Experts of Experience
#57 Why Your C-Suite Needs to Embrace AI for Customer Success
Wed, 20 Nov 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. –Are your teams facing growing demands? Join CX leaders transforming their AI strategy with Agentforce. Start achieving your ambitious goals. Visit salesforce.com/agentforce Mission.org is a media studio producing content alongside world-class clients. Learn more at mission.org
Chapter 1: What challenges do organizations face when embracing AI?
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 ModUp, just for the folks who may not know about it.
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 iBall, 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.
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Chapter 2: How can AI transform customer interactions?
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, you experience resistance in every wave of technology, right? So I've been around long enough to have lived through the initial wave of the internet, right? Impacting from its most nascent days and invention all the way through to what it is today. And when the internet surfaced, There was a lot of resistance in Oregon.
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, right? 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 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, right? 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 senses 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.
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Chapter 3: What data governance practices are essential for AI success?
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, et cetera, right? 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, right? So 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. So 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 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 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.
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Chapter 4: How can companies mitigate AI risks?
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 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.
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 that basically drives the outputs from those AI tools.
And so I think getting the basics right on that is the quality of your data, completeness, its organization, how it's joined up, and 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 re-evaluation 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, right, then you can eliminate that. The way we did that with this client is essentially using what's called graph technology, sort of 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. It 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.
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Chapter 5: What principles should guide AI implementation?
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've 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 gonna do that. An agent is gonna 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 our 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, 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? Because it has to work in the culture of the organization, et cetera. Governance is exactly the same, right?
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Chapter 6: How does AI enhance customer experience?
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 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 gonna 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 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 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, 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, 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? 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 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, right? then the outputs from these systems will likely be biased. And there's all different types of bias, of course.
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Chapter 7: What opportunities does AI present for marketing?
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 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.
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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 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.
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 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.
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. Exactly.
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