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Experts of Experience
#57 Why Your C-Suite Needs to Embrace AI for Customer Success
00:52:00
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.
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- [00:00]
Jonathan Murray
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.
- [00:23]
Lauren Wood
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 strategy Officer at MODOP and co author of Getting Digital 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.
- [01:02]
Jonathan Murray
Very nice to meet you, Lauren. Delighted to be here.
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Lauren Wood
So tell me a little bit about ModUp. Just for the folks who may not know about it.
- [01:10]
Jonathan Murray
ModUp 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 ModUp about a year ago and we do full digital transformation strategy work. So we do everything soup to nuts, board level growth strategy for firms all the way through to technical implementations and 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.
- [02:30]
Lauren Wood
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 how 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.
- [02:50]
Jonathan Murray
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, copy, et cetera, off those that data driven, 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.
- [03:55]
Lauren Wood
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?
- [04:16]
Jonathan Murray
I think one of the interesting things is that we experience, you experience resistance in every wave of technology, right? So I'm, I've been around long enough to have lived through the initial wave of the Internet, right? Impacting and you know, from its most nascent days in invention all the way through to what it is today, and when we, when the Internet surfaced, there was a lot of resistance in organization. What's that going to, that's not a technology. It's going to apply to us, right? That's not, that's not going to impact how we serve our customers. That, you know, we don't understand. It does, we don't, it's. There's a lot of risk associated with it. We're not quite sure how to leverage. 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.
- [05:08]
C
Right?
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Jonathan Murray
So 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. And they're asking those questions, which is how do, what's that going to do to our business? What's it going to do to the markets that we serve, the customers? How is it going to reset customer expectations?
- [05:33]
C
Right.
- [05:34]
Jonathan Murray
And then what are the skill sets we're going to need as an organization? How do we embark on that journey and 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. So 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 new cycle, the hype around the technology and whether it's real and what the real risks are.
- [06:27]
Lauren Wood
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.
- [07:00]
Jonathan Murray
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.
- [07:09]
Lauren Wood
Yeah.
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Jonathan Murray
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.
- [07:15]
C
Right.
- [07:16]
Jonathan Murray
Which is, you know, if you're talking about AI at its most advanced level, you know, large language models and cognitive AI, et cetera.
- [07:24]
C
Right.
- [07:25]
Jonathan Murray
There's hallucinations, you know, there's a whole discussion about, well, you know, I go into ChatGPT and I type a prompt into ChatGPT. It's not always correct.
- [07:35]
C
Right.
- [07:35]
Jonathan Murray
So what, you know, 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 standards, engineering standards space. They're one of the largest organizations that sets engineering standards for their industry and 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, which we did 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 human, the human in the loop, right? 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.
- [09:14]
Lauren Wood
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?
- [09:43]
Jonathan Murray
So I think a lot of this actually comes back to how well organized your data is. And 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. And so I think getting the basics right on that to say, 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 like have a natural language conversation with a client. So that's, that's job one, right? 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. You know, without sort of going down a rabbit hole on 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.
- [11:18]
C
Right.
- [11:18]
Jonathan Murray
Then you can eliminate that. The way we did that with this client is essentially using what's called graph technology. So building a knowledge graph, which establishes the relationship between all of 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 and says, okay, I know what the relationship between these is, and I'm going to serve you back an answer that is accurate. And 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. So there are different ways of solving this from a technical perspective, but data is the foundation. Well formed data is the foundation.
- [11:59]
Lauren Wood
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.
- [13:14]
Jonathan Murray
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, 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.
- [13:51]
C
Right.
- [13:51]
Jonathan Murray
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. That's putting that framework in place is a huge piece of work that has to be done by every organization.
- [14:41]
Lauren Wood
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 our listeners and how they can approach it themselves?
- [15:18]
Jonathan Murray
Obviously governance has to be crafted for every, you know, each organization. It, in a sense is like Agile, Right. 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, right? Yeah, because it has to work in the culture of the organization, et cetera. Governance is exactly the same.
- [15:39]
C
Right.
- [15:39]
Jonathan Murray
Every business is going to be, it 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.
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C
Right.
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Jonathan Murray
Principles that guide all down downstream decision making. And I think if you're embarking on the AI journey without having established what those guiding principles are.
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C
Right.
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Jonathan Murray
Then you're, then you're opening yourself up to risk.
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C
Right.
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Jonathan Murray
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.
- [16:41]
Lauren Wood
What's an example of a principle that.
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Jonathan Murray
Models should be explainable and understandable as an example? Right. That would be a key principle that when you develop a model and whether you're reusing ChatGPT or something like that, that you're able to explain why it came up with the answers that it came up with.
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C
Right.
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Jonathan Murray
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.
- [17:10]
C
Right.
- [17:11]
Jonathan Murray
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. 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? 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.
- [18:02]
C
Right?
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Lauren Wood
Yeah.
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Jonathan Murray
And then the eliminate the management understanding and elimination of bias.
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C
Right.
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Jonathan Murray
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'll be a critical. 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.
- [18:51]
Lauren Wood
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?
- [19:30]
Jonathan Murray
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 know, 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 going to deploy it in the business. The IT team will deal with that, or we'll let marketing work for the IT team to sort of figure out how to go do this. This is a board level and C suite level set of decisions that have to be made.
- [20:24]
Lauren Wood
Say goodbye to chatbots and say hello to the first AI agent. Agent for Service Agent makes self service an actual joy for your customers with its conversational language anytime on any channel. To learn more, visit salesforce.com agentforce 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.
- [21:17]
Jonathan Murray
I was going to 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. 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.
- [21:53]
Lauren Wood
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's 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.
- [23:10]
Jonathan Murray
The one that everybody sees is sort of transforming the interaction model. So basically how people will interact with organizations, right? 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, right? So the next generation of user experience for any service that we consume, I think will have a Voice component will have a natural language component to it and organizations that will build a more, that creates, you know, 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.
- [24:17]
C
Right.
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Jonathan Murray
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.
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C
Right.
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Jonathan Murray
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 is 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 going to 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 products from that business?
- [25:26]
Lauren Wood
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, 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.
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Jonathan Murray
You know, it's sort of ironic, right? It's ironic that the very thing that might help us build a deeper connection to our consumers.
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C
Right.
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Jonathan Murray
Is actually the artificial intelligence.
- [26:27]
C
Right.
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Jonathan Murray
Is that because of the cost structure around sort of having people in sort of service centers talking directly to clients and having that to be a very structured process, we're all frustrated with the IVR experience, et cetera. 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, you know, the help desk. There's a sort of a certain irony to that.
- [27:10]
Lauren Wood
Oh, completely, Completely. It is personally the thing I am 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 Saks Fifth Avenue where someone picks up the phone and calls because they want a smaller sweater.
- [27:46]
Jonathan Murray
Yep.
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Lauren Wood
So instead of. And they showed the. Here's what it would normally be. You know, if you're calling for this, press 1. If you're calling for this, press 2. If you're calling and, you know, you go 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 case is 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.
- [28:49]
C
So.
- [28:50]
Jonathan Murray
Exactly. I hate to be boring about it, but it brings us back to data, Right. Which is, it's not boring at all.
- [28:55]
Lauren Wood
We're here for it.
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Jonathan Murray
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 a wasted investment.
- [29:10]
C
Right.
- [29:11]
Jonathan Murray
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 have a very sophisticated data environment and they're Serving consumers.
- [29:23]
C
Right.
- [29:24]
Jonathan Murray
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. Right. 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.
- [30:09]
C
Right.
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Jonathan Murray
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 back end 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.
- [31:06]
Lauren Wood
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.
- [31:36]
Jonathan Murray
Yep.
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Lauren Wood
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 button. They're like, oh, I can't see that conversation. I'm like, how can you not see that conversation?
- [32:04]
Jonathan Murray
Yes, exactly.
- [32:07]
Lauren Wood
And why is it so hard for, for organizations to, I mean, I know 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?
- [32:22]
Jonathan Murray
I think the couple of, 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? Right? It's like, do we have a, do we have a model that says strategic priorities for the firm? Growth, efficiency, customer experience, whatever it is. And 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 why you're, 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 sort of 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? And what that means for a CEO is do I think of my business not as a set of functions that all have their discrete purpose, but as a unit, unified business. And the platform of the business is how those business units work together. And if you think about your business that way and serving your customers that way, then you have to think about the technology that way. And a lot of organizations are fairly behind the eight ball in terms of having evolved a true platform strategy that serves that horizontal need in the business. And 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. So 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?
- [35:37]
Lauren Wood
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. And 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.
- [35:59]
Jonathan Murray
So I'm smiling because this goes back to very formative experience in my career at Microsoft. So back in the late 90s, go back. You know, that's a few years ago now. Microsoft face some fairly existential risks as a business. It wasn't, 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.
- [36:33]
C
Right?
- [36:33]
Jonathan Murray
As a business, we were arrogant. Our products didn't deliver what they needed. We didn't listen.
- [36:39]
C
Right.
- [36:40]
Jonathan Murray
And they were making other choices.
- [36:43]
C
Right.
- [36:43]
Jonathan Murray
They were going to go buy other technologies. Scott McNeely was selling the network computer. That was going to 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. And 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, right? Organizations have to have those sensing mechanisms. And 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, okay, 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 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 ten 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 customers 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 somebody says I'm having a hard time understanding the CEOs 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 what have you that we use to help educate C suites 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.
- [40:11]
Lauren Wood
So 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, 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 should we should be investing further in AI. Would you say that's true or not true?
- [41:08]
Jonathan Murray
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.
- [41:20]
C
Right.
- [41:20]
Jonathan Murray
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 again 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 so to help you analyze it. And AI is an important aspect of that.
- [41:50]
Lauren Wood
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 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'd 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?
- [42:56]
Jonathan Murray
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, 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 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.
- [44:36]
Lauren Wood
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.
- [45:00]
Jonathan Murray
Yeah.
- [45:00]
Lauren Wood
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, you know, job security on the table. How do you approach the employee experience and that change management as you go through digital transformations, that's such a critical.
- [45:30]
Jonathan Murray
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, et cetera, will slow any transformation initiative to accrual if they're not. If they don't feel they're listened to and engaged. And there are some key elements of that.
- [46:00]
C
Right.
- [46:00]
Jonathan Murray
So change. Every transformation of this kind requires very explicit change management.
- [46:07]
C
Right.
- [46:08]
Jonathan Murray
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.
- [46:16]
C
Right.
- [46:17]
Jonathan Murray
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.
- [46:29]
C
Right.
- [46:30]
Jonathan Murray
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 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 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.
- [47:33]
C
Right.
- [47:34]
Jonathan Murray
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.
- [47:49]
C
Right.
- [47:49]
Jonathan Murray
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.
- [48:08]
Lauren Wood
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.
- [48:37]
Jonathan Murray
The most frontline employee in a business can slow a transformation down. Right. They, you know, 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.
- [49:26]
C
Right.
- [49:26]
Jonathan Murray
Particularly as you start to think about bringing 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.
- [50:23]
Lauren Wood
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. I mean, just think about that. It doesn't feel good. You know, 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?
- [51:01]
Jonathan Murray
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.
- [51:34]
Lauren Wood
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 landscape today and into the future. So it's been wonderful to have you. Thank you so much.
- [52:04]
Jonathan Murray
Lauren. Thank you very much. It was a really enjoyable conversation. Thank you.