybe more on the small to mid side, certainly with the not, you know, considering the logos that you’ve worked for. Right? Give me an observation about running operations in some of the largest businesses in the world that might surprise some of the listeners who operate in the mid market and below?

[00:07:58] Arun Chandra:  I mean, one whole data point would be that, uh, you know, I ran the CX function for Disney. And quite honestly, I had not drawn CX functions for a consumer company for and not all any consumer company. Disney, the ultimate consumer company. And so the little factoid there would be that I have run tried to run operations like a general manager and not like a functional manager. So in other words, when you look at operations, whether it be the corporate cash operation for Hewlett Packard or the shared services function you built at Facebook or the CX function at Disney, three very different kinds of operations. But you apply them more as a, hey. I’m a business manager. What what are we trying to accom what is the strategic intent? What is the vision of this function? What are we trying to do here? And that vision statement or that ambition, laying that out early enough in the transformation, for example, at c at Disney. It was a very simple mission statement that and you guys were supporting us then. Our ambition was we wanted to be the best CX program on the planet. It becomes an inspirational thing for the teams to rally around. It also then begins to paint a picture of we are not here simply to address average handle time. I mean, that would be the myopic view of a functional leader looking at just the contact center. The ambition was, hey. We’re gonna deliver outstanding exceptional experiences. At the same time, we’re gonna support the p and l of the business by reducing the cost, but we can impact the p and l by also doing cross sell and upsell. So one thing I would say to everybody listening is do not approach operations as a pure function, supply chain, CX, whatever that might be. Become a business leader. Try to figure out how this function can be accretive to the overall business.

[00:09:45] Scott Kinka:  I love that. And I I probably you know, public service announcement should have at least said or at least asked our relationship your relationship with Bridgepoint goes back to Disney. One sentence on that just so that we can at least ground people in our shared experience.

[00:09:59] Arun Chandra:  Yeah. I mean, the the you know, we were leveraging Bridgepoint to be able to help us with the various implementations, design and implementations of the NICE platform. And, you know, and Bridgepoint was a was a wonderful partner to work with, um, so on different dimensions of the program. Fantastic.

[00:10:16] Scott Kinka:  I do wanna explore that kinda coming into a new role because, like, you know, even having gone through your resume, and it all seems to knit well together when you look at it, but you pointed out, you know, so appropriately that you were in large operational roles that, frankly, were each very different in terms of which portion of the pie you owned. So each one was unique. Each one is its own snowflake. Right? To a to a listener who may be heading into a new job, leading a new transformation. You know, you just gave them how to look at it, but where do they start? What’s the step one first day at the desk in your mind for somebody who’s gonna either take on a new role over an IT organization or take on a new role leading change?

[00:10:58] Arun Chandra:  First thing, I mean, this would be the quintessential classic answer, listen more than you speak. You know? So if it’s literally the first thirty days, first forty five days, you know, your your job as new leaders, uh, or leading a new program is to listen to the various stakeholders, to the people who are driving that work or to customers, to partners, whoever is the right set of stakeholders to begin to develop a picture of what the reality is, what the aspirations are. As you’re building that knowledge base, then you start developing a point of view. Mhmm. And that’s the point of view I spoke about earlier. What do you want this function to become? What what’s the aspiration? What will get you a seat at the table? Because oftentimes in operational functions, you one of the things I hear or I used to hear more often was, hey. I I need a seat at the table. Decisions are being made for the business and the operations is not being the way you get that seat at the table is that you think like the business leader along with the rest of the people around the table.

[00:12:00] Scott Kinka:  Let me paraphrase. Don’t go in with preconceived notions per se on your playbook, quote, unquote.

[00:12:06] Arun Chandra:  Don’t go in the preconceived notions. Do a lot of listening. Paint a bold, broad vision. Yeah. Because once you know what’s what’s the North Star, and then you have to obviously start chunking and deciding where do you tackle first. There’s a classic sort of how many things will you approach at the same time. That’s all the, you know, the the project planning program planning that comes after that.

[00:12:30] Scott Kinka:  Makes a lot of sense. So so maybe this is a good opportunity for a transition. Um, speaking of doing before thinking. Right? Let’s let’s shift to AI initiatives. Because I think there’s not a CIO or director of IT or, you know, IT leader of any kind who’s listening to this, who who is not getting pressure from the c suite or the board of directors to just do AI, whatever that, you know, may mean. Um, and we’ll get to that in a minute. But from your perspective, what do you think the biggest shift is that organizations need to make if they want AI to become part of how the business actually runs rather than just another tech project?

[00:13:09] Arun Chandra:  Yeah. I think this, uh, ties back a little bit to thinking big. I’m sorry. I will continue to emphasize that because one of my favorite phrases is, you know, go big or go home. So for AI transformation, funny, I’ll be very candid. The way we are thinking about it, the way we’re driving the program within NICE is that we wanna make AI the new operating system for the company. It’s literally it’s not like I’m implementing a tool or a use case. Yes. We are doing that too, but you have to fundamentally think the the big vision is that AI becomes at the center of an operating system that we use because it is not a tool. It is basically knowledge and intelligence on tap. And so then you gotta start weaving that into it’s no longer, you know, an ERP system that is implemented to design a workflow. Yes. We need to go absolutely do some of those things, but reimagining how work gets done, how you operate as a company. And these are not I mean, they would be very super difficult things to start approaching. But at least my approach is all that let’s put that out there as the North Star and then see how much closer how can we keep getting closer to that. So so that is how I would think about the internal AI transformation is really as broad as you can imagine it to be. And then from there, then you come up with projects that you do to support that vision.

[00:14:24] Scott Kinka:  Let’s talk about preparedness. Um, you know, because I think there’s a lot of big ideas, and sometimes maybe the right objective is just to get some AI literacy and let the business tell you what it needs. There’s there’s various approaches on that. But, usually, the business isn’t prepared in other ways, data, system, security. They I think in particular, data. Right? Where have you found I mean, you’re leading not only operations, but AI transformation, right, at NICE. You know, as you’re thinking about engaging with the customers that you’re engaging with, where are they in their preparedness to even being able to quote unquote turn on AI? You know? And and where do they really need to be thinking about as the foundational steps for getting moving?

[00:15:07] Arun Chandra:  The short answer, because this is a much talked about topic that I get involved into, uh, partly because of my Disney days, but the short answer to your question would be that the AI solutions, that the value that they deliver, as we all know, will be only as good as three three things that that that’s fed into it related. One is just like shared data, both structured and unstructured data. Second is knowledge, you know, and that knowledge resides in many, many places inside an organization. It’s beyond the data, just the knowledge. And then the third is tying all that together. What is the context the operating context of the organization, which comes from, you know, some of the unstructured data like emails and and and so on? Who reports to whom? You know, who has a thorough decision rights over certain things, etcetera, etcetera. Because if you want agentic AI to go build, replace a whole bunch of stuff that can be built, it requires those three things. How are organizations prepared? Generally, not as much. I mean, this is a well known fact. I mean, even when we did back in the day when the focus was doing systems systems implementations like an ERP system, even back then, the same discussion used to take place, but it was more data centric. Now, today, it’s data centric, knowledge centric, and context centric to be able to provide the get maximum value. I think in general, organizations tend not to have spent too much time on this body of work, so I think there’s a lot of work being everybody stepping back to saying, okay. It’s not just the investment to be made in an AI tool. Ongoing recurring cost of the tokens, which itself is on the challenge we can talk about. Third is I need to get my data prepped and cleaned and my knowledge organized in a way that can be used. Um, so I think there’s a there’s a fair amount of pressure, and that is what the primary reason is why there is this big valley of you know, between POCs and true enterprise wide deployment of AI.

[00:16:55] Scott Kinka:  No. I agree. I mean, there’s so many stats out there about failed POCs and things of that nature. Generally speaking, my feeling is the failure isn’t the failure to execute on the things that you just talked about, right, or in having a true north star for the project itself.

[00:17:09] Arun Chandra:  And that took see, even for AI, because there’s so much c you know, there in many cases, it was sea level pressures. It’s go do something that led to token maxing, and then people suddenly realize, oh my gosh. That’s not a great thing. We’re we are basically all we’re doing is supporting the the Frontier Labs with more revenue. So then there’s now there’s a tremendous push towards looking at open source models or having a choice of models with a model selector depending on the task at hand. So there’s a lot of, you know, obviously, rapidly changing evolution that is happening. But at the same time, I think those who are trying to get to true scale of AI at enterprises have to grapple with the data knowledge and context conversation. They have to grapple with change management of their people, which we don’t talk about often enough beyond just the data and knowledge. Ultimately, you’ve gotta build in. AI is gonna coexist with humans. You gotta have a change management process with the human side. And then, ultimately, you gotta be clear as to what you’re trying to produce from the program. Is it impacting top line? Is it impacting bottom line? Are there measurable goals? Are there reasonable goals? I think that that kind of thought process is what’s separating our people who’ve deployed this at scale, uh, those who are still in the POC phase.

[00:18:19] Scott Kinka:  Folks in the POC phase, I often say to them, in the absence of the true path to an ROI and, frankly, an agreed around structure with the executive team and in particular, the CFO and how it’s gonna be measured, right, um, that AI, at least for the next couple years, is a cost center. How do you feel about that statement?

[00:18:38] Arun Chandra:  I think it is an investment center. Okay.

[00:18:41] Scott Kinka:  Fair.

[00:18:42] Arun Chandra:  I’m not not trying to be, you know, sort of too semantic here with words, but if you think of it purely as I’ve always thought of technology as an investment. Yeah. So we invested heavily in technology at Disney to modernize the CX platform. That was you know, it was an investment because it produced value elsewhere on the p and l, either in top line or in the cost structure so that it impacts the bottom line. Yeah.

[00:19:06] Scott Kinka:  I think it’s a little bit, and I wanna say explore a little bit about customer experience at AI in a moment. But before we dismount from that topic, there are, however, measurables in the contact center. I mean, you mentioned it earlier whether it’s handle time or answer time or, you know, outcome rate or something along those lines, subscriptions sold. How do you think about just kinda sheer employee productivity around use of generative AI in areas of the business where they’re generally knowledge workers? Do we have the HR and the organizational, uh, fortitude, frankly, to start measuring by productivity as opposed to hours worked?

[00:19:42] Arun Chandra:  It depends on the work. Like, I mean, if you’re a consulting organization, you’re very much in tune with in the measuring hours where people are spending time. In many other industries, you don’t have I mean, if you’re on the shop floor, again, there’s probably time motion studies being done so that they can but in the most of the knowledge workers, there is no such tracking that is being done to sort of measure hours spent and so on. So I will defer that to our HR colleagues as to what they can measure and how they should measure it. But here’s what I will say. We should absolutely be expecting a higher level of productivity given this intelligence that is available to us on PAP and the agentic frameworks to go with it. It will be very dependent on role types as to how much productivity you can get and, therefore, how much cost avoidance you can get. You know? And then we start getting into the very familiar ground, Scott, which is okay. So the more repetitive tasks are okay, low value tasks, you can automate them faster, which has been, you know, what technology has been doing for the longest time. So my short answer from my point of view is you should have reasonable, measurable goals of productivity, which will vary by function and by team inside the organization. It is not a one size fits all answer. It is not something that should be driven purely from a finance perspective because what may apply in sales may be very different than what applies in services and customer success, for example.

[00:21:02] Scott Kinka:  Totally agree. So let’s let’s focus on customer experience then for a minute, and I’ll use that segue. Um, you know, you’re now in this role. You’ve we just said you’ve been in multiple operational roles. The last one was on a customer experience, um, you know, set of work for a very large customer centric company, obviously. You’ve now come over to NICE given that experience. You’re tasked with transforming, really, leading the AI initiatives as well as, you know, operations. Let’s just hone in there. How do you see the customer experience business, if you will, changing over the next three or five years as it relates to AI? Are we chasing the replacement of the agent? Are we chasing the superpower of the agent? Is it some mix? Like, what’s your look into your crystal ball and help us figure out what customer experience looks like over the next three to five.

[00:21:51] Arun Chandra:  I mean, without question, in the next three plus years, we will be managing or brands will be AI agents and human agents. I mean, that that is a given. And so then it becomes a matter of how are you orchestrating and harmoniously managing the coexistence of AI agents and human agents? What are the role types? What you know? You mean, like, for example, those of us who are used to doing workforce planning, you know, your workforce planning tools should be able to accommodate the ability to schedule AI agents and the human and human agents. So I do think that that the biggest change that’ll happen is going to be that coexistence. It is happening today. You can see it. People have to start thinking of it a little bit more systematically because, you know, like, just like you have cost of wages with a human agent or human, you know, who’s doing some work, you have cost of tokens attached to an AI agent. So there’s an OpEx line item for that. So, again, put that strategy back into an operating framework that you can run. It will be one big change to be to be managed. I think the number of interactions will grow partly because there’ll be agent to agent communication. So you and I will have an agent tomorrow that if you have a billing issue or some other, you know, tactical problem, you’ll ask your personal agent, Gemini based or or, you know, OpenAI based, to go talk to an agent, you know, at at an airline or a phone company. And those will be unbounded in terms of number of interactions, if you would. So that, like, that volume will increase. So an agent to agent protocol will have to emerge. I think the third thing that will change is it will be you know, think of that in the role of a human agent could become one of a supervisor where you’re supervising a dozen AI agents who are doing work for you. You will see a future in our mind where you will find that even AI agents can clone themselves going forward. So while, let’s say, an AI agent has you interacting on a phone conversation in pretty realistic terms, it is now unable to solve part of your problem while it keeps you engaged. It makes a copy of itself and goes off and gets help so that the contact is not broken, for example. So the I think the what is the art of the possible will change dramatically, and it will be a blended model. It’ll require organizations to be super clear about, again, strategic architecture and financial architecture that you’re chasing and learn how to manage humans and AI agents and human population. By the way, not just in contact center. I think this to be true for the nature of work.

[00:24:22] Scott Kinka:  Super interesting. Um, you you keep mentioning tokens. I do wanna focus on that for a minute. I give this speech about waves and technology very often around the country. One of the things I say is we don’t yet understand the currency of AI. And people look at me a little bit funny, but, apparently, we didn’t understand the currency of cloud in kind of hours worked on servers and things of that nature. We didn’t understand the currency of of unlimited local and long distance in UK seats and told what told Freeman and all. We didn’t understand the currency of how much bandwidth we were gonna consume if we were successful for using. Eventually, we get there. But if I take those other three or four waves, the answer in many times to the currency debate was that the cost of the item degraded in price, frankly, over time, the widget, the minute, the cycle on of server. Okay? And then suppliers and providers ultimately bundled them and got the customer out of worrying about currency. Again, it became a supplier to provider a problem. Right? I’m seeing increasingly the big models move away from caring about the UI and looking more and more like service providers to the application platforms, like NICE as an example. Just put all that in perspective for us. Do you see this or do you is the end user going to constantly be fighting the token battle? Do you think we’re if this is gonna settle down into some kind of realistic and explainable volume around what an agent could consume? How do you see all of that going? And when do you think it begins to settle down a little bit so we can get cost predictability as we’re chasing some of these, you know, more agentic workflows?

[00:26:03] Arun Chandra:  If I knew the answer to your last question, I would probably be in a different profession altogether.

[00:26:08] Scott Kinka:  Well, I had to ask.

[00:26:10] Arun Chandra:  Of course. Only I would be then in the prediction markets on, you know, on Clashay or on Polymarket. I’m doing a bet on where how soon anyway anyway, how the Frontier Model business will evolve. And you said all to your point, no question about it that we are very, you know, we are in early days of the currency conversation. The difference in this cycle compared to previous cycles is the speed of transformation and change is like none ever before. And partly it’s because it’s built on the back of all the previous innovations that have come. The amount of dark fiber that was laid during e commerce days, the cloud computing solutions that came about in, you know, in the early two thousands, the amount of, you know, um, you know, build out that has happened over time from the hyperscalers of capacity, that is what allows us to engage with the chat GPD and get near instantaneous voice response and, like, near instantaneous. I think we are early days of that, but the trend line is very clear. It is commoditizing as fast as it became innovative and relevant. It is just like sheer pressure. And I think because of that, the three layers, the infrastructure layer, the model layer, and then very simplistically, if I was to just put the, you know, the application layer on top, it is still being debated very actively in the market as to where the real value is going to accrue and which part of that stack, if you would. Right? And that ties back to your question. If the token price is gonna continue to drop dramatically, as is the post most likely scenario with, you know, even US based open source providers like Thinking Machines, NVIDIA pushing forward safe, secure open source models, Microsoft embracing that approach, suggests very clearly the token cost will come down. That means the Frontier models will have to find other revenue sources. They’ll have to either move up the stack or move down the stack and do something different to protect current valuation. But, again, those are I mean, there are people who are much better equipped to answer those questions, Scott, than I am, but at least that’s my point of view.

[00:28:07] Scott Kinka:  You would at least agree, uh, I don’t wanna put words in your mouth. I’m asking that this it’s not like this hasn’t happened before, this currency debate. Like, this is pattern oriented. What’s different about it?

[00:28:17] Arun Chandra:  The difference is the capability of what is being made available to us is never before have we seen this level of capability. And two, the speed at which that capability is becoming available, becoming redundant, being improved upon, prices changing, that speed is just I mean, I don’t think I’ve ever seen that before in my entire career.

[00:28:38] Scott Kinka:  Yeah. It’s amazing. With the amount of time that we have left, I’m gonna hit you with some fun questions, if that’s okay.

[00:28:43] Arun Chandra:  Of course.

[00:28:44] Scott Kinka:  What’s the most contrarian opinion that you have about enterprise AI adoption that you think others in the industry might disagree with?

[00:28:54] Arun Chandra:  The benefits will come slowly and steadily with very disciplined execution.

[00:29:00] Scott Kinka:  I like that. Alright. Um, what are

[00:29:02] Arun Chandra:  you reading right now? Anything I can lay my hands on for Let it go. Yeah. Like, just trying to keep up. And it’s actually a little bit less reading more podcasts because you’re trying to work out and trying to catch up at the same time. So a lot of, like, different podcasts to stay up with what’s happening in the world of AI, both strategically and financially in addition to, you know, somewhat technologically.

[00:29:24] Scott Kinka:  Yeah. I love that. Alright. This is a fun one. I’ve asked every guest since the beginning of this podcast. We’re going on, like, I think, the hundred and fifth or a hundred and sixth episode on this thing. I’ve asked them all the same question. Alright? I’m a sci fi nerd. You can choose whatever dystopian future you choose, but something occurs, and you have one app that’s still functioning on your mobile device. Alright? Which is your app choice?

[00:29:49] Arun Chandra:  Well, anything that can get me in touch with my family.

[00:29:52] Scott Kinka:  Okay. I like that. That’s a good one. Anyone in particular? What’s your favorite comms app?

[00:29:58] Arun Chandra:  I mean, we’re just like you know, we’re kinda like boxed into using iMessage most of the time or WhatsApp. Well, I guess, one of the two, but anything that I mean, at that moment in time, you know, being in touch with your loved ones becomes more important than anything else.

[00:30:12] Scott Kinka:  You are true. And, again, it I guess it depends on which dystopian future you’re choosing for your sci fi movie. But, indeed, I get asked that question all the time. People are like, well, what what actually happened? I’m like, it’s your movie. It’s not mine. You choose. Alright.

[00:30:23] Arun Chandra:  In the spirit of this, let me what would be what is your absence? You’ve asked this question so many

[00:30:27] Scott Kinka:  I usually have a tendency to think that’s in whatever future that is, I’m alone. Um, so it’s usually Spotify. Uh, if I’m I just need something to occupy my time.

[00:30:37] Arun Chandra:  That’s a great one. This is a

[00:30:39] Scott Kinka:  a fun segment we do when we call this or that. Alright? I’m gonna give you a you gotta give me a choice with no context. Right? And and then I may ask a follow-up question. Alright? You ready?

[00:30:50] Arun Chandra:  Yep.

[00:30:51] Scott Kinka:  Alright. World Cup or Super Bowl?

[00:30:54] Arun Chandra:  Super Bowl.

[00:30:55] Scott Kinka:  Alright. Window or aisle seat? Window. Disney level storytelling or Silicon Valley speed?

[00:31:05] Arun Chandra:  Disney like storytelling.

[00:31:06] Scott Kinka:  I love it. Chad or Gemini?

[00:31:09] Arun Chandra:  Gemini.

[00:31:10] Scott Kinka:  Build or buy? Depends. I love it. I love the hedge. Alright. You could give me a sentence on that. I’ll let you clarify.

[00:31:19] Arun Chandra:  That depends is that you don’t wanna take AI and rebuild stuff that you know, traditional classic stuff that is needed, like a system of record that is just needed to be able to manage things in your organization. If you have innovation dollars, I would put them in innovating towards business goals versus repeating what is actually can be built.

[00:31:37] Scott Kinka:  No. I love that. I love that. Alright. Well, that was this or that. And now we’re gonna give you one more segment we’ve been doing. We call snippable moments. Alright. I’m gonna ask you some questions. Some of them may have some overlap with things that we already said. That’s okay. What I’m challenging you to do is answer me in a sentence. Right? And, again, I may clarify, but let’s see how we do. Alright? Where are organizations overestimating the value AI can bring today?

[00:32:04] Arun Chandra:  It’s in the efficiency that they can get, the amount of headcount that they need. I think there’s over estimations going on in terms of, uh, immediate productivity or versus over time.

[00:32:14] Scott Kinka:  Gotcha. What is the biggest misconception executives have about involving AI and customer experience?

[00:32:23] Arun Chandra:  Mostly a fear of hallucination that still exists at some level depending on, you know, the brand of the company. Um, so I think that would still be the little bit of hesitancy that people have is, uh, an AI agent going rogue.

[00:32:35] Scott Kinka:  How do you know when an AI initiative is actually creating business value?

[00:32:41] Arun Chandra:  Like any other initiative, doesn’t have to be AI initiatives. Do we have the right metrics that we assigned before the program started? Do we measuring it as a true business drivers? Yes. If you have the right KPIs, which are business grounded, then you will know you’re making progress.

[00:32:54] Scott Kinka:  Love it. Last one. What leadership capability will matter the most for IT executives over the next decade?

[00:33:03] Arun Chandra:  Empathy and EQ. I love it.

[00:33:06] Scott Kinka:  You’re pretty good at this game, Arun. I’m not not gonna lie. Usually, I get, like, some people I gotta I gotta qualify that statement. I love it. I love it. This was incredibly enlightening. Uh, first, thank you for your partnership. Obviously, it’s very clear, Bridgepoint and NICE have a partnership together, but also your belief in Bridgepoint over, you know, multiple, uh, relationships that we’ve had through your time back at Disney. So I appreciate that. Love that we’ve got to spend some time together, and this was super enlightening for me. For listeners who wanna learn a little bit more about NICE or they wanna connect with you or learn a little bit more of your time there, what’s the best way for them to do that?

[00:33:40] Arun Chandra:  I mean, to learn about the company, go to our website. And if you wanna connect me, LinkedIn is a good option.

[00:33:45] Scott Kinka:  Fantastic. So finally, if you had to summarize your leadership philosophy for executives that are navigating AI driven transformation in one sentence, send us off with a nugget.

[00:33:59] Arun Chandra:  Build a bold vision and execute relentlessly with empathy.

[00:34:02] Scott Kinka:  I love it. Somebody should put that on a coffee mug. I might. I like that. I just wanted that facing out so people see it. This has been amazing, Arun. Thank you so much for your time.

[00:34:13] Arun Chandra:  My pleasure, Scott. Thanks very much. I enjoyed it.

[00:34:15] Scott Kinka:  You got it. And that is a wrap on this episode of the Bridgecast. If today’s conversation gave you something to think about, I know it gave me something to think about. Please hit subscribe. Leave us a review. Five stars. Share it with your team. We’ll be super excited to see you on a future episode. Thanks so much. Listen. If you made it this far, there must have been some good content, or you’re just a fan or a relative of mine. In either case, we appreciate you spending your most valuable asset with us, your time. We don’t take that lightly. We’d also like to say one more time that we’re appreciative of Bridgepointe Technologies and their belief and sponsorship of this show. We hope that if you or someone you know is thinking about your company’s digital transformation or simply the next IT project that you may not have the resources, budget, or time to get to, I’m sure that Bridgepointe, one of the country’s fastest growing technology advisory and procurement firms can help. Check out bridgepointetechnologies.com. Don’t forget the e on Bridgepointe, or simply reach out to me at skinka, skinka,@bpt3.net. Also, take a minute, please, if you would, to give us a five star review on your favorite platform. It helps give us the visibility to reach other people like you. Thanks for listening to this episode of The Bridge. I’m Scott Kinka. And until next time, there’s a lot of noise out there in business and in life. Do what you can to be the signal.