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Artificial intelligence is changing the way real work gets done. But big ideas don’t drive change. People do.
The ones who roll up their sleeves, modernize data, and bring AI to life where it matters most. In the workflow.
This is for them. For you. The visionaries. The innovators. The leaders turning potential into performance and pushing their organizations forward.
Everyone’s talking about the promise of AI and what it can do. On this show, we’re talking about making it real.
Learn from the experts who are driving it forward and walk away with everything you need to bring AI to life in your organization.
[00:00:00] Carolynn Smith: step one before you even get started is you've gotta assess the value that you're deriving. You're doing this for value, right? So really anchoring to three or four baseline metrics, of what exists today and what are your aspirations from a business outcomes perspective, having that clear delineation.
[00:00:17] It's kind of like losing weight, right? Like you know what you weigh, you know what you wanna weigh. And if you could have a very clear distinction in the key performance indicators from a value perspective, you can do your analysis, generate your business case, and then get going.
[00:00:33] Shirley MacBeth: You're listening to Make it Real,
[00:00:35] brought to you by EXL. I'm your host, Shirley Macbeth, and on this show we're exploring how artificial intelligence is reshaping workflows, industries, and the way real work gets done. And yes, we're going to make it real.
[00:00:53] When we talk about AI adoption in the enterprise, there's one group that plays an essential role in AI adoption, and [00:01:00] that's the frontline employees who will be using AI technology in their day-to-day workflows. Joining me today to discuss her AI journey is Carolyn Smith, head of US Service at Prudential. Carolyn has helped grow Prudential's AI capabilities across the service organization, taking AI from idea to scale with a focus on leveraging AI to improve outcomes for Prudential's employees and customers and advisors. Together we'll talk about how AI is evolving the roles of human employees, the requirement for continuous evolution, and how to put the focus on tracking AI outcomes rather than just on AI use. So a lot to unpack there. We're really excited to jump right in. So Carolyn, welcome. So glad to have you with us today on the podcast.
[00:01:47] Carolynn Smith: Thank you, Shirley. It's a pleasure to be joining
[00:01:49] you. you. So we'll jump right in. So, Carolyn Prudential is really focused on driving AI adoption for its frontline employees. Can we start with an example of what you're doing with [00:02:00] AI and why was Frontline an adoption really a priority for you?
[00:02:05] Yeah, certainly Shirley. And as you mentioned, I have the honor and the privilege of leading the US service operations team within our US businesses at Prudential, and our focus has been on how do we deliver an improved employee experience. Customer experience and, uh, operating outcomes that really enable us to show up in key moments that matter.
[00:02:27] So AI is something that we look at as one solution of many that's being embedded in our service journeys with the intent to get those specific outcomes for our company.
[00:02:38] Shirley MacBeth: Amazing. So tell us a little bit more about how you were started bringing AI into this, the service organization and some of the challenges that you were trying to solve.
[00:02:49] Carolynn Smith: Yeah, certainly. So when you think of ai, we don't. At Prudential, specifically in the US Service organization, we don't think about AI as something [00:03:00] additional to what, um, is a tool that our current employees use. We think about it as being embedded in the service experiences we deliver. And the important component to all of this is making sure our frontline employees are actively engaged in the design.
[00:03:16] Because you can have fantastic models, you can have great orchestration. It's only as good as how you integrate it into the workflows, and that may very well be redesigning workflows. Um, but it's ensuring that we understand the details and the dependencies of what enables our service professionals to deliver to our customers in a way that gives them accurate, reliable information in the most effective way.
[00:03:43] So our employees are an integral part to the design and the implementation, and ultimately the adoption that gets us to the desired outcomes that I mentioned to start.
[00:03:52] Shirley MacBeth: so so much sense as the employees are really the ones on the front lines trying to solve those problems. Can you give us an [00:04:00] example of what you heard from your frontline employees early on for how they felt this could help with their experience journey for clients?
[00:04:08] Carolynn Smith: Yeah, so I mean with anything, when there's process change or technology change or a new tool that becomes available, there's the natural trepidation, but it's important that you quickly identify your change agents because you'll have different spectrums of employees. Some folks that wanna lean in immediately.
[00:04:25] Some folks that have resistance and change management with. AI and any other technology tool is real. It's, it's something that cannot be underestimated. You cannot unlock value at scale unless you drive the adoption and care for the spectrum of feelings and sentiment toward the tool. So you'll get the spectrums of both.
[00:04:48] but how you manage through that to upskill, to train, to identify the value. it was important for us within the US Service organization at Prudential to be very transparent [00:05:00] around what we needed AI to do and what we needed AI not to do. Because there are unique things that our employees bring to bear when it comes to empathy in the key moments that we serve and meet our customers that still have to be honored and treasured because that is really what differentiates our service experience.
[00:05:17] So it's the best of both worlds. That becomes important, but change management cannot be underestimated, and the voice of the employees becomes very, very important in that journey.
[00:05:27] Shirley MacBeth: love that and I, let's double click a little there. 'cause I think that's something that has been so integral to the success of your deployments in involving the frontline workers. Were there things that you heard that were a surprise or that they said that leadership, you know, you went in there, you had an idea of how this would go, and then with. The feedback of the frontline employees. Was there any surprises or any, uh, insights you wanna share there?
[00:05:50] Carolynn Smith: Yeah, I mean, Tru, truth be told, the leadership team can't do it without the frontline engagement. They know the details. They know the pain points. They know what our [00:06:00] customers want. So this was not a top down. this was as much of establishing a vision to achieve the desired outcomes, but also that bottoms up engagement of how do we get this right if we're driving for high quality interactions with efficiency.
[00:06:16] What are the pain points? What are the process opportunities and how do we redesign this? So it's not just about thinking about the AI integration, but the process end to end. So think about it from a value stream perspective and they will tell it like it is. They know where the opportunities lie. the details in the case, of deploying AI is what matters because you fully have to appreciate what information, what data needs to be available and when, um, to effectively serve.
[00:06:45] And we were very much focused on not just doing this for the sake of deploying use cases, but delivering. Uh, better experience, better financial outcomes and delivering, on our commitments to get this to full scale. So they very [00:07:00] much bring forward the facts and the truth around what's working, what's not.
[00:07:04] And this is about deli iterations. So as you're deploying AI in your service organizations, you have to iterate. Your first launch isn't gonna care for everything, but how do you continuously learn from the deployment, tune your models, expand them to get full integration across your value stream? And that has been truly their voice and their involvement in the design.
[00:07:27] Implementation has really been one of the key components to success.
[00:07:31] Shirley MacBeth: That's amazing. And I love the idea of iteration and fine tuning and, you know, learning and deploying. And you, you had said that, you know, there's those sort of. Early adopters or p voices within there to cultivate and bring others along. Can you talk a little bit more about that, you know, with, with some of the voices that you heard and how maybe it, it became more of, um, the more it deployed and you've got the traction from some of the adopters within the team, how others followed?
[00:07:59] Carolynn Smith: [00:08:00] it's a great question. I, I mentioned transparency, but I would say equal to transparency and communication is aligning your rewards and recognition program to it. So when you're starting to deploy. AI and other technology capabilities within your experiences, understanding your objectives and your desired key results of those objectives.
[00:08:21] it is important that all levels in your organization know what success looks like, know the baseline starting point and really what winning is defined as. And that is in your quantifiable metrics. those metrics both at the team level, in the individual level create an interesting, um. I liken it to your gamification, right?
[00:08:41] When, when folks know what success looks like, they want it. and it's important that you drive accountability. You reward and recognize role model behaviors when it comes to adoption, role model behaviors when it comes to continuous improvement, and really demonstrating how our investment in this [00:09:00] technology is ultimately resulting in value to our customers.
[00:09:04] That is a very important component to this. So the public reward and recognition, the objectives and key results in driving accountability in your culture is really what can give you that step change. People will speak up more and get more engaged when they feel recognized, when they feel heard, and they feel
[00:09:21] Shirley MacBeth: I think that that is great. I love some of the words that you said there around aligning rewards and, uh, recognition and uh, and accountability and so that definitely sounds like it was a change in your KPIs, you know, sort of to drive the behavior you want, you have to change how you measure and how you end.
[00:09:38] Sent, and if you could, uh, go even a little deeper there. Is there, you know, something that, rose to the top that you're now measuring in a different way than you did before?
[00:09:48] Carolynn Smith: Yes, measurements. Uh, I like to say consistently in my organization, we have to measure what matters and what matters evolves over time. we all know customers expectations every [00:10:00] day continue to rise. So speed of delivery, accuracy, quality. It matters. And those are core to your service levels and your key performance indicators.
[00:10:11] You mentioned KPIs, but you really have to look at things like containment. You have to look at things like accuracy and resolution time. Things beyond that of your traditional service metrics to ones of, um, that really take into account your new way of working. as I mentioned to start this podcast, it's not just about deploying AI on your existing workflows or your existing, ways of working, but it's changing over time.
[00:10:39] So you have to have a control mindset. You have to make sure things accurately are happening. We're augmenting how people are doing things. within Prudential and specifically the US Service organization. Responsible AI is very, very important to us. So that brings with it a new sense of control. You can [00:11:00] only, manage what you can measure.
[00:11:01] So it is incredibly important in the infancy phases all the way through to fully operationalizing ai. Are you measuring what matters? Are you controlling it? And then more importantly, how do you know? So what metrics are telling you that you're getting better, that you are well controlled, and that you're being responsible about ai?
[00:11:20] Shirley MacBeth: Absolutely. And I, I, for others listening on the podcast, what does responsible AI mean within Prudential? And obviously you're in a very regulated industry and there's all sorts of things you need to track and, and be accountable for. Can you tell us what that means within your organization?
[00:11:35] Carolynn Smith: Yes, it's very, very important, um, to Prudential that we ensure that we are highly responsible for our data, that we are protecting the privacy of our customers, and that we have humans in the loop for decision making. So there's some of the core foundations and pillars to how we operate. Again, this is about driving toward experience, experience in the form of employees and customers.
[00:11:59] So that is [00:12:00] making sure what is uniquely human, that we are doing the best of that, and that we are removing some of the non, I shouldn't say some, all of the non-value add, uh, tasks that ultimately create the experience so that our people can focus on what is most valuable. Um, to our customer. So it is at the core of how we show up.
[00:12:21] You mentioned a highly regulated organization. It is very, very important to us, and it's at the core of how we build and then how we deploy and fully operationalize our
[00:12:30] Shirley MacBeth: That's great. And, um, you had talked earlier about empathy and that being a big part of your, the experience that you're delivering and where humans play the most important role and maybe where tech, takes a backseat and that you're enabling your teams to focus more on things that are value add and, and, and deliver that empathy and that brand experience.
[00:12:51] Can you talk a bit more about, uh, empathy as a word, uh, for you and what that means, uh, within the context of how you've deployed ai?
[00:12:59] Carolynn Smith: [00:13:00] Yeah, I mean, let's, let's take the question and like flip it just slightly on the, on the inverse of when you're attempting to provide empathy and key moments that matter. It can be when you're retiring, it can be when a death happens in your family. What you don't wanna do is have a service professional that has to navigate multiple different systems, has to look for information.
[00:13:22] Um, what you want them to do is demonstrate compassion. And we have to make it easy for them to do that. And that means that we are orchestrating an experience that. Proactively pushes information and data that is relevant and unique to the individual we're speaking to so that we can most effectively serve them and be be there for them in key moments that matter.
[00:13:43] They could be moments that are exciting, like your retirement, or they could be moments where you're going through, um, a difficult time in your life, like a death. So it is incredibly important that we are offering experiences and enabling things through data. That is [00:14:00] timely and accurate, um, so that our service professionals don't have to exert energy and effort to be able to, to give the customer the very best possible experience that they deserve.
[00:14:10] Shirley MacBeth: I think that is exactly what we would all want as we're calling and talking, you know, in these moments that matter, uh, the human side of us as consumers, uh, as well. You can, you can see where that is changing that. Experience experie and also helping your employees do their jobs better and feel better about that experience that they are providing.
[00:14:30] I think it's very exciting. when you look back at your AI journey, did you think that you would be here now? Uh, uh, as, as far as, as far along and delivering, you know, I'm curious to that journey and maybe some of the surprises along the way.
[00:14:44] Carolynn Smith: it's amazing and anyone tuning into this podcast would would likely agree with this statement, but AI has evolved so much in the past 12 months. I mean, when we think about just last year when you were integrating generative AI into your workflows, I mean, we are at the place and it [00:15:00] is here and now.
[00:15:01] Where AG agentic has the opportunity to do things that I never imagined even possible. Now how you do that and how you orchestrate it with data and a well sound architecture matter. So it's an exciting time. I, I can't say I, a year ago, I would think we would be where we're at now, but it's a time in everyone's life that if you aren't embedding AI in your personal and professional life, you are truly missing out and likely falling behind because it really does create the space regardless of what industry that you're in, to do things that are much more meaningful and impactful.
[00:15:39] to advance whatever your remit is. And in the case of mine, it really is about delivering transform service experience so that we can keep up with the evolving expectations that customers have. And I don't expect those expectations, um, to slow down because we are all, in fact benefactors of improved experiences and quick resolution of
[00:15:59] Shirley MacBeth: I [00:16:00] think the expectations, uh, certainly go up as people cer, see what is possible. And I think to your point, the, the technology is moving faster than ever and, you know, to keep on that journey, you're not done. The AI journey keeps evolving as, as we would think about it. I. When you look forward, where is this AI journey, um, headed for you and where you've got a, a roadmap and things that you're looking to achieve and, you know, have
[00:16:24] even greater levels of customer experience?
[00:16:27] Where do you, are you looking next? Is it, are new, new capabilities? Is it, you know, improving what you already have? What's ahead for you?
[00:16:36] Carolynn Smith: This is about, reshaping how work gets done. So, a year ago you would think about ai, um, as integrated and embedded into an existing workflow to create efficiency. We're at a place now where you can take a step back. And think through and reimagine how work gets done.
[00:16:55] that's an important differentiator when you think about scale and outcomes. [00:17:00] So going back to measuring what matters, there's real unlock to be had, but you can't think about AI and isolation of your architecture, your data, and your process. They really do go hand in hand, but that is the pivot we're at.
[00:17:14] You can get incremental change or you can do significant step changes in your transformation, but it comes back to. Our customers and employees are at the center of everything that we do. We are incredibly responsible about deployments and that continuous learning to iterate and refine, making sure that humans in the loop, are where they need to be and that we're doing so responsibly, but.
[00:17:36] Um, the future is incredibly exciting. It's gonna be, challenging, but challenging and fun. If you think about ai, as I mentioned, embedded in everything else that we're doing to really change and fundamentally evolve the way things get done.
[00:17:50] Shirley MacBeth: I love what you talked about as far as that opportunity with AI to rethink the whole way that work gets done. I love that and it's. It's so true. You know, at first AI [00:18:00] maybe is, you know, changing a little piece, but when you step back and you really rethink a whole workflow or a whole, experience and how that, that can be transformed, that's really exciting.
[00:18:10] You had mentioned data, so I just wanna pick up on that, uh, thread a little bit, of how you. Data, um, throughout, to really transform that experience. What role did, uh, you know, looking at your data and, uh, making your data AI ready play in this?
[00:18:26] Carolynn Smith: He has specific to the, to the US service experiences that I have responsibility for. All roads lead back to data, data that is accurate, data that is accessible and reliable. because without. Good, reliable data, you can't deliver. Personalized experiences and personalized experiences are incredibly important.
[00:18:49] So we've been on a journey, um, at Prudential to do just that. It's about building the building blocks so that you can have accessible, reliable, and accurate data to ultimately fuel your [00:19:00] experiences. You cannot, um, and there's plenty of white papers out there, deploy or integrate your AI models on data that hasn't been certified and mastered.
[00:19:10] In a way that can feel that. so we have been very, very deliberate and thoughtful in this journey, um, because again, personalized experiences for those key moments that matter are very, very important to
[00:19:21] Shirley MacBeth: Yeah, I think it's so foundational. that data foundation really drives everything. And it sounds like at Prudential you've done a lot of work to carefully get that right. And to then using that to deliver the personalized experiences.
[00:19:33] That's amazing.
[00:19:34] we talked a lot about, already about the change management and certainly the roles of the frontline employees as, as being part of that and embracing and being accountable and measuring what matters, all of that. We do a segment on this podcast that we call the shout out. And, um, when we really celebrate and recognize the human element and the change management that it takes. So Carolyn, I would ask you, is there a team or a group of [00:20:00] people, um, or functions that brought this all together that you would like to shout out as part of that, that piece of celebrating the human element.
[00:20:07] Carolynn Smith: Yeah, I'd be here for hours if I, if I gave the shout out to all the teams that I would've wanted to. But this is a real cross-functional effort. Um, so I've mentioned a lot about our service professionals, uh, but we can do nothing, um, without our various different functional teams that exist across Prudential, whether it's data technology, our control partners.
[00:20:30] Um, this takes a disciplined effort and it is a team effort. To ultimately deliver and scale something. Um, we've been very deliberate to ensure we select use cases, commit to them, and then execute. Um, because what you don't wanna do is get in a cycle of use cases, you wanna define where the value is, you wanna lock in, and then you wanna execute and measure it.
[00:20:52] Um, and I would say that is what the cross-functional teams at Prudential have done. So I can't, I can't do just one shout out for one team, but I will say the various [00:21:00] different teams that have made. Um, this service transformation happen, uh, deserve all the recognition. We have incredible, incredibly talented people at Prudential.
[00:21:09] Shirley MacBeth: to you and to the teams that you've brought together to make it happen. I think that is the shout out that it is such a group effort that, uh, is driving this forward, so congratulations on that.
[00:21:19] Carolynn Smith: Thank you, Shirley.
[00:21:20] Shirley MacBeth: Carolyn, I know a lot of folks are struggling with how to go from pilot to really scaling it. What's your one piece of advice that you would give to folks to really get going and, and, and drive solutions forward with ai?
[00:21:33] Carolynn Smith: step one before you even get started is you've gotta assess the value that you're deriving. You're doing this for value, right? So really anchoring to three or four baseline metrics, of what exists today and what are your aspirations from a business outcomes perspective, having that clear delineation.
[00:21:51] It's kind of like losing weight, right? Like you know what you weigh, you know what you wanna weigh. And if you could have a very clear distinction in the key performance [00:22:00] indicators from a value perspective, you can do your analysis, generate your business case, and then get going. So, commit, right? Decide on your use case and then execute.
[00:22:10] Execute in a disciplined way that helps you measure. What matters so that you can make decisions. If you're seeing value in certain pockets, you wanna double down there, right? And then as you double down there, you can identify ways to very responsibly scale it. That will help. So again, it constantly reminds you you're doing this with the intent to derive value.
[00:22:31] Value for us in the US service organization is experience and operational outcomes, from an efficiency perspective. So if you do that. You can measure what matters. You can fine tune your models, and then you get to a place where you have enough intelligent data to say you are effectively ready to scale it.
[00:22:47] So again, it goes back to commit, decide, execute, and measure becomes really important in the journey from going from use case to full scale.
[00:22:56] Shirley MacBeth: that's a great way to wrap up and I have so many, um, [00:23:00] pieces of advice that I've gleaned along the way from our conversation. So it's been, um, amazing. Let me try to summarize, and you can tell me if I've got this right, but I think what you just said around commit, decide, execute. And measure.
[00:23:12] I mean, that's sort of in a nutshell, the way that you've approached this in the US Service organization. So that's my takeaway, number one. I'd say, um, number two in the way that you've approached this, that you cannot, uh, unlock value at scale unless you really bring those frontline employees. In and help them be part of that process and part, you know, be accountable.
[00:23:32] So that would be another thing I, uh, would take away. Number three, I think your advice around how you have aligned rewards and recognition to driving that outcome, that just drives that accountability and then it really helps align folks on. What success looks like. Uh, so that's another thing I took away. and then I think the last thing I would say is what you said, if you're not using AI in your professional and personal life, you're missing out. And I think that is [00:24:00] something that is a good takeaway for us all to think about to, you know, you're falling behind and also maybe not taking advantage of some of the great opportunities that are out there.
[00:24:09] It takes so much, you said it creates so much.
[00:24:11] Face for you to do more value added things and to improve and drive things forward. So that's my final takeaway. So, Carolyn, how did I do as far as summarizing some of the points?
[00:24:20] Carolynn Smith: I think you recapped that exceptionally well and hit on the important highlights. Again, it all comes back to how you make your employees feel, how they feel valued and recognized, and how they're part of the journey.
[00:24:33] Shirley MacBeth: Well, what a great conversation and, um, a true pleasure, Carolyn, to have you on the podcast. I know you, there's a lot that people are gonna take away and learn from your success. So thank you for joining us and I wish you bets of luck
[00:24:45] moving forward.
[00:24:46] Carolynn Smith: Excellent. You too. Take care.
[00:24:47] Shirley MacBeth: Thank you. Thanks for listening to Make It Real. We hope today's conversation gave you ideas, insights, and inspiration to help bring AI to life in your [00:25:00] organization.
[00:25:01] Remember, big ideas don't drive change. People do keep learning, keep experimenting and keep embedding AI where it matters most.
[00:25:09] Follow along. So you never miss an episode.