Accenture Lead Explains Why Customers Are Leaving non-AI Companies

Two-thirds of the folks that we surveyed were actually comfortable with Gen.AI. If you look at OpenAI and DeepSea, the adoption of their specific apps have been really some of the fastest adoption ever.
They feel like they are being bombarded by generative content. The question of trust comes to mind.
How can we trust that this is real? How do we know that what is being told to us is true? 60% said that they were concerned with bias and misinformation. 88% of those that we surveyed that use Gen AI Weekly think that AI dramatically enhances their online experience.
83% of those folks think that AI can be more creative than humans. That statement should cause a little bit of pause in my opinion.
I'm kind of already nostalgic for the old days. It sort of places human created content almost on a pedestal now, right? That actually can become the premium thing.
Consumers are now going to be looking for the human element. That human touch is going to be the differentiator.
Hello, everyone. Welcome to Experts of Experience.
I'm your host, Lauren Wood. Today, we are talking about one of the most important shifts happening in customer experience, the impact of AI on both businesses and consumers.
We know that the topic of AI is consuming all of us these days. And while the promises of AI bring efficiency, personalization, engagement, the reality is far more complicated.
Companies are struggling with implementation, employees are wary of change, and consumers have mixed feelings about how trustworthy AI-generated content really is. So today we have Kevin Yalowitz, who truly has a front row seat to all of this as the software and platforms industry lead for Accenture.
And while he is working with top organizations, navigating the complexities of AI, tech, and digital transformation, he is really getting to see the challenges of AI adoption, both inside and outside of organizations. Kevin, so wonderful to have you on the show.
Thank you for having me. It's great to be here.
So in our prep call, you shared so much incredible information, and we're going to try to cover as much of it as possible. Let's do it.
First, starting with the consumer. I know that your team has been doing lots of research and studying the impact of consumer behavior and opinions when it comes to AI.
And I'd love to just start off by asking, what are some of the most surprising findings that you were seeing? Yeah, so this is something we've been tracking over the past couple of years. It's just consumer familiarity with AI.
and frankly, what the tipping points are for when consumers are really, truly comfortable. What's interesting, though, is that two thirds of the folks that we surveyed, and we surveyed consumers in 10 different countries around the world, including developed markets and developing markets, but two thirds were actually comfortable withAI.
Like, they're using it. They're comfortable with it, which is actually, if you step back and just think about it, like, that's a material amount of adoption in a short period of time.
And, you know, obviously, if you look at Open.AI and DeepSeek, like, the adoption of their specific apps have been really some of the fastest adoption ever, which is great. But the consumer comfort with that, we think is pretty darn interesting.
Like 50% of the users that have adopted it are actually really comfortable with it. And they're five times more likely to actually pay for it, which is another pretty big barrier, right? Because typically at early days, people don't pay for it.
They just play around with it, right? I think the thing that's particularly interesting though, is that if you look broadly across consumers out in the world, they feel like they are being bombarded by generative content, maybe even more so than they are. Like they think that 60% of their content in search is generative.
40% of the music they listen to is generative. Very interesting data point, but we know that it's not that high, right? We are not to the point where that level of what you consume is actually created by Gen AI.
So there's this bit of a mismatch or dichotomy in which people just, people think that there's more of this happening today than maybe there is. And we're going to dive into that.
I know a little bit later, but it's, it does set an interesting stage. It's so fascinating because I find myself do that where I'm like, is this AI? I mean, it's been so fast where I remember a year ago, seeing my, the first, maybe a year and a half ago, seeing the first AI generated image and being like, oh, whoa, that's crazy.
But it also looks like a computer. And totally, but now it doesn't.
And it's, you know, how, like, how do we know? And so I, the question of trust comes to mind of how can we trust that this is real if it's being said to be real? Or how do we know that what is being told to us is true? And I think that there's also that happening in the background. I'm curious to know what you've seen in your findings.
Yeah. Well, there's also some irony in that statement because there's a lot of mistruth in what humans create, right? I mean, for good reason and for bad, that is actually not a new phenomenon in online content, but there's definitely a heightened awareness of this around Gen AI.
So, of the consumers that we surveyed, 60% said that they were concerned with bias and misinformation. So, that's big, right? That includes folks that see the promise of Gen.AI and want to use it more.
And what we found is that of that group that are actual users, identifying that something is generative content massively increases the trust that exists there, right? And I would argue two other interesting data points that underpin that, specifically, 88% of those that we surveyed that use Gen AI weekly, right? So we're talking active users, but that's an increasingly growing set of folks think that AI dramatically enhances their online experience. And just to go a step further, 83% of those folks, so plurality almost, think that AI can be more creative than humans, which I was a little taken aback by, if we're being totally honest, right? And you talked about it, like AI can do some really interesting generative imagery or video or audio, but that's it.
That statement should cause a little bit of pause, in my opinion. Maybe good, maybe bad, depending on where you sit in the ecosystem.
Completely. I mean, it makes me wonder about, like, what's going to happen to creative industries, for one thing.
And, like, the concept of music creation, I think, is one where my mind goes. Like, it's where does AI play a role here in helping artists create music? But is music going to be enjoyable if computers are creating it? Like, I mean, I guess it will be, but there's something like the magic kind of gets taken out of it if it's not like the genius behind someone who's spent their entire life learning how to create this thing.
And same with visual artists or even advertisers, you know, the creativity of how do we take these consumer insights and translate them into a like wow moment. Yeah.
Is I'm kind of like already nostalgic for like the old days a little bit. Yeah.
No, I agree. I mean, one conclusion you could draw, though, is that users that want to be notified of something being generated content, it sort of places human created content almost on a pedestal now, right? Like that actually can become the premium thing.
And I know we'll get into this a bit later. But I think that the human component there, there is not a broad dismissal that, well, AI is just going to be more creative writ large than humans.
I think it's going to be interesting to see how this plays out. And we're not really at a point where we can predict exactly how it will yet.
We are so early. Yeah.
How is this impacting consumers' purchasing decisions? Yeah. It's early days.
I think what we're seeing is that there is an increased willingness of consumers to see, and this ties to experience, to see AI and Gen AI as a means to improving the consumer experience to do a job that they would normally have to do themselves. So as an example, customer support is something that comes up continually as generally we don't stumble across many companies that are wowing consumers and driving, you know, NPS of 10 all the time.
Right. But I do think that we're starting to see the initial sort of vision of how AI can actually help consumers have just a better support experience when something does go wrong with a product.
And that is by driving consumer behavior. Like we did see that in our data that consumers are willing to gravitate towards services that are being innovative and helping them get to that resolution better.
Which makes sense. That's intuitive, right? I mean, if you think about it, like, yeah, just as if you think about any company you've had a great experience with, like, if you have a problem, it gets resolved quickly, you're happier.
Yeah. Yeah.
I mean, it, there's so much low hanging fruit when it comes to post sales support. Like, and we all know that no one wants to pick up the phone and call a company.
I have like, always, there's like a list of things when call this company, call the bank, call. And I'm like, I don't want to, because it's going to take half my day and it's going to be frustrating.
What, what if we flip the script on that? And I think that that's where, when we think about like purchasing decisions where every company obviously wants consumers to be purchasing their products and services, if you have a good reputation, because that post-sale experience isn't always something that you're leading with. But if you have a good reputation and like my ears always perk up, obviously, if a friend's like, oh, they have the best support.
Like you have to go with this company because if you have an issue, they're going to help you. And I'm like, okay, that's kind of like the ultimate gold for me.
If I know that the pains of actually getting what I need will be solved, then I want that. And it's interesting.
I mean, I think a good example that probably the entire audience has experience with is Netflix, right? I mean, Netflix has become really good at proactively telling you that there is an issue, they are aware of it, and like what the derivative of that issue is, right? If it's your local internet service provider, and that's causing degradation of your stream, like you are made aware of that. And it's something on their end, like within their content distribution network, they flag it and they tell you that in advance.
And I think being proactive is obviously a big point, which will be enabled more by AI going forward, right? It should enable more players to be able to do that. I also think equally interesting though, to your point about your friends mentioning this.
I mean, one example that we hear all the time come up this actually came up with a client who was like who what is the best example of just great end-to-end support experience and their example was trader joe's comes up all the time right all the time right and trader joe's i mean sure you can go and return something easily but i goes a little bit further, right? Trader Joe's employees can actually take like a bouquet of flowers out of stock and give it to someone because they're having a bad day or it's their birthday. So there's like that human touch will be a little bit interesting to see like how would AI replicate that? Because AI is obviously going to take maybe a little bit more data-driven approach and probably balance the cost of delivering that with it as well.
Totally. That I would argue Schumann might do better at today.
So there's maybe some offline lessons to learn from Trader Joe in the online world. And we're going to talk about the employee experience in a moment.
So I want to put a pin in this one, but I think just to underscore what you're saying, how can we use AI to enable our humanness to thrive and be less bogged down by technical complexities? Like in the customer experience world for so long, it has been, how can we be more efficient? Efficiency, efficiency, efficiency. How can we answer more tickets, get back to more customers faster? Like just send them a stock response.
Just, just get back to them. It doesn't matter what you say.
That is changing now. Consumers are now going to be looking for the human element.
That human touch is going to be the differentiator. And I'm curious when it comes to like marketing and content, how can companies leverage, if you have any thoughts on this, leverage generative AI while still keeping an air of authenticity and trust? What we're seeing is our clients are increasingly using AI to ensure that the path from an initial issue or frontline support to when you talk to a human is faster, right? I mean, how annoying is it when you have an issue and you call a call center and you have to press eight, 13 times in order to get to an actual resolution, right? I mean, first and foremost, I am a believer and we're seeing this.
And frankly, some of the platforms that exist, the big ones today, Amazon Connect, Google CCAI, they are enabling the entire support process to move much more smoothly. But I think that obviating support is really probably going to be the gold standard.

Because to be very honest with you, you don't really, my hypothesis would be, you don't

really need a human interaction if the problem is solved before it's a problem, right?

Or we see that being an issue is when a problem is a real problem and nothing else can solve

it but a human.

And I think what we're seeing most of our clients make those investments, as I said, is figuring out how do you just obviate the need for support entirely. And you know what I think is an important piece of this is that when the problem happens, human emotions start to get involved.
A hundred percent. And this is my take on it.
If you have an angry person, you need a person to connect with them and bring them back down. It is very, very difficult for AI to emote and show empathy so that someone's, when like literally if your amygdala is like activated and you're like, I am angry, I am in like a fight or flight mode.
That's when you have to have a human involved. That's when it's a human conversation.
And if we can avoid those high emotional experiences through frustration or things just not working the way they're supposed to or great disappointment, if we can avoid that altogether, then yes, AI can solve a lot of the problems. And so this is where the call for proactivity is so ultimately clear.
We can just get ahead of it. Yeah.
And I think, you know, if we look over the past five to 10 years, like five to 10 years ago, most companies, right, whether they're tech companies, a travel company, or, you know, a retailer, we're doing some initial measurement, right, that would that would allow them to understand that, wow, investment upfront to ensure that you obviate the need for support, or when support is needed, that you solve it properly, has a real impact on customer lifetime value, right? Like, and in a space, and many the companies that I, or many of the segments I just mentioned, being able to retain a customer for longer is dramatically cheaper than having to go acquire a new one. We see this specifically in the software space, right? So we've started to see this shift from doing measurement just to recognize that that problem exists and now being able to actually attribute the savings, which is, which to be fair, has been a bit of a driver of investment into customer experience, right? Which makes total sense.
If you can invest a dollar up front to say $3 later and net new acquisition, it makes total sense. So we've made progress on that front, which I think is good.
Yeah. I mean, it's one of the things that as a customer experience leader, my entire career, I've struggled with deeply because I intuitively know that if we are able to be proactive, if we're able to create a good experience, we will have more customers for longer who are happier and less expensive to support.
Yep. But it's very difficult to say how expensive that really is because the data just doesn't connect.
And I want to dig into this a little bit. How have you seen companies starting to draw those lines using generative AI? What's the opportunity? An AI agent your customers actually enjoy talking to? Salesforce has you covered.
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Yeah. So to be very honest, I think that the jump to actually connect that cost is more clear now than it really ever has been, to be totally honest with you.

Like we've made material progress in that.

But going forward, I would argue that we are seeing clients, particularly in the tech space, have very good understanding of what customer lifetime value is.

Clarity on what consumers want, and are investing upfront, even in product, right, to solve the issues that are causing churn and drop off later in the process. So it actually has ended up being product that's become really the beneficiary of this.
And it is paying dividends. And if we look at any space that has recurring revenue, if you think whether it's Amazon Prime or a premium Spotify subscription, in every case, it's cheaper typically to retain a customer than to acquire a new customer.
Specifically in the environment where, I mean, if you think about it, telcos have been in this dynamic for years, right? And I actually think T-Mobile is a great example here, right? I mean, T-Mobile took an experience that was very much driven by lock-in, contracts, and they created the uncarrier. And they totally flipped the script on what experience meant as a wireless consumer for those of the audience in the US.
And they went from being, you know, what, fourth in market share to, you know, they're now very near the top. And their lifetime value of a customer is longer than it ever has been.
And it's proven that investing in experience is actually ends up being better than investing in solving problems on the back end. Thank you for bringing up that example.
It's actually such a great case study on customer experience because it's exactly that. Instead of locking them in, instead of forcing someone to pay us money when they're resenting us for the amount of money that they're having to pay, how do we create ease? How do we make it so that they want to be here? I think it's such a great example.
We have in the tech space where I spend most of my time, we see a lot of players building their own homegrown infrastructure to measure this sentiment and understand what are the things that are causing consumers to be frustrated and engage with support. And then how do we solve that from a product perspective? But I will tell you that even at absolute experienced leaders, there's oftentimes a pretty massive divide between product teams and support teams.
Like support teams solve problems, product teams build cool product. And we believe that connecting those two and using AI to ensure that there's a real understanding of what those two parties need to do together is a major opportunity to get this right.
And it's not, it's early days, right? I mean, it's still. But I think that this is where AI is really enabling us to build that connection.
Of course, there is the connection in terms of the, are both teams aligned in what their vision is? Is there like top down alignment on here's the experience we want to create? I think that's always first. but then there is how do these two teams actually play together and how do they see eye to eye and it is so often that the customer experience team and i'm sorry to all my cx people you're kind of

annoying sometimes because you're like, here's all the problems. We have all these problems.
Why is no one solving the problems? I get it. I feel you so deeply because I've been there.
And then the product team is like ruthless prioritization is essential. And so how do we see the product team almost as our customer in saying they need to prioritize? So how do we position information so that they see what's in it for them so that they can understand that information and they can make prioritization decisions on it? And this is where AI is really helping us because if you have a great customer support tool, it is automatically tagging trends for you.
No longer do we need to then say to our team of, you know, whether it's five people or a hundred people, oh, we need to tag about this, you know, product bug. Every single time you see one of those tickets, click five times to put the tag on it so that we can see how many people reached out about this thing.
AI is helping us, one, track all the inbound, but also see where issues are happening on the product itself so that we can connect those dots and see those trends and tell that story in a more tangible way. And this is where I think AI investment is so, so, so necessary

because the investment in understanding the data

or collecting the data, that unstructured data,

and then processing that data

allows us to create collaboration across teams

that ultimately makes the customer experience better.

So that's something I'm really excited about.

It is.

And as I mentioned before,

the availability of support platforms now, such as a CCAI or an Amazon Connect, are sufficiently available that this is no longer something that only the enterprise can use. The mid-market and SMB now can have the same visibility into that data that an enterprise did exclusively five years ago.
And that's really exciting, in my opinion. Yes, I totally agree.
It's democratized. We can all have it.
It's all there. Totally.
Well, and as an example, another example that I think is pretty exciting, if you look at what Shopify is doing. Shopify is really acting as the enabler of SMBs in the mid-market online.
And they are building tools specifically to help with content creation and make the experience of a quality online interaction better. But then on the back end, support tools that actually enable your five-person SMB or one person SMB to be able to tap into the same measurement ability that a large tech company would have.
That's pretty darn exciting, I would say. It's really exciting.
And I think it's really exciting for us consumers. We are all sitting at the cusp.
We're the beneficiary. Totally.
We are sitting at the cusp of much better experience across the board. And I do think that it is going to really influence our purchasing decisions.

How well a company is able to understand your needs,

deliver to your needs

and do so efficiently.

All the companies

that are not investing in AI

and their customer experience

to drive that improved

customer experience,

they're going to be left in the dust.

Yeah.

In my opinion.

It's table stakes.

There is one point

that we would be remiss not to touch on here, which is we talked earlier about experienced teams and product teams and how they work together. I would argue that in addition to having great signal, it's really important to have aligned OKRs.
If you don't have aligned goals, if the product team is not incented to reduce support cases, and the support team is not incented to ensure that product is aware of what should be on the roadmap based off of the big pain points that are coming up, this will never get fixed. And I know we're going to dive into this, but like the organizational barriers to AI, the human organizational barriers are probably the biggest bird adoptions that exist today today, right? And it's who solves for that and does so aggressively and proactively, but I think it will probably win in most of these segments.
I'm so glad that you bring this up. It is so, so incredibly important.
And it also goes for not just product and experience teams. It goes for all teams.
If you want collaboration, people need to be going in the same direction. And it is so often because, I mean, this is my hypothesis, and I'd love to hear your take on why is this problem so pervasive in organizations as we get bigger? In my opinion, it's easier to create our own boxes and operate in our departments.

Like, we have our own thing here and getting someone else's like goals involved. It just makes it complicated.
If we're like trying to then we're both trying to like build a product really fast and we're trying to make sure that the experience team is like less burdened by complexity. It's just like, it's too many things to focus on.
So we just try to focus on one, but that's doing a really big disservice to the company, the customer and the organization or the people in the organization as a whole. That's my take, but I'd love to hear yours.
I think it all comes down to customer obsession. Candidly it does like, and you can read all about this in the Everything Store about how this was baked into Amazon's DNA in its early days.
But the larger you get, the easier it is to not have every individual be customer obsessed. Right? If you're working at a 10-person startup and your sales team sells products to an end user and they're unhappy with the product, like the entire company is going to know about it.
And the entire company is going to galvanize around solving that problem. But as organizations grow, it's very easy to, yeah, well, we don't in product, we don't maybe deal with the customers directly.
That's either sales or support slash experience, right? And I think regrounding on being customer obsessed probably will be the thing that separates great companies from good companies going forward, especially in the tech space. Yes.
And that's, it sounds like 101, but like, it's something that oftentimes gets overlooked. It is so much easier said than done.
Why is that? Well, I think to your point, as organizations get larger, there's there become silos and your your internal metrics become what you are primarily focused on. And it's very hard to step back and say in an organizational level, what are we going to measure that is indicative of true customer happiness and customer obsession? And I think at the end of the day, it just requires the conviction and gravitas to say, we're going to measure everyone based off this.
Because at the end of the day, it directly correlated to revenue. I think it's also...
When I see organizations that are really doing this well, it is a top-down obsession. The Amazon example comes up all the time.
They had an empty chair. Jeff Bezos always would make sure there's an empty chair in the boardroom and that represents the customer.
And it was from him that he enforced that the customer obsession really existed, exists still. And I think that that's really where it starts.
But then there also needs to be an approach and a strategy to that customer obsession. It's not just we're all obsessed with the customer.
Well, what does the customer think? It is that measurement. It is that ensuring constantly that all the teams are aligned and remembering what we are here to do,

especially for the non-client facing teams. Product is the obvious example.
Engineering. I even think of like legal or finance, like just because you're not speaking to the customer every day does not mean that your actions don't impact them.
Everyone's actions impacts the customer at the end of the day. It's just like, that's what we're here to do.
So that's what happens. and we have to remember that.

So in that, I mean, but that it's honestly like a perfect segue to something that, that I know we wanted to cover today, which is if you think about the promise of AI, uh, from an experience perspective, right. End to end experience in an organization, we can talk all about the barriers that cause that, right? But we've seen lots of AI built into the front end of customer experience.
Gen 1 is mostly with chatbots, which has been okay, right? It's scratched some of the itch. But realistically, if we think about this pivot to agentic AI, where AI is going to do jobs, and it's going to solve work to be done in a faster, more meaningful way.
If you make your F&A organization more efficient to ensure that invoices are paid faster, as an example, or that your books are reconciled more quickly, all of that actually does have a trickle-up effect to the customer, right? So we're at this interesting point where I think we're going to start seeing agentic AI help to clean up some of the organizational debt that exists that is hampering customer experience, but maybe at a second order, you know? Yes. What is going on inside of an organization is felt by the customer, whether you want to believe it or not.
When you are working with a company as a consumer or as a business and things feel complex or complicated or unclear, I guarantee if you look under the hood of that company,

you see that same complexity underneath the surface.

And it seeps out.

And I think it's just really important for us all to remember, especially leaders,

that if that is the experience of your employees inside,

it is going to be the experience of your customers outside.

And so applying AI internally is a key piece of this customer experience improvement that we're talking about. But as you and I have spoken about, it is complicated and a lot of companies are struggling.
And can you tell me a little bit about what are some of the challenges that organizations are facing as they adopt more AI within their organizations? Baseline, right? And most companies are solving this in some form or another, but baseline is typically the data in a company to enable AI to actually do a job, right? It's super messy. So we spent the past five to 10 years,'m using that window, because it kind of is the right window for a lot of things around AI.
We spent a lot of time with companies just helping them clean up their data to ensure that they can they can run effective AI on it or machine learning on it. So that was sort of v1.
Yeah. And then I think as we moved to v2, was sort of the co pilot phase, right co-pilot phase in which your sales team could use,

you know, Microsoft sales co-pilot, for instance,

to better understand a lead before they have a meeting.

And therefore they could grow,

just grow the sheer number of clients

that they're talking to in a given day or a given week.

That's great.

But I really think that it's going to be this next step

of agentic AI where we're seeing AI do actual work, right? Like processes and workflows are going to be handled in part or in their entirety by AI. And I think that's wildly exciting.
But to your point, there's a lot of barriers to actually making that real. It's akin to bringing on an entirely new workforce to your organization.
And if you had an organization that had a thousand people and we said, we all of a sudden have massive funding influx, we're going to bring in another thousand people to do functions A, B, or C, that would be wildly disruptive, right? Massively. So this is an opportunity in my mind, and we're seeing this with our clients, to stop, look at the areas where wastes exist or where efficiency is possible and look at processes end-to-end.
We mentioned F&A, like just thinking about invoice to payment, like what is causing breakage in that process? And we've seen companies, particularly platforms that have customers on both sides, right? The buy side and the sell side. If you don't pay the sell side in time, they're really not happy.
And that sends NPS scores way down. So getting stuck into that is really important, but it is arduous.
Like it is arduous and it's going to bring about the same growing pains that you would have, as I said, bringing on an entirely new workforce. And it's also managing like, what are they allowed to do? What are the rules? 100%.
Yeah, exactly. What information do they have access to? It's like, we have to make these types of decisions and, and probably more so, I haven't rolled this out myself, but more so than a human.
I mean, you can't say like, oh, they should know better. Like, no, it's AI, you know, like we have to create really clear guardrails and they are very good at following direction.
It's like the best intern you could ever imagine, but like they still just came out of school. Like you still have to give them the sandbox for them to play in, you know? Exactly.
Which, which I would argue is why there's, there's actually, this is a good news story for many employees because in many cases, it means they're going to be able to have a force multiplier on their impact, but they're going to have to make sure that those interns, if you will, get the right direction and can be nudged the right way when something goes wrong or they enter unknown territory, right? I mean, this is huge transformation, probably a greater level of transformation than most organizations have ever faced. I would say that's right.
As companies are approaching this, which I think this is also, it's also transformation that every company, is it fair to say every company will go through at some point over the next few years in some way, shape or form. How should leaders be thinking about this transformation in terms of readying their team for this change? So we talk a lot about top-down versus bottom-up.
And I think in this case, what we're seeing is that in order to bring in material change, right, where we say we are going to use an agentic architecture, we're going to use a vertex agentic architecture to completely rethink our marketing workflows. As an example, that's not something that you really can do from a bottom-up approach, right? It requires exposing sensitive company data that your CIO is signed off on and frankly, exposing the internal workings of a company at a level that you wouldn't really want to happen bottom-up, right?

So we think that the top-down approach is kind of a requirement to do some of this big instantiation, particularly of agents into workflows. However, we also think that having a bottoms-up approach and allowing your teams to have the wherewithal with the right sort of private privacy boundaries to play with new AI tooling can actually surface some really exciting things.
And I'll give you just a couple examples. Like there's a company out of Seattle called Read AI.
And, you know, there's a lot of transcription services out there, but Read actually allows you to look at someone's sentiment in a meeting and understand how engaged they are, come away from that meeting with a clear set of action items. That is something that top down inserting into someone's workflow is going to be challenging, but enabling folks to use that bottom up and then rolling it out further once

there's critical mass, we think is interesting. So I think it's really, you got to go from both ends.
Yeah. Yeah.
So if I were to summarize what you're saying, top down, it's really creating the rules and the regulations for how are we utilizing agentic AI in workflows? How are we improving efficiency? What's the like overarching strategy for how this is getting into our organization, but bottom up, it's like the, we want to create a playground. We want to enable people to experiment and explore, but playgrounds have fences around them.
Right. And so how can we empower people to try new things so that we can then propose even greater solutions? Because when we think of, okay, we want to bring AI in to help our sales team be more efficient.
Okay, great. But there are like infinite tools that are popping up.
Like there is a new tool every day to help sales teams be more efficient. It is wild.
And the case is for every single team. The level of new tech hitting the market right now is obscene and it is very difficult to parse through it.
And so it needs to be an all hands on deck thing. But I think that what I see in my clients as a consultant is some leaders see this and some leaders are maybe a little intimidated or they're like, I just can't think of it right now.
I'm going to like, I don't know. We don't, we don't have to do it right now.
Your team is going to start using AI, whether you like it or not. And so are you going to put a fence around it or is it just going to be like the wild west and someone's going to get hurt? And I think that we do still need to create some level of guidance around this is what we want you to be experimenting with.
And this is where it's not something that we want you to do. Um, cause I have seen, I've spoken to leaders for like my, you know, an executive is like using a email from chat GPT that wasn't edited and sending that out.
And it's like, well, that's not a good idea. And, and you wouldn't think that you would have to say something, but maybe you do, you don't know.
I like, I think it's, we do have to create some boundaries. You're spot on.
And it's interesting. Like if we go back to our data that says two-thirds of internet users have at least some exposure to Gen.AI and half of them are using it weekly.
People are using it in their jobs, whether you like it or not. Yes.
Right? Your point is spot on. We see a lot of companies utilizing agentic tool sets that are provided from their cloud providers, right? Like if you have a relationship with Google Cloud or AWS or Azure, like using what they have can sometimes be a path of least resistance, specifically from a data security perspective.
So we do see that. In order to bring about reinventing an entire workflow, though, I really still do believe you need a top-down push to say, we are going to use this tool to do this type of thing.
And the experimentation at the end user level should be more around individual productivity and sort of day-to-day tasks, right? I really appreciate that distinction. I fully agree.
I mean, it's something like, also on the topic of using the tools you already have, we need to mandate that sometimes. It's like, let's find out what does Salesforce have now that we didn't know six months ago when we did a shift in the system.
Let's dedicate time and resources to figuring that out. How can we be using the tools that we already have better? And then if we need to, or there are other tools out there that we trust that are going to bring us long-term gains that we should look at implementing.
Those types of decisions cannot be made bottom-up. That has to come top-down because it is very expensive, both from a tooling and time perspective.
Yeah. No, that's right.
And look, I think it's a great time to be a buyer here, right? Because if you look at Agent Force, for instance, or Adobe Firefly, the tools that most companies are already using are getting smarter. They have more AI functionality.
And there's functionality that you can use today, right? And that's not to say that some of the incredible innovation happening from privately funded companies is not worth looking at. It absolutely is.
And it's moving even faster. But for those that are moving slower, maybe outside of tech, this stuff's showing up on their doorstep, whether they like it or not.
And it would behoove them to dig in. If you haven't already, look at all the tools you're already paying for and see what new features exist because it is changing constantly.
And I have this conversation with clients all the time. I'm like, have you looked into the tool you're already invested in to see if that is going to solve the problem? But I get it.
It's confusing because we're getting bombarded with all these new shiny things. And I think we also just have to be really careful about how much time we are investing in AI implementation because it could become a whole big rabbit hole.
Totally. Totally.
Could not agree more. Something else that we spoke about previously that I wanted to bring up is the approach that leaders take in implementing agentic AI into workflows.
And there is kind of the softer approach and the more firm approach. I'd love for you to share your perspective on that.
We kind of touched on it when we talk about top-down, bottom-up. Because I candidly think that you could almost map soft versus firm to top-down, bottom-up in a way.
But what we're seeing at most of our clients, candidly, is that the firmer approach is being used when work to be done is being reinvented wholesale. So for instance, in CRM, if you want all of your data to flow into CRM in a new way, right, using agentic tools, that's not something that you can allow people to pick up and choose themselves, right? Everyone needs to get on board with using Agent Force to do that if you're using Salesforce as an example.
But I do think that for individual productivity and some of the things that don't necessarily impact an entire workflow, I think meetings and how you digest what happened in a meeting and what you do next, that might not be something that you should force on someone, right? Make the tools available and let them decide. And the snowball effect is going to happen when people see that that's very useful and benefits them and their ability to impact on their jobs.
But sometimes we need to mandate the use of it. And I think that this is the case with most technology transformations is people will be resistant to change.
And with AI, I feel like there's even more resistance to change because there is this question of, is AI going to take my job? And of course, we don't want to steamroll people into, you know, having to do something that they really, really don't want to do, but we need to really focus on guiding them into this. It's not just like, this is a fundamental step change in how people are working and we need to train them and we need to make sure that they're using things in the way that they need to be used in order for us to get the job done.
Yeah, yeah, that's right. And look, there's also, you know, there's also, this sort of brings up a whole nother new thread of AI tools out there, which is looking at employee productivity, right? I mean, there's players like Seam that are doing ingestion of data from all of your productivity tools to tell you like, who's more productive than someone else, what time of day.
And, and frankly, allowing being using that to enable coaching to understand how you can do your job more effectively. But I think there's two certainties this year, right? One is that companies are tired of Gen AI hype, they want to see real business value, right? And that's determined in revenue growth or cost.
Like simple as that. That's going to drive the optionality of playing ball if you're an employee.
It's going to kind of change that dynamic, right? Because this year, I think there's just going to be a little bit more force. But at the same time, I think that it also creates an opportunity.
Because if you are a forward-looking person on this or a forward-looking employee, and you can become masters of a lot of these AI tools, it puts you in a great position going forward, right? Yes. It's table stakes.
It is. We need to understand it.
And we need to tie it back to revenue. It always comes back to that.
And, but now it is, I believe easier than ever to do that based on the data that we're saying. We just have to think about how can we show the value? Yeah.
Yeah. Yeah.
Totally. I could not agree more.
It's going to be exciting year to watch this play out for sure. It's going to move quickly in a different way this year.
No doubt about that. Totally.
I have my popcorn and also my keyboard. I'm like in it, in it and watching.
My last question for you is what is one piece of advice every customer experience leader should hear? This is going to sound a little silly, but it's be customer obsessed. Yes, it is.
I totally agree. Common sense.
But realistically, like, and I think I do this personally, like step back and look at everything that I'm working on it and really, and really dissect. Is there something we are doing that would come across as not being obsessed with our clients? And it's, it's simple, but it's something that I think oftentimes gets forgotten in the hustle and bustle of our quarterly OKRs that we're all chasing.
Yeah, completely. Because we're often chasing our OKRs, not our customers' OKRs.
And if we put ourselves in our customers' shoes, literally, if you can, go and sit in their seat, walk in their shoes, live their life, then we can really understand what do they care about and bring that back and make that our North Star as well. 100% agree.
I would be shocked if those that are not customer success and evaluate what they do on a daily basis through that lens don't succeed far better

than those that don't. Kevin, it's been so wonderful having you on the show.
Thank you for

sharing all this wisdom with us. It has been so insightful and I cannot wait to see what the

future, what the near future holds. Totally.
It's at our doorstep. It's exciting times.

Thank you very much.