Experts of Experience - #57 Why Your C-Suite Needs to Embrace AI for Customer Success

Episode Date: November 20, 2024

Want to navigate the complexities of digital transformation successfully? In this episode, Jonathan Murray, the Chief Strategy Officer at Mod Op and co-author of Getting Digital Done, outlines a step-by-step approach to integrating AI into your customer experience strategy. He explains how to build a solid data foundation and establish governance principles that will set your organization up for success. Plus, Jonathan and Lauren discuss the disconnect between leadership and customer needs, and how to bridge that gap using data-driven insights.Tune in to learn:Why organizations often resist new technologies due to fear and uncertaintyHow AI can enhance customer interactions through conversational experiencesHow AI can help rehumanize business interactions with customersWhy organizations must have the right data infrastructure to leverage AIWhy employee experience must be prioritized to ensure successful transformations–How can you bring all your disconnected, enterprise data into Salesforce to deliver a 360-degree view of your customer? The answer is Data Cloud. With more than 200 implementations completed globally, the leading Salesforce experts from Professional Services can help you realize value quickly with Data Cloud. To learn more, visit salesforce.com/products/data to learn more. Mission.org is a media studio producing content alongside world-class clients. Learn more at mission.org.

Most customers are going to love that future that you're dreaming up, but what they really want is you to deal with today's problems. And if you don't deal with today's issues, you're not going to get permission to sell them the future. Every wave of technology that organizations have lived through have all the same questions, and AI is no different. Hello, everyone, and welcome back to Experts of Experience. I'm your host, Lauren Wood.

Today I am joined by Jonathan Murray, the Chief Strategy Officer at ModOp and co-author of Getting Digital Done, a blueprint for navigating digital transformation. Today we are going to dive deep into effectively leveraging AI and data analytics to transform your customer experiences, as well as what are the critical questions that leaders need to be asking themselves to ensure that their digital transformation is done right. Jonathan, so wonderful to have you on the show. Very nice to meet you, Lauren. Delighted to be here.

So tell me a little bit about Mod-Up just for the folks who may not know about it. Mod-Up is a full service marketing agency that's been growing pretty rapidly. We've acquired a number of firms over the last two or three years. The agency goes back decades, was anchored in a couple of foundational agencies like Eyeball as an example.

But the growth has really started to snowball over the last few years. We've gone from 150 odd folks 18 months ago to over 400 folks today. We have a large footprint in North America. We're one of the largest independent full service agencies in the country. So we fly a little bit under the radar. Our brand is not as well known as some others.

But in terms of scale and capabilities, we're, like I said, a full service agency. And I think one of the unique things about us that differentiates us is that we actually have a strategic consulting arm. So we joined the firm that my partner Len, Gilbert and I had built over the last decade, joined ModOp about a year ago, and we do full digital transformation strategy work. We do everything soup to nuts,

board level growth strategy for firms all the way through to technical implementations. That spans both the marketing domain as well as businesses in general. So that's a little bit of a differentiator for us as a business. And it's interesting when I go on your website, AI is a key topic of conversation. It's really what you lead with. I'm curious to know why has the digital component and then specifically AI, as we've entered that world, how has that been key to your strategy and why has it been key to your strategy as a marketing agency?

Well, I think we've all witnessed the transformation of marketing over the last decade by increasingly powerful analytics and data, right? So marketing organizations today survive on the data that they can use to sense the environment, to sense what their customers need, to target, et cetera. We all know how important data is to marketing today. And what AI is doing is bringing a new generation of disruptive tools that can then use that data, right, to drive insights, to drive, you know, in the generative space,

to create new content, drive, you know, in the generative space, to create new content, copy, etc. off those data-driven insights. So AI is essentially a new set of tools that leverages the power of the data that we have sitting in our organizations to drive better outcomes for marketing, better targeting, better creative, more interesting experiences for consumers. So it's a highly disruptive wave of technology that we're going to live through. And it's going to disrupt marketing as much as it disrupts any other aspect of the businesses we operate in. When it comes to how you're integrating AI into business, into marketing and business operations, what's some of the resistance that you're experiencing from some of your clients?

Where are people feeling maybe uneasy or where do they have questions about AI that you're really having to overcome with them? I think one of the interesting things is that we experience resistance in every wave of technology, right? we experience resistance in every wave of technology. I've been around long enough to have lived through the initial wave of the Internet, impacting from its most nascent days and invention all the way through to what it is today.

When the Internet surfaced, there was a lot of resistance in organs that's not a technology that's going to apply to us, right? That's not going to impact how we serve our customers. We don't understand it. There's a lot of risk associated with it. We're not quite sure how to leverage it. It requires a lot of investment.

How do we decide what the right level of investment is? We need all these new skills, right? Every wave of technology that organizations have lived through have all the same questions and AI is no different. Senior leaders in organizations starting with CMOs, CEOs, the boards of companies are looking at what on face value is a very disruptive set of technologies that comes with a lot of positives and a lot of risks.

They're asking those questions which is, what's that going to do to our business? What's it going to do to the markets that we serve, the customers? How's it going to reset customer expectations? Then what are the skill sets we're going to need as an organization? How do we embark on that journey? How do we do it in a way where we balance the investment that's required with the return on that investment and the risks that will come along with adopting any new technology.

In some senses, AI is no different to every wave of technology that organizations need to deal with and the same tools that we've built over decades to deal with those transformations are the same tools we're going to use with AI. But at the same time, AI is so powerful and the news cycle on AI is sometimes hyperbolic that it does create a level of concern that I think is in some sense is warranted that organizations are going to have to get comfortable with. So there's a higher risk profile, I think, with this that's driven by the news cycle, the hype around the technology and whether it the technology and what the real risks are.

Yeah. I mean, it's a step change. Yes, we've been going through technological transformations consistently over the past few decades. I mean, consistently throughout our time, but it's been picking up speed. And AI is a different beast in many ways. And like you said, there's many opportunities as well as many risks. I'm curious to understand some of those risks that you commonly see as you are driving these

digital transformations. And then I have some follow-up questions after that. I think a lot of the risks that folks see, and again, because of the, and these are all risks that can ultimately be addressed, right, and mitigated. But a lot of the risks that folks are concerned about are what they see, again, in the news cycle or in coverage of the technology, right, which is, you know, if you're talking about AI at its most advanced level, you know, large language models and cognitive AI, etc., right, there's hallucinations. You know, there's a and cognitive AI, etc. There's hallucinations. There's a whole discussion about, well, I go into chat GPT and I type a prompt into chat GPT,

it's not always correct. How can I put that in front of my customers? We actually did a use case for a proof of concept for one of our not-for-profit clients who are in the engineering standards space. They're one of the largest organizations that sets engineering standards for their industry. They wanted to transform the way their members and users consumed their standards from a very traditional search-based experience, very sort of old school search-based experience, to a conversational sort of natural language experience, right? Applying these new technologies. Their biggest concern was when I type in, you know, I'm building a bridge here and I need to know what the correct environmental standard is

that I need to follow, is that the system just doesn't cough up some hallucination, right? Because building bridges, they need to work, right? So there's a criticality to that. And so again, one of the points of the proof of concept is could we build something for that client? Could we put that in place and eliminate the hallucinations, which we did successfully. There are techniques and mechanisms for doing that, which are available successfully. There are techniques and mechanisms for doing that, which are available today. And so it's essentially, there's concern about,

does the technology disintermediate the human in the loop? Does it run wild? Does it create existential threat to the organization? Does it create reputational risk? All of those are real things. And you just basically need to go through each of those risks. And there are mitigation strategies for each of them as you start to think about rolling that technology out

in the organization. I mean, when we talk about customer experience and putting AI technology in front of customers, it is so vital that companies take those actions to mitigate those risks. Because I know I, as a consumer, have seen those hallucinations. I've experienced AI not working the way that it was supposed to. So how can companies mitigate that hallucination risk? So I think a lot of this actually comes back to how well organized your data is.

And I hate it. I hate to bring it back to something as mundane sort of in this conversation as that, but we all know that essentially the fuel for AI is well organized and well curated data. Right. And so many organizations even today have not gone through the work that's required to actually curate, organize, link the key pieces of critical data that will be the underlying fuel for any AI initiative, right? That basically drives the outputs from those AI tools. those AI tools. And so I think getting the basics right on that is say, quote, the quality of your data completeness, its organization, how it's joined up. And, you know, do you have a complete 360 degree view of the domain that you're trying to serve, those sorts of

things. Those are all first level problems that need to be solved before you start thinking about slapping an LLM on there to have a natural language conversation with a client. So that's job one. And the client we did this proof of concept work had already done that work. They'd already gone through a very rigorous reevaluation of their data assets, their content assets, curated them, done the quality work that needed to be done. The next layer is there's a set of technologies today without going down a rabbit hole on the be done. The next layer is there's a set of technologies today, without going down a rabbit hole in the tech itself. The large language model technologies in and of themselves

are going to hallucinate. That's just the nature of their design. But when you combine them with a set of other technologies, then you can eliminate that. The way we did that with this client is essentially using what's called graph technology, building a knowledge graph, which establishes the relationship between all the entities in the data and the content. That puts a

constraint on the large language model and prevents it from essentially going off the field in different directions. It says, okay, I know what the relationship between these is and I'm going to serve you back an answer that is accurate. If I don't have an answer, I'm going to tell you that I don't have an answer, and that's how it should operate. There are different ways of solving this from a technical perspective, but data is the foundation.

Well-formed data is the foundation. Yeah. I keep hearing people say garbage in, garbage out. We have to make sure that what we are putting into our AI technology is clean, it's ready to go. We're essentially like, I don't know, like if you're recycling information, you've like gone through and sorted through it. So now it can be produced into something else.

And then when it comes to that graph that you're talking about, it's really creating rules and guidelines. Like, I also really like the analogy, you know, as we talk about agents in the realm of AI and how we have thousands of agents, which we can almost consider to be, you know, assistants of ours. We would create, for an assistant,

here's your job description. Here's, you know, in an ideal world. And as I work with teams as a leadership coach, I tend to advise people to make sure that people know what their job is. Here's what you have control over. Here's where you can make decisions. Here is where you cannot make decisions,

where you need to get approval. So it's abundantly clear to both. We can do this for humans, and we should do this for humans, and we should definitely do it for AI in telling the AI where it has a domain to roam and where it needs to stop. And I think you're raising a really good point, which is one of the biggest concerns with applying AI. And look, AI represents a broad range of techniques, right? Everything from sort of advanced machine learning all the way

through to these cognitive systems that we're becoming increasingly familiar with. But when you look at the implementation of those systems, it's really important that we establish the rules. And more important that we establish the rules in this wave of technology, probably than any other wave of technology, because we're taking humans out of the loop often, right? You talk about agents. Well, what's an agent doing? An agent's doing a task that previously might have been done by somebody in the organization, a real human being who would look at that task and would make a value decision on certain

things that need to be done. An agent's not going to do that. An agent is going to run on the data that has and the rules that have been set. And therefore, that requires a higher level of quality in terms of how we think about governance. And we're working with a big client right now, big financial services client in California as it happens. And the whole project is about how do you establish the appropriate level of governance

at the organizational level to make sure that you can implement AI safely. Putting that framework in place is a huge piece of work that has to be done by every organization. And so this is kind of layering on top of the data is getting aligned, getting the humans aligned on what the governance is. What are the rules that we're putting on this AI? Do you have any tips for our listeners in how to approach that? I know many of our listeners are dealing with various levels of AI implementation, but I

think it's safe to say that everyone is working with AI in some way, shape or form or, and will be increasingly so. When we talk about governance, how do you approach that with your clients and what tips do you have for listeners and how they can approach it themselves? Obviously governance has to be crafted for every, you know, each organization, in a sense is like agile. Everybody talks about agile as a methodology. But in my experience, you ultimately end up crafting your own version. Every organization crafts their own version of agile,

because it has to work in the culture of the organization, etc. Governance is exactly the same. Every business has its own context, the regulatory environment it's in, the rules they have to follow, et cetera. Your governance model needs to be built for that environment. But there's a starting point. And the starting point we generally feel is most valuable is to start with principles.

We love principles based models, which is starting with defining the, and it's not a dozen, it's the seven or eight, right, principles that guide all downstream decision-making. And I think if you're embarking on the AI journey without having established what those guiding principles are, right, then you're opening yourself up to risk, right? So doing the work to actually figure out what are the key principles that we're going to use and apply to all investments in AI, all of our use of AI across the business, that's a critical starting point to make sure that you're embarking on a journey that can be

can deliver the value but can be safe and you can mitigate the risk. What's an example of a principle? That models should be explainable and understandable as an example, right? That that would be a key principle that when you develop a model, and whether you're reusing chat GPT or something like that, that you're able to explain why it came up with the answers that it came up with, right? And that's a huge challenge in the AI space because a lot of what we deal with,

particularly large language models, is a bit of a black box. But to the extent that you're implementing inside your organization, you need to be able to audit and explain how a system came up. So if you're serving, and we're talking about customer experience,

in your world, if you're serving customers, customers are going to want to know that, why, how did you come up with that recommendation? If I'm interacting with a natural language system, why did you recommend what you just recommended? Not, you know, simple menu based system. That's really easy to understand, but a more sophisticated natural language environment, I may end up because of subtleties in the interaction, giving a different recommendation to one customer that I'll give to another customer.

Can you explain why those subtle differences occurred and why one customer got a different recommendation from another? What were the meaningful inputs that changed the recommendation? So being able to explain explainability, right? And then the management, understanding and elimination ofability, right? Yep. And then the eliminate the management, understanding and elimination of bias, right? That's the other thing, which is, and again, that goes back to the data, which is if the raw data you're feeding these systems with is biased, then the outputs from these systems will likely be biased and there's all different types

of bias, of course, but I think that's one of the things that ethically and from a, from an external governance perspective, when regulators look at things and that sort of thing, depending on the industry you're in, there's going to be an awful lot of attention paid to what are the inbuilt biases in these automated systems that we're starting to build and how are they being managed and how are they being eliminated. That's a critical principle that you're managing and understanding the bias in your system and that you're working to essentially eliminate critical biases.

Yep. So I just want to recap really quick two really important points that you shared here. Making sure that we are cleaning our data, we're preparing our data, we're inputting the right data into our AI models and tools. And we are, even before that, we have principles set for how we are going to approach it,

values that are guiding our actions. A few of them, not too many of them, for everyone on the team to follow, which is really also the rules that you're putting in place for the AI. What are the boundaries you're creating for yourself as you play in this new AI sandbox? Did I get it right? Anything else to add there? Right. 100%. One thing I will emphasize, and this is from lessons from decades of doing

digital transformation work, that rule setting, those principles have to be adopted at the highest level of the organization. So this is not something, you principles, have to be adopted at the highest level of the organization. So this is not something we cascade it down to the implementation team, and they have a set of principles. We're talking about a board and a CEO and a group of C-suite executives, understanding those principles, being part of the process of their development,

and then being fully committed to those principles in every aspect of the business's operations. And that often gets lost when companies are thinking, oh, you know, we've got this new tech, we're just gonna deploy it in the business. The IT team will deal with that, or we'll let marketing work with the IT team

to sort of figure out how to go do that. This is a board level and C-suite level set of decisions that have to be made. An AI agent your customers actually enjoy talking to? Salesforce has you covered. Meet Agent Force 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. It's such an important point that you're making here because it will become very hairy down the line when you need to make decisions and you don't have a set of principles that are set. If the board is pushing for something that is, you know, what commonly happens, a revenue generating activity, but it doesn't align with the principles that you've built your AI on,

you are going to get into trouble and there's going to be difficulty in moving through that. If you are all taking the time to get aligned in the beginning, things will become much smoother in the long run. I was gonna just add to that, which is whether, if you live in a regulated environment, financial services today,

then these are already rules you're starting to see that you have to comply with. Yeah. But I would say that every business, at some point you may not see them today, but some point let's say over the next five years or so, every business leader just like we have SOX compliance today, will have a set of compliance responsibilities that they are responsible for as

an executive that relate to AI. And so get your house in order now, because you're going to have to deal with that at some point in the near future. That's such a good point. And it's actually really interesting how much of the wild west we are living in. And this is a very likely unique point in time where there aren't a lot of rules being handed to us by governments or regulating bodies around AI. It is coming and definitely there are some industries where it's more prominent than others. But for the most part, we really have fairly open grounds. And like you said, that is not going to last.

So get yourself organized now so that once your business is even more dependent on AI, you don't have a risk of having to change things that are fundamental to the way that you are working. So I want to shift gears a little bit to, obviously, our main topic of conversation when it comes to this podcast is customer experience. And everything that you've been sharing here is, I think, just helpful for any business leader, anyone who is working with AI to have these things in mind and

to advocate for the way that we're using and implementing AI. But when it comes to customer experience, I'd love to understand your opinion on what are some of the big opportunities at play here for us in the customer experience space when it comes to AI? I think the one that everybody sees is sort of transforming the interaction model to basically how people will interact with organizations. And we've already talked about it, which is moving from sort of very structured interaction through web pages or search or whatever it is to much more conversational experiences, right? So I think we already see that,

we already interact with Siri on our phones or Alexa on our connected devices or what have you, right? That mode of interaction, we're training users already to have that expectation. The next generation of user experience for any service that we consume, I think we'll have a voice component, we'll have a natural language component to it,

and organizations that will build a more, that creates this risk that comes with that, of course, but we've talked about that. But there's also a lot of opportunity, right, which is, we all know that the richer the dialogue we have, the more opportunity there is bluntly to collect intelligence on the conversation, right. And more intelligence on the conversation means I can be smarter about my interaction with that client or that customer

or that person that I'm serving, and I can be more targeted in what they need, right? So a combination of the richness of the more rich those experiences are, the more natural and language driven they are, the more data I'm going to be able to collect about nuances and information about what the consumer actually wants. And if I've built the systems, I'll be able to parlay that into much more focused targeting and focused responses that feel like a one-to-one conversation, which isn't that the dream of every marketer, which is that every interaction with a consumer or a business partner, what have you, feels like a one-to-one interaction. And I think we're starting to see a set of tools

that will allow us to get to that point. And I think that's the biggest transformation we're gonna see. Can we really get to really, truly one-to-one experiences between a brand and a consumer of that brand or a business and somebody who's buying brand or a business and somebody's buying products from that business. Yeah. Talk about relationship building. You know, in a, especially in a B2C environment,

if we can literally talk to our customers, which is something that we don't really get to do, we're talking to numbers versus humans. And I think the opportunity that AI is giving us is to really rehumanize business, take us out of, of course, analytics, KPIs, data, all really matter and is a huge component of this. But if we get to feel like we are having a human interaction with a company, it just builds so much more trust and loyalty and ability to remember and feel that brand that it just goes so much further than I clicked a couple

buttons and I got the thing. It's sort of ironic, right? It's ironic that the very thing that might help us build a deeper connection to our consumers, right, is actually the artificial intelligence, right? Is that because of the cost structure around sort of having people in sort of service centers talking to directly to clients and having that to be a very structured process,

we're all frustrated with the IVR experience, etc. And typically when we call up the bank or whatever, it's not a very fulfilling experience, but a lot of organizations put a lot of money behind improving that. And yet we're on the cusp of a technology that may feel much more engaged because it's an artificial natural language data-driven experience, than what I get when I call the help desk. There's a certain irony to that. Oh, completely. It is personally the thing I'm the most excited about AI,

where we can be less on computers and more in relationship with one another. And that is the beauty of AI. It's reminding me, I was at Dreamforce a few, I guess it was a month ago now, Salesforce's big conference, and they were rolling out Agentforce, their new agent AI technology. And they did a demo for Sakseth Avenue

where someone picks up the phone and calls because they want a smaller sweater. So instead of, and they showed that here's what it would normally be, you know, if you're calling for this press one, if you're calling for this press two, if you're calling, you know, you go through it and through it. It's awful. It's just like, it's literally the worst and we've all just been putting up with it. And the, the new use cases, you pick up the phone and you call

and an AI agent answers and says, hey, how can I help you? Immediately, they answer immediately. You're not on hold. You're not waiting. There's no callbacks. None of that. There's just someone there to respond. It is a computer, not a human, but you are having a conversation. And like you said, the insights that you can gather from that rich conversation versus someone pressing a number

goes so far. And also we're not dealing with annoyed people who have been waiting on hold for 30 minutes or an hour. And now you're just getting a pissed off customer who's not going to tell you what's actually going on for them. So exactly. I hate to be boring about it, but it brings us back to data, right? Which is it's not boring at all. We're here for it.

If you unless you have the infrastructure in place and you're organized in a way that you can take that sensory data and those insights and do something with it. That's, that's a wasted investment, right? We have a client right now that we're working with and I shan't name them. And I won't hint at, you know, who they might be there. They, they have a very sophisticated data environment and they're serving consumers, right? They literally don't have in their environment right now the ability to collect all of the interaction data that they have with their client.

A very sophisticated company, very sophisticated data environment. They don't have the ability to know that I called the service center and I abandoned the call or I was online with a chatbot and I abandoned that chatbot session, and then I called the call center. When you connect to the call center agent, call center agent has no idea that I was in a chatbot session and abandoned for some reason.

That's a lack of intelligence, that's a lack of joined upness that you will need to be able to do, that's a lack of joined upness that you will need to be able to do. That's a competence that organizations will need if you're to fully exploit the technology. Because it will be that full 360 connection that basically makes the difference between brands that successfully deploy this technology and get a massive leverage effect off it, and brands that basically do it, excuse my language, half-assed because you're not going to be able to basically deliver on the promise.

The promise is I'm going to give you an interactive experience that's personalized and yet I don't have the data connections and the tools on the backend to make those connections to make that experience come to life. Again, you've got to come back to do you have that infrastructure in place? Have you thought through the data? Are you collecting every piece of data you can about your consumer interactions, making it available, making it connected, and making it, surfacing it for these new AI techniques? If you can't say yes to those things, then you have foundational work to do before you start spending a lot of money retooling the front end and your AI experiences.

That would be my perspective. I could not agree with you more. I've had this experience as a consumer where I've tried to use a chatbot for an airline ticket. Chatbot was not able to help me. I tried multiple times, get on the phone. There's an hour long wait. So as I'm waiting for an hour, because I have to solve this problem, I'm stuck now.

I'm in the like death hole of customer service. Yep. So I'm chatting with the chatbot at the same time, seeing if I can get it to do the thing that I'm waiting on hold for. And I'm really not able to get anywhere. I have to log in 10 times. You know, it's just like, and this is a, it's a major airline, you know, it's like, and I'm like, how is it possible that they haven't figured this out?

And then I get on the phone with the person and I'm like, I've been trying to use the chat bot and they're like, oh, I can't see that conversation. I'm like, how can you not see that conversation? Yes, exactly. Why is it so hard for organizations to, I mean, it's probably a silly question, because we can assume, but I'd love to understand your perspective. Why is it so difficult to connect the wires on the back end? I think there's a couple of dimensions to that. One is just commitment.

And again, this comes back to what I said about board level and CEO. So CEOs and boards need to understand what needs to come together. They don't need to be the tech, they don't need to know the nuts and bolts, they don't need to know wiring diagrams, what have you. But a CEO today does need to understand the critical importance of joined up data and curated quality data to serve these new experiences. They need to understand that fundamentally. And then they need to prioritize scarce resources, because all organizations

have scarce resources, to basically align to delivering on that experience. And that comes back to a very fundamental thing. Too often we walk into organizations when we're doing advisory work, and they haven't even done the basic strategy work about why am I even doing this? What are the outcomes we're looking for? Have we prioritized? It's like, do we have a model that says, strategic priorities for the firm, growth, efficiency, customer experience, whatever it is.

Here's the cascade of all of the different things that we invest in to deliver on that promise on those strategic objectives. A lot of times that basic work isn't done. If you haven't done that strategy work that joins the dots, then how do you know what you're prioritizing? How do you give your IT organization or the partners that you're paying direction and where to spend the valuable resources

and the scarce resources they have? So a lot of it comes down to strategy, prioritization, and then direction and motivation from C-suite, board level, senior directors in the organization. Once you've done that, then basically it's technology and complexity. And a lot of organizations, we all know this, right,

have grown their technology environments over 40 plus years. They're complex. It's not easy to wire up system A with system B because they were built to serve different purposes 10, 15 years ago. So a lot of what organizations are now having to think through is a lot of this has come out of the sort of the move to the cloud is how do we become a platform based

business, how do we think in platform speak? What that means for a CEO is, do I think of my business not as a set of functions that all have their discreet purpose, but as a unified business and the platform of the business is how those business units work together. If you think about your business that way and serving your customers that way,

then you have to think about the technology that way. A lot of organizations are fairly behind the eight ball in terms of having a vol day true platform strategy that serves that horizontal need in the business. An important part of that platform strategy is your data architecture and your data strategy, how data joins up and flows seamlessly across the business. There are multiple layers to that. But again, I come back to it begins with CEOs begins with, do you understand

your strategic objectives and priorities? And are you setting priorities for the business in a way that aligns to those strategic outcomes? But what about, you know, I think something that I hear a lot from customer experience leaders is, my C-suite doesn't get it. They don't get the frustrations the customer is experiencing. I'm curious to know if that's something that you see in

your clients and how do you overcome it, if so, because you're smiling. I'm smiling because this goes back to very formative experience in my career at Microsoft. So back in the late 90s, that's a few years ago now. Microsoft faced some fairly existential risks as a business. It wasn't the behemoth that it is today, it was pretty dominant in

the desktop space and with office and what have you. But we were trying to get into the enterprise and I was part of the enterprise leadership team at the time. And our customers thought we sucked, right? As a business, we were arrogant. Our products didn't deliver what they needed. We didn't listen. Right. And they were making other choices, right?

They were going to go buy other technologies. Scott McNeely was selling the network computer. That was gonna be the answer there. There were existential threats to Microsoft's business and its growth that needed to be dealt with. I led the team that basically put in place the first research exercise to go measure real customer and partner satisfaction

across the entire Microsoft business. We built an annual survey that surveyed 24,000 customers and partners. What we did with that data was transform the way we operated as a business. Everything from how our executives were compensated all the way through to frontline staff. We moved from just revenue and growth to

a balanced scorecard of customer satisfaction, revenue and growth, right? And then we put that onto the scorecards of all of the engineering leadership. So they had to build products. And we used all of that intelligence to basically recalibrate how we were building the products, how we included customers

in that product development process. And everything you see today from Microsoft, with that button you click that says, hey, how happy am I using Word or Teams or what have you? That button is a direct descendant of that original work. Organizations have to have those sensing mechanisms. Too many organizations in our experience really do not understand and that starts at the C-suite,

what their customers actually want. It's surprising to me, it's surprising to my partners and what have you. We walk into an organization and say, hey, what do you think your customers want? And they'll tell you, here's what we think our customer priorities are. And we say, well, tell you what,

we're gonna do an independent third-party survey of your customers. We're actually gonna go and talk to some of your customers Here's what we think our customer priorities are. And we say, well, tell you what, we're going to do an independent third party survey of your customers. We're actually going to go and talk to some of your customers. And nine times out of 10, what we hear is different from what they think.

And that's a problem. You need to build an organization. And again, starting at the leadership level, that respects and has the tooling and has the processes to truly understand what your customer's real needs are, what their priorities are. And as a CEO, you have a fiduciary responsibility because the only purpose your firm exists

is to sell something to a client. If you don't fundamentally understand what that client wants, how do you know you're building the right thing, making the right investments? That's a CEO level Query so when I when somebody says to see it's I'm having a hard time understanding The CEO is having a hard time joining the dots between customer experience and let's say revenue There's some education to do there. That's not on the person who's trying to make that case

That's on the CEO and there are tools and tools and what have you that we use to help educate C-Sweets about the importance and the linkage. But having a third party partner come in, you know, not to be self-serving about it, we're in that business, but having a third party partner come in and actually do that independent voice of the customer work and bring that back inside the organization. Having an organization basically bring in a third party to do that third party validation of customer perspectives, I think is critically important. All organizations should do that. You should have some mechanism for having an independent party really go out and do

voice of the customer work, bring that back in, and that voice of the customer work should not be buried five levels down in the organization. That should get C-suite level visibility. Something that I'm thinking about as you're talking, because I also do voice of customer work with my clients as a customer experience consultant, I totally agree, an independent party, someone who one, knows how to ask the right questions,

but also is not the CSM or the person that that company, that your client is speaking to on a daily basis because they have a relationship. They're not going to want to hurt your feelings. They'll hurt my feelings all day and no one cares. But there's, when it comes to AI, just bringing that back into the conversation. There's Incredible tools now that can help us to listen Across all these channels to connect the dots between different information sources and bring it all together And I feel like if there's one place to start and tell me if you agree or disagree with this It's if we're implementing AI. Let's use it to listen so that we know where we should be investing further in AI. Would you say that's true or not true?

I could not agree more. There's an incredible set of new technologies out there that are leveraging sort of advanced analytics and AI to really sort of crunch through all of that sensory data, right? And give you insights that we weren't able to get previously. So I'm a huge fan of using those tools. I will say that you have to have the data first. It comes back to you have to have a mandate that you're going to invest in the collection of the data, right, and that you're going to have that data available. And then there's a never ending sort of plethora of tools right now that can

sort of help you analyze it and AI is an important aspect of that. I also think when it comes to listening to our customer, understanding what is it that our customer actually needs and wants, which is so critical. As you said, the business doesn't exist without selling something to the customer. So the customer needs to want it and really honing in on what is it that that customer wants is so important. That being said, in this moment of AI, I don't think most people, most consumers, most business customers even know the possibilities, know what is possible.

And I actually have a client right now who is in the hospitality space and is trying to disrupt the industry. And I'm out there talking to customers on their behalf, really trying to gather the information from the customer. And the CEO makes a good point, and I love your thoughts on this, is that they don't even know what they want because what we can do is beyond what they've ever seen before. How do you tackle that type of sentiment? Steve Jobs said that back in the day, right? Which is, you know, there's only, there's a limit, there's a limit to what you're going

to learn from listening to customers, because customers don't see the future in the way that you see the future. And part of your role as an organization is seeing the potential in that future and making it real for your customers. Right. And that's really difficult for a consumer. Consumers, you know, in the moment, they're dealing with the issues that they have today. They have some foresight about what they might want. So your role as an organization is to balance that, right? Which is to have enough sensory perspective of what your customers actually want and to guide some of that, to create some frameworks

for that conversation with your customers. But that acts as a foundation upon which you can build. The problem is building a future on, you know, like building your house on sand, right? If you don't have that foundation in place, because most customers are going to love that future that you're dreaming up, but what they really want is you to deal with today's problems. And if you don't deal with today's issues, you're not going to get permission to sell them the future. So it always has to be a balance, right?

It's always in balance, which is, yeah, your brand may be forward thinking and innovative, and you need to be the ones leading your customers to that future opportunity. But your customers are telling you today that I'm only going to follow you if I have trust that you can actually deliver what you've committed to deliver today and how many companies do companies do we know have spent all their time focusing on that future sort of opportunity and have lost the trust of their consumers in what they're trying to do,

what they're really delivering today, that's going to be done in balance. We have to really understand our customers so that we can bridge the gap between now and the future. If we really understand them, if we know them, if we know what problems and challenges they're facing on a day-to-day basis, then we can use our knowledge that we have from diving into AI and using AI and actually bridge that gap of what's possible. And that's where true innovation comes from.

When it comes to digital transformation, the employee experience is changing. What employees are faced with, both in terms of how frequently we are learning new tools, new mindsets, new approaches to work, and also there's job security on the table, how do you approach the employee experience and that change management

as you go through digital transformations? I have such a critical component of any successful transformation, whether it's the ones we've all sort of worked through in our careers or whether the ones that we advise our clients on, which is, and again, this is not rocket science, culture, people's perspectives, opinions, etc. will

slow any transformation initiative to a crawl if they don't feel they're listened to and engaged. And there are some key elements of that, right? So change, every transformation of this kind requires very explicit change management, right? Which is communications, a structure, but that, and I hate to come back to this, but change management starts at the most senior levels in the organization, right? And again, too many times you walk into an organization and there's some big initiative to deploy some technology, which is going to be very disruptive. It could be as simple as deploying teams inside an organization, right? So like transforming the way people collaborate. That's going to

disrupt the way people work. It's going to be uncomfortable. If your CEO and your senior leadership team are not on the same page, and they're not all articulating a vision about the future of the company, articulating a vision about the future of the company, articulating why this is important to the future of the company, and constantly reinforcing the importance of everybody being engaged and then having the mechanisms just as you do with your cluster, you should be doing with the customers, to listen to what people's concerns are, folding those concerns into sort of tactics and strategies

that you're executing from a change perspective, then you're not going to be successful. You need all of those things to come together. And I have to say it starts with, again, comes back to CEOs and the C-suite having the ability to articulate a vision, being passionate about it. It's simple stuff. It's having a CEO be able to tell us tell a story to the staff and the employees about why this is an important transformation, what this is going to do for the future of the company, why this is a key to our success, right? Setting that agenda, and then having a managed change process, again, which listens to the voice of

the employee and the staff, and make sure that you are responding to those concerns because all concerns are real concerns and they have to be dealt with, right? As you go through a process like this. We spend an awful lot of time on that with organizations before we ever start talking about the bits and bytes of the data and the technical implementation.

Because if you don't get those things right, you can spend a boatload of money on tech and it's not going to have any impact on the business. It is so true. The voice of the employee. I love hearing that. I think it's something we don't talk about often enough is that the sentiment, the needs, the feelings that our employees have directly impacts whether they will adopt something or resist it. And we have to tap in and listen to that and they have to feel like they are being listened to in order to feel like they can trust what they're being told. The most frontline employee in a business can slow a transformation down. Those frontline staff that are the folks that actually run

your business that actually make the business operate, if they're not bought in, they're going to be a barrier to you moving the business forward. That's true for every digital transformation. The other thing I would say in bringing it back to customer experience or employee experience in this particular case is, and we're starting to see this, we've seen this over sort of the last decade or so, the experience, the actual digital experience that many employees have has not been world class

for a lot of the tools that we put in front of our employees, right, to say the least. And that needs to be fixed, right? To say the least. And that needs to be fixed, right? Particularly as you start to think about bringing in a new generation of employees into the organization. They grew up with very high expectations of the experience of every digital tool that they used, always being connected, always being real time, world class user experience. And when they come and work in an organization, they want that experience. They want to be always connected. They want to have access to the data that they need.

They want to be able to share their experiences. And they want the experiences of the tools that you're putting in front of them to be just as good as the tools they use on their iPhone or their other mobile phone. That's an expectation. And if you don't get that right, you're going to have a challenge as an organization continuing to hire and retain the talent. And at the end of the day, all organizations survive and die on the talent that they have. If you can't hire and retain that talent, then all of this investment means nothing. Yep. If we are investing in creating a better customer experience and the employees are pushing for that, but their experience is not being improved, let me just think about that. It doesn't

feel good. That is where resentment brews and then people are like, I'm out of here. I can't handle this anymore. I have been one of those people in the past. I'm like, why am I still working in a spreadsheet when I'm doing all this work to create a great customer experience that is streamlined and effortless? Well, I have one last question for you. And that is, what is one piece of advice that every customer experience leader should hear?

I would come back to fundamentals, which is data is your friend, right? And you're going to survive and die on the richness of that data, your ability to collect it from every point of interaction, the ability for that data to be joined up, and the ability for you to increasingly deploy these increasingly sophisticated tools on top of that data. If you don't have that piece of the puzzle done and that foundation built, then you're going

to be challenged. Very, very important advice. Garbage in, garbage out. We need to make sure that our data is nice and clean so that it can be used effectively. Well, Jonathan, thank you so much for coming on the show. It's been so insightful to hear all about your experience leading digital transformations, how companies and C-suite leaders can think about AI and the impact that AI is having on the customer experience then scape today and into the future.

So it's been wonderful to have you. Thank you so much. Lauren, thank you very much. It was a really enjoyable conversation. Thank you.