Tech Obsession is Failing Citizens (Rescuing Government Customer Service)

Don't fall in love with your technology. Don't fall in love with your strategy.
Fall in love with how you listen. Fall in love with how you watch.
Fall in love with really getting an understanding of customers' problems. That is the name of the game when it comes to CX.
I could not agree more. There's only one outcome that matters to the citizen, and that's their outcome, right? And every person you leave behind is one person that didn't get that service.
And it tends to be the more vulnerable parts of the population. All customers' missions are unique, but the blind application of technology, just because it's better technology, it very rarely works.
If I don't do this thing, if I don't put this technology in place, if I don't take this little bit of risk in whatever part of the system, how many more citizens are not getting served? Are you concerned with our policymaking around AI and our speed of policymaking around AI? Yes and no. The hint of concern to me is...
Hello, everyone, and welcome to Experts of Experience. I'm your host, Lauren Wood.
Today, I'm joined by Mike Raker, the CTO of Maximus, where their mission is simple yet powerful, making vital government services accessible through transformative technology. Mike is passionate about how AI and machine learning can revolutionize customer experience, as I know many of you are as well.
And I can't wait to explore how

he's leveraging these tools to enhance customer journeys, especially within federal and health services. Mike, so great to have you on the show.
Thanks for having me. So your career spans many different industries from defense primes to startups and scale-ups.
And I'm curious to know how has your diverse experience really taught you about innovation and its role in transforming customer experiences? Thanks again for having me. You know, I'm a student of the world and I think part of what's really transformed me over time is, you know, your learning journey is really never done, right? So when you take positions at different companies or different parts of different companies I've worked across the globe is you take unique insights from all those roles and people that you meet, right? And I think people that are successful at it are good at understanding those differences, applying their history into new missions and then harvesting from those organizations and taking them back out, right? And I've had a real pleasure in my career to work with lots of fantastic people, lots of fantastic missions, and hopefully I've contributed equally to what I've gained out of those.
And that makes me a better technologist over time. Regardless of if I'm working in the US or the UK or elsewhere, I think there are certain truths about the market and tech and how you apply it.
And to me, one of those main elements in successful companies and individuals is how do you combine technology and the operational understanding of the mission that it's going to get applied to, right? All customers' missions are unique, but the blind application of technology, just because it's better technology, it very rarely works. It needs to actually be solving a problem.
And I see this so much with AI implementation. I don't know.
I'm a customer experience consultant myself. And I have clients who say, we need to use AI.
And it's like, for what? Yeah, why? And what are you trying to do? What's the problem? What's the question, right problem? Here's the answer. Yeah, exactly.
I mean, when you think about people that you work with, companies or organizations that you're working with who are really craving more innovation, what is one lesson or tip you would give them as they're really working to be more innovative, if you were to kind of sum it up for us? Certainly at scale, I think one of the things that gets tried too often is manufacturing innovation. It's hard to do.
It's impossible to do. There are ways that you can set you as a technology leader, you need to set the conditions through your organization, culture and other elements to make sure the ecosystem for innovation is there.
It needs to have the scaffolding around it to allow it to proliferate. But within those bounds, you need to empower individuals in the organization to innovate because they're the ones that know the technology better than anybody else.
They're the ones that know the mission better than anybody else. And that is how you move faster, but how you move faster continuously.
And I think that's one of the things I've tried to bring to every organization that I have is set those conditions for organizational success, not a top-down directional, you know, thou shalt do this, go do AI, try and manufacture innovation because it never works. You can't force innovation.
It's a cultural mindset. that are some of the aspects of that? I'm curious to know, if you have a company who, say, isn't really happy with their level of innovation, where are some of the first places that you look or some of the first actions you suggest? One of the things I found in my career that often happens in organizations is you may already be innovating in areas that you don't realize.
And so one of the places I often look is what are those flagship customers and programs that have been successful for the company up to this point, right? And what's made them successful? You often identify really innovative things that are already going on. And even if you don't, you get a really good mindset for what is the real problem you're trying to solve? So you get the understanding that you can then say, all right, these are the types of problems we're truly trying to solve for key customers.
All innovation needs to start and end with solving a problem for these types of things. And when you set an organization around that construct, I think you're much more likely to innovate in the right areas.
I think then there are other normal things that you do organizationally. Make sure that communication and collaboration are primary, that people can speak up, that vision and the strategy and where you're going forward, that people collaborate on it and that it's clear and concise and that people know what good looks like all the way from the customer to the, to the corporate level.
And that way you're set in an ecosystem to drive continuous innovation. I always think of it as kind of like, like grease the wheels.
And it's so often it's like, if communication isn't flowing, if there isn't clear lines of cross-functional communication or clear ownership of different roles, that's just like, it's like rust on your wheels that just prevents everyone from moving smoothly and being able to think innovatively about what it is that they need to do. Or you end up with the same innovation being done different ways in three parts of the organization, right? So you're getting, maybe you might be innovating, but you're doing it really inefficiently where, hey, where could I have taken some savings in human capital resources and money and apply that and done two or three different things with that same amount rather than doing the same thing three times.
So your clients with Maximus, I'd love to hear just a little bit, if you could give us a brief overview of the types of organizations that you work with and some of the challenges you commonly face.

And then we'll dive in a little bit more into your approaches.

One of the great things about Maximus is we help governments better deliver citizen services,

right? So I think most people, particularly in the U.S. and in other countries, have probably

engaged with the Maximus system but may not know it. We help the government deliver the services and help the government serve their citizens better, which is the role of government.
Examples of that, we do a lot of business in the state and local market in things like Medicaid eligibility and enrollment in those types of programs to make sure that people are getting access to the services that they're deserved by the government. At the federal level, we support a range of customers from the VA to the IRS to the SEC to Department of Energy and beyond.
But again, the one truism across all of those, whether it be for health or other services, is how do we make sure that the services that the government are delivering are efficient and effective in serving customers, as well as in DoD and other places. But that's where I really get excited, because what a better way to impact the country than to better allow the government to do their job.
And we do the same thing, particularly in the UK and in Canada. Wow.
I mean, I think it's safe to say that your client's customer size is probably greater than pretty much any other organization. Or there are a few other organizations that have that level of just numbers that they are servicing and volume that they are servicing.
And so in doing a little bit of research about Maximus, I noticed that you emphasize total experience management. I'd love to understand a little bit of what that means and then how does that approach really redefine the way that you help these organizations connect with their customers? We launched TXM, total experience management, I think about a year ago.
It was a little bit before my time at the company, but about a year ago. And I think this is an important technology advancement and go-to-market approach that serves customers better.
And if you break it down to its core, you take the implementation of the technology and the innovation that we've done there off the table for a second. Really what drives that is what I open the conversation with, right? Is how do you connect, you know, our global understanding of the operations and every unique government customer service? How do we understand really what their goals and outcomes are that they need and combine that with technology to enable them to do that better? And that's really about what TXM is, is that continuous solution and ecosystem that brings technology and the customer industry together to drive outcomes.
We have world-class partners in that environment to help drive the technology envelope, not just internally in Maximus, but with worldwide partners, with people like Salesforce and AWS and ServiceNow and others. But, you know, again, the technology itself doesn't solve the problem.
You have to understand how to apply it. And that's really the combination of goodness that I think we've driven in that.
And then secondarily is how do you make sure when we deliver total experience management that it's not a singularity, right? That we don't, it's not a one and done that, hey, this is the experience and we're done forevermore and therefore everybody's successful in it. That's not reality.
We have extreme focus on data-driven nature of how we deliver that experience and the outcomes for that customer so that we're not only doing it once, but as the mission changes, as the need changes, as policy changes, we can make sure that that citizen service that we're delivering is up-to-date, effective, and as current as it can be. And technology is always getting applied again and again and again to make it better.
What would you say, if you think about the clients that you've worked with, if you were to kind of sum up, what would you say that most organizations struggle with when it comes to their customer experience and really creating a seamless customer experience end to end?

First is the understanding.

The customer base that we serve in our business and the government, I mean, think about the diversity of every individual in the United States that might be going from a service

from socioeconomic to where they live, to the languages they speak, to their abilities and anything else, right? I mean, we are a true melting pot. And understanding each of their unique perspectives on what they want the government to support them in doing, If you're not rooted in that bottom level of,

you know, everybody's outcome matters, right? It's hard to make, you know, much headway above

that. And I extend that to, that's the customer understanding, the end customer understanding.

I think where industry and government can do better together is how do you have acquisitions

and contracts where we as an industrial base are taking responsibility for those outcomes? you know,

the,

the,

the,

the,

the,

the,

the,

the,

the,

the,

the,

the,

the,

the,

the,

the,

the,

the,

the,

the,

the,

the,

the,

the,

the,

the,

the,

the,

the,

the,

the,

the,

the,

the,

the,

the,

the,

the,

the,

the,

the,

the,

the,

the,

the,

the,

the,

the,

the,

the,

the,

the,

the,

the,

the,

the,

the,

the,

the,

the,

the,

the,

the,

the,

the,

the,

the,

the,

the,

the,

the,

the,

the,

the,

the,

the,

the,

the,

the,

the,

the,

the,

the,

the,

the,

the,

the,

the,

the,

the,

the,

the,

the,

the,

the,

the,

the,

the,

the,

the,

the,

the,

the,

the,

the,

the,

the, the, whether it's, you know, financially or otherwise. And it's not just reputation.
We should have skin in the game, right? So that's a move to me away from the T&M and cost plus and those types of contracts to a shared responsibility up into cybersecurity risk models, right? And being able and willing to say, well, why can't I host some of that stuff given policy and other considerations so that if there's a cyber breach in a program and I built the software or the ecosystem that did it, why aren't I sharing responsibility with the government? I shouldn't be able to wipe my hands off. I need to run it like I own it, right? But also be responsible enough to say, I'm not going to own data, right? I'm not going to own the IP in it.
I want to be a good steward of government process. So I think those are the elements where we've tried to merge both TXM and what we do technologically with the ethical and moral stance that we take as companies.
We want to share in the outcomes and share in the risk because we're citizens too. Well, I really appreciate that.
As a citizen myself, I'm glad to hear that.

And just as you were talking, I'm thinking about how, you know, I have two clients at the moment that are in the healthcare space and these are startups or, you know, smaller companies, especially as compared to like government organizations. And the amount of restriction at play when it comes to really being technologically savvy, I think is something that these types of organizations, it's limiting.
And I just think about your experience and your role in helping these extremely regulated organizations to really be innovative. And so how do you balance that, that innovation with

regulation and also making sure that you're taking responsibility in it all the way through? Yeah, that's a really good question. And I think something we continue to think our way through because we do want to, in a fair amount of our contracts, we take on direct responsibility for the contracts that we drive, right?

And there's a shared destiny model there

where it doesn't work in all, in all contract styles and outcomes, right? It's, you know, we're not going to put, you know, in DoD, we're not going to put, you know, highly classified data in our networks and stuff like that. That doesn't make sense to the nation, but there are other areas that we work where the boundary is, is much more responsible.
So I think it's working with that government customer to make sure that everybody clearly knows what does good look like in this outcome, right? And making sure that we can then identify from the policy to data security, to the contracting model, to the terms and conditions, to how you grade an acquisition, to any other number of elements, they describe as well as we can mutually aligned incentives between the person performing the work in the government organization that's writing the check to go do it. And that has elements, like I said, all the way from policy to legal to technical to acquisition.
Getting clarity on what those outcomes are is the first step to making sure that that is mutually aligned in outcomes. Yeah, going through that process of really thinking about what is the end goal here and when we'll be really happy with the results.
And I think that's something that every company should be going through as they're embarking on new projects and thinking about what can we do to improve here? And also thinking, what are the trade-offs? I'm sure there's a lot of trade-offs for you as you go through that process to say, how do we make sure that this is still safe and that this is still protecting our end customer while also maybe pushing the boundaries of new technology or incorporating new technologies into the mix. I can imagine the workshops you guys do are probably pretty intense and thinking about what could go wrong here.
Part of that is just we have to be transparent in the ecosystem to make sure that there's risk in anything you do. You can't eliminate it, but you can mitigate it.
You can identify it. You can do all of those other things to make sure that it's at least clear, even when there's a risk of inaction.
Right. I mean, I think that's often one of the risks that's not calculated is, hey, if I don't do this thing, if I don't put this technology in place, if I don't take this little bit of risk in whatever part of the system, how many more citizens are not getting served as a result of that, right? So there's always risk.
It's about making sure you can identify it, quantify it, and then have open and transparent discussions with the government to say, hey, we want to go do these things. Here's our analysis that shows the positive outcome that we think is going to have it.
Here are the indicators that we think will confirm or deny that we're on the right path or not. And here's what we want to go do because the status quo is never usually the right way to continue to innovate and drive different outcomes.
We need to be very conscious of the status quo. An AI agent your customers actually enjoy talking to? Salesforce has you covered.
Meet AgentForce Service Agent, the AI agent that can resolve cases in conversational language anytime on any channel. To learn more, visit salesforce.com slash agent force.
Correct. So I want to talk a little bit about AI and machine learning and how you really, what is your vision when it comes to customer experience for how these technologies can really help to improve the customer experience? We have massive amounts of CX applications across, you know, across the globe, right? And, you know, I think as AI continues to proliferate through it, I, I think about it in a couple of different lenses, right.
Um, historically enough to this point, a lot of AI in these, in these systems have been really focused about the efficiency of the systems, right. So, you know, how do I apply, if you think about good customer experience, it's, um, how do how do I do more with, you know, intelligent agents or text or the like to make sure that the contact is seamless and fast.
But I think we're moving into the realm of this isn't just about, you know, efficient IT and delivery. It's about the effectiveness of them.
Right. So just because you are efficient in the engagement with a customer doesn't mean that that was the best outcome for a customer or a citizen or that we couldn't have completely upended it.
So I think about it from an effectiveness point of view. Well, why was that person even calling in in the first place, right? So can I use AI to identify the trends and conditions to say, how do I get proactive with that customer so that I can answer their question before the question comes in? It's the CX equivalent of Amazon's goal of making sure that the product's on the truck before you even order it.
It's that mentality in CX, is how do I proactively anticipate what a citizen or a customer is going to need, deliver it to them so they don't hit my contacts and they're going to have to come into my systems. That proactivity is a really open running room for how we think about the next generation of AI and how we make the experiences not just faster, but more effective.
And then I think the third piece of it, which we're getting to is how do I use my CX systems and all of the data in them to gain better insights about the citizenry and what better policy decisions overall and things like that might actually be? Because I have all of this information about the customers that I can offer new services, new solutions, policy updates, and the like.

And I think that's the crawl, walk, run to me of efficiency, effectiveness, and then insight.

I love the point about the insights and really enabling us to be more proactive with the

information we are now able to gather. Do you have any examples of how you've helped a client

to use technology and really create a more proactive customer experience that you could share? Almost every single one of our customer has some element of AI or the like within it. And some of the public stats, right, it's watching things like CSAT scores, you know, go up and up and up, right? And we publicly report a lot of those things, even in, you know, trying times.
And, you know, we had a recent incident on one of our contracts where, because of the way we use AI and in our workforce management and other applications of it, the hurricanes in the Carolinas interrupted a couple of our locations. But the mission still goes on.
So we were able to seamlessly still handle all the call volumes and contacts that we were obligated to do, still keep our CSAT scores where they were, even though we had a massive amount of people displaced by natural weather events that we couldn't have forecasted. And then the moral and ethical responsibility for us to make sure that we can also, outside of the contract environment, how do we make sure we take care of those employees that are affected by something that wasn't caused by them? That's just one example of it.
We've got hundreds and hundreds of, you know, RPAs in existence. We have massive amounts of intelligent document processing across our contracts.
Can't get into the actual details of that, but in citizen services, you have a lot of our contracts where people are asking for enrolling, asking for eligibility in systems, and they've got to verify a bunch of documents or this or that or the other, both inbound and outbound, right? We need to send mailers out and contact people via snail mail. We've built massive intelligent document processing systems that are really modular, that can go all the way from applications in health to applications in civilian services and beyond that process an absolutely otherworldly amount of pages of documents every single day.
And anybody else that wants to know the details, you'll have to come talk to me about it, but it's really mind-blowing. AI in the form that it is in today hasn't really been a part of our lives or the average person's lives that long.
But I just think about what you're saying in document processing and how we would do that manually not that long ago. And it's like, wow, we're never going back.

We can't go back to that.

I'm curious to know your thoughts on,

or any advice that you would give to leaders

who are looking to implement more AI and machine learning

into their customer experience strategy.

Are there any common mistakes

or things that people should be aware of and look out for as they embark on those projects? I think it goes back to the comment you made earlier, right? Which is, you know, they figure out what the technology is first, not what the question is, right? And I think that's the biggest mistake is, you know, when you're going into it, truly understand what the problem is that you're going to solve and then look at the application, the technology. There's phenomenal technology companies in the world, but when it gets into government services, just because it works someplace else doesn't mean it's going to work in this particular customer.
And I think that's often overlooked. Like you have to keep your eyes and ears open and really understand what is the nuance of this problem.
Don't say, I've got a hammer. I'm pretty sure I can make you look like a nail.
And really ask what the question is and do that system-level thinking and that consultative-level thinking up front to make sure we really know what good looks like. Otherwise, technology is just technology.
What about for companies? I'm just thinking about your clients, customers, which are people of all walks of life, of all age groups. And I think it's something I think about a lot is like, how do we make sure we're including all the customers in this? Because as we become more and more technologically advanced, we tend to lose some people in the process.
And I mean, I think of my parents who are like, what the heck is happening? And how do you really build for everyone? We have a CX accelerator that works in my organization that's in a digital group that we have absolutely world-class designers, right? Service designers and the like that are skilled at building systems that give you multimodal, omni-channel ways to engage with citizenry. We've got a trophy case to back it up, but it's not really about that, right? It's the meat of the question that I think I always work with that team about is even if we have great outcomes at a contract level, there's only one outcome that matters to the citizen and that's their outcome.
Right. And every person you leave behind is and one person that didn't get that service.
And it tends to be the more vulnerable parts of the population that tend to fall in those traps. Right.
And it's our job to make sure that there is no one left behind in that. We do everything we can in human-centered design and good CX and all that to implement systems, but inevitably you're going to miss something, right? Or the mission is going to change and the policy changes and something slips through the cracks, which means this is really about in live operations that you're collecting the data off the systems

that gives you the insight first

to first recognize that somebody's been missed, right?

That's your first job.

Because if you don't know what's happened,

you can really kid yourself that,

hey, look at my CSAT scores

and my whatever other metrics that I'm being judged by

are well above industry average

and we can pat ourselves on the back too hard,

but that doesn't matter to the person that didn't get the service. And data is the key to unlocking what are we missing, right? Who's falling through the cracks? How do we make sure that we can identify who should be enrolled, not just who is enrolled, who has tried to enroll and fail, right? Working with customers and really driving the data out of that so that we know what a perfect score looks like and identify the trends, conditions under which people are falling through the cracks.
That's why we're always in a data-driven system, because how does that CX experience change over time with the citizenry and who we're serving and make sure that it's personalized to everyone out there? I love this point because it is far too often that we will look at metrics and say, look at our CSAT, it's so good, but we're not looking at who didn't answer the question, right? And how can you know that? What are some tips to know who's missing? How can we like really architect our data structures to see that? This is another one of those areas where really understanding the customer and the population helps inform that at a contract level, right? Because working with customers in the citizenry and the like helps you unlock that at a contract level. But that's often, you know, it can be a challenge, right? Is, you know, yes, our CSAT scores are good and they're going up and this, that, and the other.
And those are all wonderful things.

But we can also, I think, go to other, like you said, that requires somebody to fill out the survey or the, you know, go through the QA or whatever that says you're performing well. There's all kinds of other mechanisms for open source data to say, well, what's the rating of the overall agency that we're supporting, right? What are the other, you know, what's going on, you know, Twitter or, you know, in other media forms that are people that might be expressing dislike for the fact that, you know, they haven't been able to do X, Y, and Z and they never got caught in the data system, right? So I think there are outside of system ways to at least get leading indicators to say, hey, we think our CSAT score is great, but man,

there's a news article over here about this population of people that are really, you know, annoyed or upset or this, that, you know, how do we make sure that we have a total worldview of that and we don't fall in love just with the data that we have? Yeah, we need to be listening outside of just our channels. And I think that's where AI can be so helpful for us as well, to be listening on social and the internet, to say, okay, here's something that we should be looking at and taking into consideration.
And also just, I think, the awareness that we don't have, we're not collecting everything through our channels. We have to also think about, you know, what are people not saying to us directly and go out and find that information.
And that's where sometimes, you know, we, you know, sentiment and topic mining and other things, even with the population that you're catching might give you leading indicators to say, something's going on here that I can't put my finger on is not showing up in my CSAT, but I keep getting fill in the blank, right? That's another really interesting,

almost analytic and intelligence gathering effort. Again, I go back to that insights thing.

Here's what the numbers are saying, but what's the real insight? What's really going on here and get really good at that? This is where intuition comes into play. If we are hearing

something in tickets, if we get the sense just because we've had enough experience with it,

maybe this isn't working, maybe we're missing something. I think it's just so important that

Thank you. If we get the sense just because we've had enough experience with it that maybe this isn't working, maybe we're missing something, I think it's just so important that we stay attuned to that and then go and look into it further.
because the data, as much as I love data and I love to see things in the numbers,

there's always going to be gaps.

And we need to be really savvy and tuned into

what could be missing here and ask ourselves that question. If you think about contact centers and some of the work that we do, the massive scale at which we're able to do that, the human element is at the end of the day, still the linchpin, right? I don't care how good the technology I get that delivers.
Human beings are, you know, for the foreseeable future, still a big part of that system. So whether it's our, you know, the citizenry or our call representatives that are picking up the phone, both for how do we make their environment better, but have that ear that the data and the AI is not going to pick up, right? We can't get too reliant on that, that the human touch is still the human touch.
And giving for almost back to the top of our conversation, part of the innovation

ecosystem is making sure that you have the means for people to provide that feedback, but also the

culture to be able to say, hey, I know you want to do fill in the blank and you want to do this AI

and we've done this and this and this and this, but what you're not seeing in the numbers is

they need to have a voice and they need to be able to speak up and we need to listen, right? Because technology doesn't work without people. Yep, completely.
I could not agree more. So looking ahead, technology is advancing faster than ever before.
What are some of the trends or technologies that you see coming into play that are exciting you today? This is something that I continuously think about. And in my career, the pace at which this is happening is faster and faster and faster.
And I think anybody that says that 10 years ago, they could have predicted where AI is today deserves a prize or they really need to be looked at as going like, I don't know about that. And I'm one of them, right? I think we've all missed it at some level on this one.
But it's about, again, if you keep your ears open, it's okay if you miss, right? Because you're listening. But more to the question, I think things like agentic AI are certainly on our minds.
Right. It's another step in the transformation journey.
As AI continues to mature and provide more capabilities, it broadens the spectrum of where they go. So I think that's kind of here and now.
It's not really five years out. We're in the we're in the front end of that of that basket.
I also think about how AI comes into our CX solutions, not just out. I think a lot of contact centers, we think about how do we make the consumer and customer experience more efficient, more effective in getting insights out of it that's kind of in the bounds of the CX.
But it's not far off when AI is going to start coming into our systems, right? How do you as an individual start to have AI agents act on your behalf to go register for, you know, that doctor's appointment or you subscribe to this or you've got, you know, and so now we're not, we're dealing with an avatar or a representation of a human being coming into our systems, not just going outbound to our systems. Starts to create, you know, and then you start to have multi-agent systems and all those sorts of things just start to compound on each other in the cross-pollination, as I think will create a really interesting ecosystem.
On top of that, I think key to the technology is all of the enabling or adjacent things that affect technology. The policy end of it's probably the most dynamic to me that technology moves faster than policy can, right? And that's not a knock on our lawmakers or anything or any way, shape or form.
That's just reality of it, right? So how do we write policy on things that we don't not exist yet or really know how we're going to apply to them or where that boundary might even look? And I think that combination of speed and ecosystem and policy and quantum computing and all those things creates a really rich ecosystem and tapestry of how I think about not only the technology, but the application and synthesis of it in systems for how do we think this is a system. That's where my brain goes continuously for the next five years.
Are you concerned with our policymaking around AI and our speed of policymaking around AI? Yes and no. The hint of concern to me is that we haven't had an event that catalyzes, I think, the gut feeling that most of us have that at some point there's gonna be something that goes really wrong because of AI.
And I think that's natural in any technology evolution. There's more of a fear of the unknown element of it, I think, the voice on that shoulder of it.
But I think I'm more positive about the fact that, you know, I think we as a nation have continually taken a relatively healthy posture on saying, at the end of the day, let's make, you know, nobody's having AI directly make a human decision where there's life on the line or these sorts of things. And we, as logical human beings, regardless of what policy may or may not say, we can still make really good informed decisions to go.
That just doesn't make sense, right? And we have, you know, whether it's at the policy in the congressional level or the individuals that lead our agencies in the government, these are, you know, brilliant, driven, you know, well-thinking individuals that have the care of the United States and citizenry and our foreign nations at hand. And there's still going to be gaps in policy where people need to make decisions, right? And so that's where I'm comfortable that I think we're still in an okay boundary.
but at some point, I think we all worry about when do the bots take over and all that. There's too much science fiction out there to not have a little bit of conversation around that.
But we do need to, I think the concern for me in the policy domain also just goes to the larger, you know, how does the larger political environment make sure that we're doing what's right for policy, not for politics?

That's a non-parochial view of the world, but I think it's something that we're going to continue to battle as the nation, right? And, you know, overregulation isn't the right answer, right?

No regulation isn't the right answer.

And I think a two-party system that kind of ebbs and flows that policy back and forth generally over the arc of history is as good as we can get. So I have confidence in the people in the system, I think, to back up the policy because none of us could write a perfect policy.
You just can't do it. Yeah, well, we just don't know what's in front of us.
I mean, like you said, this has been moving faster than any technology we've ever seen. And there's no way we get it a hundred percent right.
And there's a lot of work being done to make sure we're mitigating risk. And I think the speed piece is the part that is so scary for most people of like, whoa, we can't keep up.
And so, yeah, I mean, time will tell where things land. But yeah.
If there was a perfect policy environment that existed today, right, it would be out of date tomorrow, right? I mean, that's just the nature of it, right? And you've got to rely on human beings to be interpretive and understanding and be the professionals that they are in their discipline to say, hey, this is the right decision and this isn't covered by policy, but I have to interpret it and this is what's right in the situation. So we have to rely on the checks and balances in the system to keep that active.
And it goes back to the top, kind of what I said, that includes contractors, right? That includes the industrial base that has to be willing to sign up, not just to a contract, but to the outcomes of the contract. So if AI goes off the rails or there's a cyber breach or this, that, and the other, we should be responsible for that, right? It's the only way to have mutually aligned incentives and have skin in the game to make sure that everybody's acting on best behalf of most people.
Well, I really appreciate that insight and that acknowledgement of the need of responsibility, the need for responsibility in a position like yours. I will sleep a little easier tonight after talking to you.
So, Mike, we are going to wrap up our conversation here, and we always ask our guests two questions. The first is, I would love to hear about a recent experience that you had with a brand or a company or a service that left you impressed.
Tell us about that experience and why it was amazing. The one that comes to mind was over the holidays.
And we were out of the country flying home and there were maintenance delays and delays and delays on the airline. And the flight finally went, but it went very, very quickly.
So much so that you couldn't call customer help representative to say like, hey, I think I'm going to miss my connection. You know, all those sorts of things.
So you had to go get on a flight. No worries.
Yeah. And you're international.
So, you know, the Wi-Fi and stuff like that tends to be a little bit more intermittent. But the airline, it was United Airlines in this case, who I've flown a lot for.
So they're friendly to me and I think vice versa. But, you know, they're customer representatives and being able to actually make my flight changes and get my updates in flight via text with my phone over their message contract, whatever it is, in the middle of the night, flying over the Gulf, it was seamless.
It took a couple of minutes. The agent was there.
The internet was kind of coming in and out. And I already knew what our flights were booked.
And we could then go to sleep, right? Because it was an overnight flight. So I was really impressed at the modality, the speed and the directness of being able to do that.
It struck me as really, really awesome. So kudos to them.
That's great. And you got the outcome you were looking for, which is the goal, right? That's right.
And I could get a little bit of sleep on the red eye, right? So I'm like, okay, I don't have to worry about that. I can get a little red eye.
That's awesome. And so my last question for you is, what is one piece of advice that every customer experience leader should hear? Keep your eyes and ears open.
Things change quickly. I like to think that I'm good at what I do and my team's really good at what we do, but things change really, really, really, really quickly.
And if you, we always talk about, you know, you have to have a strategy, but strategy needs to evolve. And that exists all the way from a corporate level down to an individual customer that might have, you know, unique challenges due to hurricanes or policy changes or whatever it is.
Keep your eyes and ears open and do that continuously because don't fall in love with

your technology. Don't fall in love with your strategy, right? Fall in love with how you listen,

fall in love with how you watch, fall in love with really getting an understanding of customers'

problems. Because if you're rooted in those things, you are far more likely to deliver

better outcomes for citizens, better technology and better solutions. And that's what I think we get lost in the world, right? We get caught up in social media and what's going on in the world, but listen, like really, truly listen to what's going on.
That is the name of the game when it comes to CX. I could not agree more.
Well, Mike, thank you so much for coming on the show.

It's been an absolute pleasure to have you.

And I'm sure we'll keep in touch and talk soon.