Experts of Experience - This One Thing Will Generate 400% More Customer Data

Episode Date: February 19, 2025

You’re either building trust or breaking it — which one are you doing?Customer experience isn’t just about service. It’s about trust. And most companies are getting it wrong.In this episode, Lauren Wood sits down with Michael Maoz, Senior VP of Innovation Strategy at Salesforce, to reveal what really drives customer loyalty — and why most brands sabotage themselves without even realizing it.The conversation dives into when technology enhances customer relationships versus when it erodes trust, the dangers of relying on flawed data, and why customers are willing to share 400% more information with brands they truly trust. With real-world examples and practical takeaways, this episode is a must-listen for leaders who want to transform customer experience from a transaction into a lasting relationship.From brand-killing interactions to why AI won’t save you if your foundation is broken, this episode is a wake-up call for leaders who want to build authentic, lasting customer relationships. Key Moments: 00:00 Who is Michael Maoz, SVP of Innovation Strategies at Salesforce?01:52 AI in Action: Opportunities and Risks05:09 The Role of Clean Data in AI Success06:38 Practical AI Implementations and Pitfalls19:07 Building Trust with AI34:05 Simplifying Communication with Stakeholders34:28 IQ vs EQ in Business Decisions35:13 AI in B2B and B2C Contexts37:14 Automating Customer Support in Banking38:56 Emotional and Complex Interactions41:07 Experimentation and Adoption of AI45:08 Customer Journey Hacks and Channel Preferences48:58 Voice-Driven Future and AI Integration50:08 Impressive Customer Service Experience53:34 Advice for Customer Experience Leaders   –Are your teams facing growing demands? Join CX leaders transforming their strategies 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

Number one rule, do not be creepy. And that's a real thing because you can know something about a person or a business, but should you know that and should you let them know you know that? Sephora doesn't really know you. They have to make everything as operationally efficient as possible or they can't make a profit. But what do they do? Then they say, but how can I make this personal?

If I trust you, I will pour out 400% more information, accurate data about myself than if I don't trust you, just like in a relationship. You either are building the brand or you're killing the brand. I don't care if it's the person doing billing, but if every person in the business, that's your customer success. If one person is rowing out of sync,

you're just going nowhere. Service is not a department only. Customer service is us. It is what we live for. Hello, everyone, and welcome back to Experts of Experience. I'm your host, Lauren Wood. Today, I am joined by Michael Maoz, who is the Senior Vice President of Innovation Strategy at Salesforce,

where he's focused on developing innovative strategies, as the name describes, that enhance customer experiences and, of course, drive business growth. So prior to joining Salesforce, Michael had a pivotal role in founding Gartner's CRM practice and spent two decades there focused on helping organizations around the globe improve their customer support and service. And Michael has extensive experience in cutting edge AI implementation that we're going to dive into today

and really understand how organizations can build their teams and their processes and their data effectively in order to really drive customer experiences of the future forward. Michael, so wonderful to have you on the show. Likewise, thanks. So today, I am so excited to talk about our favorite topic, AI and customer experience.

Because pretty much every organization, I think it's safe to say, is looking to benefit from generative AI in their business. And there's a paradox to this, which is there are great efficiencies to be had, but there is a risk of impacting the customer experience negatively if we don't do it correctly. And so I'm curious to know your opinions and thoughts around what are some of the common misconceptions around generative AI and how we're using it

in the customer experience space. And then we'll get, we'll go on from there, but we'll start there. Okay, that's terrific. And you yourself, when you started AI, and then you qualified it with generative AI, and that's the thing. I was covering AI for probably 10 or 12 years, You hear yourself and you started just at AI, and then you qualified it with generative AI. That's the thing.

I was covering AI for probably 10 or 12 years, and if I mentioned it, eyes would glaze over, no one cared. Because it was predictive, and predictive AI was just, what are you doing? It's inferential reasoning on a data set, so if this, then that, likely is the next thing. It's great for predictive maintenance, and it's awesome for field service scheduling,

and all sorts of other things. But then we got to this thing and more young people know when ChatGPT was launched than know when Kennedy was assassinated. And my generation, that's what you learned, right? That was the pivotal moment. And now it's ChatGPT was released because this predictive thing was very cool. But now when you add a generative component, that's even cooler and we'll get to that. But the main mistake is to see that that is the end state, that generative AI, we have arrived. It's really not that case.

The reality is that it's part of the evolution and we started with predictive and that's going to be important, it's going to remain important. Because a lot of the things I need to do, just look up what time has this arrived. That is just predict. I know what that is. It's a simple case.

Then this new thing, generative, it creates. There's good things and bad about that. We're going to get to that because it can also hallucinate. It generates, but then I don't worry about hallucinations because human beings also, part of being a creative being is that you generate. You generate new ideas. The exciting thing is when we now take the generative, we're going to move into all possibilities

around what we'll call agentic. Agentic is really neat because those are not just large language models which I've been talking about from people like OpenAI and Cloud and all the others. But action models allow you to, just as the name implies, I can now look up your order and see where it's stuck and see where the inventory is.

And I can then complete the form on your behalf. I can do all sorts of wonderful things with that. So think about this as a continuum. And all these are going to be focused on, where do I substitute labor, which today is being wasted on all these mundane things which gum up our day. That's what's really going to be cool. I'd say that the reality is that the real winners are going to be those people who get

that that's where it's going. They're interlocked with maybe three other things. So you got AI in all of its three guises. But then it's molded together with clean data. And we're going to talk a lot about that. You got clean data. And I emphasize that all the time because people say data is the new oil. I don't like that idea. That's like an extractive thing which has an end.

But data is always being produced. And if you can get just like water, clean water, clean data, you can do amazing things. And then if you have the CRM processes that are hooked in, and then you have the channels to talk to your customer on, if you can put those four things together, AI, data, a CRM process and system, and put it out in the channels.

You've just closed that whole loop and that's what's going to really give you what we've been looking for for 20 some years, that single view of the customer, that one-to-one personalization. Mm-hmm. Ah, it's so exciting. I mean, even a few years ago as I was leading a customer service team we're like using four tools in order to get a picture of the customer, it felt crazy, but there wasn't really another option. And all of a sudden, it feels like we can have it all in one place. And it's really, really cool the way that Salesforce has been approaching it with the agents and really this agentic AI. You've been leaning hard into agentic AI and there's so much to come that I'm so, so, so excited about.

And we're going to get to all the good use cases and all this good stuff. But I'm curious to know, you get to see a lot of different implementations and we don't need to name names. But where have you seen organizations maybe do it wrong? What are some of the common pitfalls that you're seeing people fall into as they're implementing AI that we need to be aware of and move away from? Sure. This is one of those YouTube algorithms

where you're going to take it dark first. You're like, let's just get it through. Yeah. Yeah. Yeah Let's get this. I'm going to bring you to the light. But okay, let's take you first to darkness. And I think that's a good thing. We'll start with what can go wrong. And there's really too many of them to start. But I'm going to take with one who was several months ago and they set up this really great thing. Conversational AI is in the form of a bot.

It looked really compelling, had a great UI, people jumped into using it. But suddenly, something went wrong. What went wrong was this was a siloed application. It wasn't connected to anything else. If you had an issue or you ran out of runway with the bot, what happened? You picked up the phone or went into a chat session or you sent an email, whatever you did.

And they're like, who are you? I don't even know who you are. This is like the great corporate amnesia, which everyone just loathes. And the second thing that we did was it didn't really understand the issues really because the data was not accurate. So it had a bunch of data. It worked on that data. And this happened to be tax information. And so people were putting in their taxes and asking all sorts of stuff to this conversational bot. And it was reading stuff from all different sources

and giving you answers, because that's what it does when it doesn't know what to do. A year ago, if you asked Chat Cheap P.T. what are bigger cow eggs or chicken eggs, it would say cow eggs. But come to think of it, that's a bovine and it doesn't lay eggs. Well, thank you very much for that insight. Yay. But that's all improving very quickly. What you saw was happening is it was pulling

in information actually from places it wasn't allowed to. It was going out to the web. It was looking at social media. It was looking at Pinterest. It was looking at Facebook. It was looking at Google searches. So the problem is, and It was looking at Facebook. It was looking at Google searches. So the problem is, and we're gonna get out of it, that's why I talk about clean data.

So it was siloed. It was a great algorithm, but it didn't connect with the right information. And it also violated privacy and a whole bunch of rules that would get you shut down in Europe for GDPR or in California or any place where you really have to know the provenance of the data. I keep hearing this statement garbage in garbage out when it comes to data and AI and I really like what you're saying about like clean water. We need to have clean resources in this form data to make sure that whatever

system we're putting in place is working correctly. It's like the foundational element of how we can use AI and then make great experiences from it. I kind of want to go there right now, even though I was planning on talking about it a little bit later, but you brought it up a couple of times, and I think we just need to talk about how can companies, one, get the right data and know that they have the right data because there's so much data now.

It's hard to know exactly what do we do with all this information and funnel it into the right places so that we can create the right outputs from our AI? Yeah. And you're asking a question that could be phrased a little bit differently in how do I do something right away with the data I have? So I don't have to do that big, just like people spent years building a big snowflake repository of information or Databricks or whatever. Well, we'll get around to fixing our customer process when we get that project done. We don't want you to do that because the tools are there now with things like Agent Force and other tools,

but to do something now. So the first thing to say is, why don't we take information that you can trust? You probably have a knowledge base around simple things. We have a client in the UK in the healthcare industry, and they said, we really want to jumpstart our generative AI program. Let's just take all the emails that we have answered over the last year, and analyze them, ask us a very basic things. It turns out there are only three or four things

that people ask repeatedly on this site. So that's a great thing. We know this data is clean. We know that the search doesn't go outside of this canonical database that we created, or knowledge base we created. So let's point Agent Force at that. What happened immediately out of

the box was that 80% of the emails disappeared. Now not 100% because if agent force detected that it wasn't really sure that the answer it was giving was accurate enough to send directly to the customer, they put it to the agent. The 80% went away, the 20% stayed and of course it's getting better. It's an iterative process. And now they're improving. But having that human in the loop so that the technology can detect, I'm not really that confident with this.

I'm going to send it back. And we'll talk later about our Atlas reasoning engine, which is also doing an amazing thing on helping automate that process as well. But in parallel, then you can go a little further and say, we have six projects going, eight products going, but every one of them points back to different places in different systems.

It's in Jira. The data's in Confluence. The data's in SharePoint. The data's in a Salesforce CRM. Some of it's in SAP or wherever it might be. Let's, just as we do with marketing, where we build these campaigns and say, OK, we point, the data is in a Salesforce CRM, some of it's an SAP or wherever it might be. Let's just as we do with marketing, where we build these campaigns and say, okay, we want data to come from here, here and here.

That's what we're doing over here. We're saying this information, which by the way, I can't see because I'm not a supervisor, but you can see because you are a supervisor. Or I can read this, but the customer can't read that because it's a price sheet and I can use that information. The beautiful thing that we did building something like Data Cloud was build, if you want to think about it, is that data ingestion engine,

and then put all of those governors, all of those filters on there to say, which data has it been checked? Is it up-to-date? Is it allowed to date? Is it allowed to be read by this person for this task for right now? And then we go forward with the process.

So what I'm pointing out is we are telling our customers we're doing a great job with data. Don't wait until you've got all your data right. You can do probably 20 or 30 things right now with the data you have. And then in parallel, think about that strategic program you're trying to build around generative AI and agentive AI. So it's thinking through, of all the information

that we have in all these different places, what is ready to be used versus what do we need to go through a project of cleaning and sorting? That's essentially kind of what organizations need to be thinking about. For example, we have all these emails that have already been sent that the customer has already

received. We can use that information as a good starting point for how we're going to respond to future customers, because we're just going to be saying essentially the same things in most cases. And then we have information from different places. What I find organizations struggle with, some of them that I work with as a consultant myself,

is even thinking that different data sources could be used. I think sometimes we're still kind of looking at things in the way that we've always done it before, where then there's opportunities in different data sources and information that maybe we hadn't thought to tap into before. Is that something that you run into? And maybe you have some examples of pieces of information that aren't maybe obvious to use but have been beneficial?

Well, sure. One of the things that we're finding is its imagination. that aren't maybe obvious to use but have been beneficial? Well, sure. One of the things we're finding is it's imagination. This is an age of storytelling. And the reason we get up on stage and we tell so many stories, you'll see if you go to Dreamforce or to a world tour, you're going to hear story after story after story.

And the reason we're doing we're trying to ignite the imagination. Because it's the art of the possible. And it's just like with language, until you're exposed to an object and then have a word for it, it's hard for you to even perceive what that is because you have no word for it in your language. So we're giving people the language, if you will, to say, hey, and let's look at customer service or let's look at marketing or by the way, did you know?

Think about you're doing service inside of marketing, or marketing inside of service. So I'm going to give an example of one of our customers, who their job basically is to sell alarm systems. Very simple. They also have cameras, they have alarm systems, and so it takes a while just to get a technician slotted to go out to that job. You have to look who's got the right look, who's got the right tools, who's got the right parts,

what's their schedule look like, are they on vacation, how many truck rolls do they have? So we do all that with our AI, our general predictive AI. But what we're doing right now is when they come in on the telephone, as they do right now, to ask that question, we're already running in the background a marketing analysis of that customer. So we have the data about what do they own.

We know perhaps what size business they have. We see their install base. When they call and say, hey, I want to go from hardwired cameras to digital cameras, you look and say, hey, by the way, did you know with those cameras, we also have the new alarms throughout your house that are digitized and by the way, we can also record them.

By the way, we can also do the analysis for you. Now, the reason we have the permission, the customer has permission to do that is because instead of spending three, three-and-a-half, four minutes on that call, just to get the technician to go to the place, we've automated all that. Now, you have the goodwill. Then you are not just throwing out one of 100 offers, we've automated all that. So now you have the goodwill. And then you are not just throwing out

one of 100 offers. You've pinpointed it. You have made that offer really personalized. So what's the conversation here? It's about how you can do marketing right inside of a support call. And that we're seeing in all sorts B2B and B2C. But it all comes back to you have to be able to imagine it.

So we're now saying to people and putting in predictive AI and generative AI, you know you can also look at sentiment while you're doing this. So now you can start to analyze the words people are using. Are those words or the frequency which they write, the cadence, their tone, and you can see are they happy customer, are they content customer, are they anxious customer? And if they're A, B, C, the cadence, their tone. And you can see, are they a happy customer,

are they a content customer, are they an anxious customer? And if they're A, B, C, or D, you can now treat them this way or that way. And you also take a learning, what task were they performing? What process was going on during that that made them upset? And maybe there's some flaw in that task

or in that process that we need to look at. So we bring that list of things in. And again, we have this flywheel of, like Toyota says, better, better, never best. And people would never have imagined they could do that. You're getting my wheels turning now just as we're talking about this. And I think that that's one of the beauties and also one of the difficulties of this new AI generation that we are just on the cusp of is really being able to imagine what is possible. And that's something that we like to do on this show is to help share

those examples of what people are doing so that we can start thinking about it. But I want to talk a little bit about how can we, well actually how do you in particular help to guide your customers to start thinking about one, what is possible, but then also what boundaries do we need to put in place? Because there's always this balance of, okay, well we could do that,

but just because we can doesn't mean we should. An AI agent your customers actually enjoy talking to? Salesforce has you covered. Meet Agent Force Service Agent, the AI agent that can resolve cases in conversational language anytime on any channel. To learn more, visit salesforce.com agentforce. And how do you approach that? Yeah, there are a few things in there. The first one pops into my head is don't be creepy. Number one rule. Don't be creepy.

Do not be creepy. And that's a real thing because you can know something about a person or a business, but should you know that and should you let them know you know that? It's just like you'll see people put anything and everything on TikTok or Insta or whatever. But goodness gracious, if you actually put that in one of their emails, oh, look at that picture of you passed out on the beach.

What? Of course I can know that, it's publicly available information, I could pull that into your profile. You're creeping up. And there's a line. So this whole thing about involving the customer. And we have to talk about that a lot

because you want all this stuff. We're in a moment when employees are afraid. You're talking all about jobs being lost or task substitution or labor replacement. That sounds like my job is going away. And the second thing, customers, consumers are thinking, hmm, you're just after my data. And there's a big world between you're after my data and,

goodness, take my data. And I give that example of Insta. You'll post anything there, but if someone used inappropriately, suddenly that's bad. And we're finding that businesses who earn the trust, and we're gonna talk more about trust, I'm sure you're gonna ask questions about trust. If I trust you, and this is large studies

been done by people like Boston Consulting Group, if I trust you, I will pour out 400% more information, accurate data about myself than if I don't trust you. Just like in a relationship. If I trust you, I'm all known and all forgiving. Forgiven. But if I don't trust you, my lips are sealed. And I think that's one of the things. So those two things about getting trust and make sure you don't go over that border. You're always working in tandem with the customer to improve it. I think those are the two big things I'm seeing. Mm-hmm. Oh, I love that you brought up trust. It's one of my favorite topics because in

customer experience, it's like the gold that we can't always see or measure. Because if we have trust, like you said, the customer is much more willing to share information that we can then use to help improve their experience. They're more likely to come back, to be retained, to tell their friends. When we have a trusting relationship, everything runs smoother and faster

and more efficiently and just better. And I think AI is one of these areas. There's definitely a generational component to it. Some of the older generations are immediately going to be less trusting where the younger generations are like, here, have it. Everyone knows it, so whatever. But there is an important role that a company plays,

especially when we think about customer data, about how do we build that trust through these interactions. And I think that a generative AI in customer service environments especially, there is a lot to gain and a lot to lose. Because if it doesn't go well, if it's creepy, we're really impacting that relationship and that trust

that we have with that customer. But if it's serving them and using their information in a way that actually makes their experience better, we can actually build trust. So what do you think about that? And where do you see companies doing it really well versus making mistakes? Yeah. Well, what is the mantra at Salesforce? What's number one?

Trust. And what we're really trying to do, all of us are trying to do the same thing, is if I trust you, I will spend a lot more time. I'll spend getting that into the DNA of everyone. The first thing is trust. We sometimes look at above the iceberg and below the iceberg when they're talking about generative AI.

The first thing you worry about is does it work? But I look at relationships and I say what's below the surface? So above the surface is it's a transactional relationship. I want your money, you want my service. But it's like Peter Drucker said, the customer never buys what you sell. And you sit back and say, what? And you think about it, I don't buy what you sell. You're selling sneakers. I'm buying an experience. The reason I go with on versus this one versus this one,

and I could buy a Adidas, I could be a Subcontinental, I could buy a New Balance, I could buy, they're all great. Or automobiles or anything else I could buy. There's something about the relationship. There's something intrinsic that I say, I would rather have this one rather that one. It's no longer commodity to me. It's personal.

If you can get into that, get into that, go beyond the transaction of selling this for this. Instead, be able to like Steve Jobs, the iPhone, it drew you in. It drew you in and it did it because they thought you're someone who deals fairly with me and I can trust you. And the perception is that you're an ethical business. Why do we have an office of ethics and humane use? Because we know, especially as you get towards GenZed and the Millennials and the Alphas, who knows what they're going to think. But we know, especially as you get towards Gen Z and the millennials and the alphas, who knows what they're going to think. But we do know that they want you to be ethical. They do know you want

to be fair. They sense you're not fair. They're out. If they think you're ethical, if they think you care about your employees, care about sustainability, even though it seems like, oh, that's going to drift away, it's a desirable place to work. Guess what? There's a sense of adventure. Like I've been at work for, I don't know how many years and I wake up every morning here and I, wow, I am so lucky.

I am surrounded by people with great thinking, great brains trying to do great things. I want to come to work and that's amazing. And I also feel like we're committed to the community. If something's going down, we're out there and places like Wegmans or places like Lego or places, you name them. That's how they, Muji in Japan, I think I can think of hundreds of them, people know that.

And so in the perception of that is that service is not a department only. Customer service is us. It is what we live for. That stuff, and you get all those things, it becomes a talent magnet. Why do we have 90 applicants for every one role? That's crazy. We want to have people as influencers,

and we want to have them sharing information, sharing a point of view, giving us more insight into our products and how we can improve. The best companies that I just mentioned, all those people, that's what they've got going for them. I mean, think about WEG maniacs, right? WEG maniacs, have you ever heard that term? I haven't actually, but I get it completely.

I called them so many times. Because when I moved to the East Coast, I was like, wow, people love this grocery store. We had a store that opened in Brooklyn and there are only like three people in Brooklyn and three times more people than live in that place showed up outside in the pouring rain at 630 a.m. to have the opportunity to be at Wegmans. So that kind of thing, building that culture and there are people in the United States and they go, wow, how did they do that? But IKEA, you know how they did it.

They had their way. And Patagonia, they had their way. And I mentioned Muji there. People said no brands, no logos, just great stuff. And of course, Salesforce. So all these people, they've got that thing. And yes, they're doing their AI thing. And yes, they're doing their innovation thing.

But they're thinking more broadly about what sews that customer into the fabric of our being. It's an inside out job. It has to start on the inside. You can't build customer trust and have the benefits of customer trust if your employees don't deeply trust the organization that they're working for and then are committed to making that organization successful because it's something that fulfills them. It's not just a job.

And that's what I'm hearing you say. And as you describe these companies, it's really something that it starts on the inside. Yeah, very much so. And people have flip-flopped around in the value of the employee, the need for the employee. And especially now as we're starting to say, hey, what is the goal of this stuff, this AI stuff,

this generative AI, this agentic AI? Well, and part of it, it's to lift that cognitive load off of me. I know that when young people come to get hired at Salesforce or anybody else, they come and they say, well, wow, these are the systems you use? Oh my God, it's terrible. Not so much at Salesforce because we have pretty good systems. But they go, it's a spreadsheet, it's a field, it's a table, it's a form. That's how I was spending all of my day. Why are you doing this to me? And

we look at 40 to 60% of anyone's time at work is filled with this mundane, repetitive stuff. So far from thing thing where we're trying to get rid of employees. Many jobs we can't even fill. Field technicians, we can't even find them. Nurse practitioners can't even find them. There are so many jobs that you can't even fill, but if we could take away,

especially since the pandemic for health workers, they're burned out. So we're saying, let's relieve that burden from you. Or call center agents, who the heck ever grew up and in sixth grade when they ask you what you wanna be, you raise your hand, I don't wanna work at a call center. Like that didn't happen. But today, we think that's gonna change

because the job, we'll get to that in a bit, but all these jobs were trying to lift off the boring, put in the exciting, and for the job, we'll get to that in a bit, but all these jobs we're trying to lift off the boring, put in the exciting, and for the customer it's the other side. It's like, why do you have to do all the stuff? It's repetitive, it's boring, it's useless, it's a dead end. Why don't we change all that so that you feel that this company really thinks about me,

they invest in me. So both things are happening. We're lifting up the employee. So your top 10% of all performers, we know how they are. But imagine if we could take 80% more of them and lift them up so that they can work just like that 10%. I think that this is one of the best use cases for AI that is not getting enough air time, in my opinion, is really how we can use AI to improve the lives of our employees because that then gets transferred to the customer. And I think I'm sure most people can agree with me here in that one of the things you dislike

about your job the most is when you are stuck in the weeds between tools trying to find information and copy and paste things. I remember once I almost quit a job because one of my jobs as a senior manager in a company was to copy and paste 200 lines of expenses from one spreadsheet into another spreadsheet because it was critical information that could not be seen by anyone else. So I had to copy and paste it line by line. It was one of the most infuriating things I've ever done in my life. To that point, they wonder why they're at field service technicians. My furnace, I live in New England and it's been very cold. It was about 10 degrees outside. My furnace went,

of course that's when it went, didn't go in the summer. Of course, of course. And, you know, the furnace doesn't have any IoT, doesn't transmit signal, doesn't say what's going on. We'll get to all that stuff in a bit. But the technician finally gets there, and the gentleman who works on it is a great person, doesn't have the part, comes back,

and says, I finally get it fixed. And he says, are we talking about it? Because I like to do ride-alongs with technicians. And he said, you know what I've got to do now? I'm going to take this whole sheet which he hand wrote, and now I've got to go back to the depot, turn it into another person, and she is now going to enter all that information.

This is the billing, this is the invoice, this is the inventory, this is the time card. Is it like, it's 45 minutes for me, it's 30 minutes for her, and guess how many times we have errors? We're like, there's 45 minutes for me, it's 30 minutes for her, and guess how many times we have errors? And we're like, there's an app for that. We can turn all of that into predictive and agentic AI. Yeah.

I think that technology, we've kind of gotten ourselves into a hole. The more tech we've built, the better life has become, but also the worse it has become. And I kind of feel like AI is here to, generative AI is here to like save the day and relieve us of this like mess that we've created for ourselves. So it's such an important thing for organizations to think about as we implement AI is not just

how do we grow the business with this, but also how do we empower our people to do their jobs in a way that they can not only be better at, but also more efficient and just enjoy their work more. I agree with you. And one of the things our customers are struggling with is what are the things that I have the algorithm do and what do I have the algorithm do and what do I have my employee do? And that's the big one.

And basically I'm like, look, you gotta write these two rules right on your wall, your digital wall, your physical wall, tattooed on your arm if you have to. But the humans handle these high value engagements. That's what you want to do for it. These are moments that are strategic, moments that are emotionally nuanced, emotionally complex.

My house just burned down. My spouse just died. I want to change the benefits. I don't want to talk to a bot about these things. All those things that you absolutely are going to need a human, then they're going to be aided by the AI. But then the things that we talked about, if it's, I can measure this.

I can literally do a task measurement and say, there's task substitution. This is repetitive, this is predictable, this is transactional, this is data-driven, this is workflow intensive. All these things are binary and the net on the one side, I have the AI optimizing the human. And let's make that list.

And I can give examples of both if you're interested. And then on this side, it's these are the things that are going to be the AI is going to do. And when there's a need, then they're going to flow to a human. But we have to get that right and find out what are the indicators of each? How can organizations set those boundaries? What is the process of getting there?

Because I think it's not always easy. And I'm curious to know if you have any examples of methods or good results that you can talk about. Absolutely. Absolutely. And sometimes it's when you stop freaking over-complicating all this. Why don't you talk to your employees?

Why don't you talk to your partners? Why don't you talk to your customers? Why don't you talk to your different segments of customer? Like if you have emerging market versus small business versus large business versus a consumer business, guess what? You've got to speak to all four. And you'll find that there are all these indicators that would say, you know, this is just about IQ. And IQ goes to AI. But then you say, no, this is really EQ. This is really the difference between the

laws of divorce and what do you think, should I get divorced? It's the difference between the laws of bankruptcy and what do you think, should I go bankrupt? One is, well, why don't you take the couch and we'll talk about this. The other is, here are all the rules and AI is fantastic, better than any human will ever be at many, many things. So is it IQ or is it EQ? Because we are so good at body language. I think dogs

are good. We are 10 times better. We have these social cues and we can get things right. So basically, I'll give you an example. Take a B2B company and you can throw it any kind you want. Let's say chipmakers are in the news right now. So let's say a chipmaker. And I'm making chipmaker chips for the auto industry and that's cool. So I'm a steady to contact per company I sell to Toyota and all good. So what happens? I have my AI power chat bot, my FAQs,

they can tell me the voltage requirement and do order tracking. Let's make the list ourselves. We know predictive maintenance, the warranty, the RMA. Dude, put all of this in AI, energetic agentic AI. But where is the human? Suddenly I've got an emergency. Turns out this chip has a defect and I want a human stat because I am freaking out. Is there a replacement part? I don't want to talk to the bot about that. When will I get it? Do I have to send an engineer or I want a custom

chip designed? What do you think? How much is it going to cost? What are the risks? What's the timing? Is it a good? That's what I'm doing. I'm paying you a fortune of money. It's high touch. And that's a classic B2B. So in B2B, you're trying to give a highly personalized

relationship. And then you operationalize anything you can. But what about B2C? Let's talk about that. And there, it's the exact opposite. Sephora doesn't really know you. They have to make everything as operationally efficient as possible or they can't make a profit. But what do they do? Then they say, but how can I make this personal? So I know, hey this is my host guy, his skin's getting older. Who would know? He uses a moisturizer. But

not only that, when it comes to the month when they've got a sale, in my email, I get a completely curated list as though they live with me. They know I buy three of these a year. This is the product, they've got a new one coming out. If I wanna buy this, I should buy three right now because they're 50% out, price is gonna go up. But we also have this new one, you didn't think about it,

but you need something for the night, you think about it the day. I feel like they know me. They've curated it, they've personalized it, but they've really operationalized the heck out of it. So, in banking, for example, we have a great banking customer, checking accounts, loans, wealth management, normal thing.

Who is their customer? It's not a small number of people like that chipmaker. It's every, it's Main Street. It's millions, it's tens of millions of people. So they have customer support and they have a formula where, okay, balances, inquiries, transaction history, routine account changes, fraud detection, small fraud, loans I want to put out, pre-fill all the forms, small disputes, all of that they are automating with Agent Force right now. They're also putting in an agentic layer so they can launch actions.

But, and what did we remove? That was searching the knowledge base. It can do it faster. Finds the answer to the question, it can post email, sent it out. It kept an eye on the customer sentiment. Are they happy or sad? It answered the questions about

the bill of delivery and the form and all that stuff, and it summarized the conversations. I just gave you six things which they completely removed from the day to day of every one of their people on the phone. But when do they step in? Large scale fraud. Your accounts has been compromised. I don't want to get a freaking message about that. Call me. Right? High stakes financial advice. I'm taking out a million dollar loan to open a bakery or negotiation or I have a crisis. So what were we talking about? What goes here and what goes here? Sit with your customers. Sit with your employees. Sit with your different employees. Figure this out. It is not rocket science. Mm-hmm. If we were to simplify this, if I take everything that you just shared and just simplify it

into what is the difference of when something should go to AI versus when something should be with a human, and what I'm hearing is that if there is an emotional component to this interaction, humans should be the ones to interact with the customer. If there's something that I might be afraid of or there's something that is not black and white, and I need to have a conversation about it, even if AI could have the conversation, it doesn't mean it should have the conversation.

If the fraud conversation or the fraud notice, I think is such a great example, because if that happens, I think, is such a great example. Because if that happens, I'm going to start freaking out and I need someone to talk to. An AI bot isn't going to cut it. No, it's not. And if it was a five-hour charge, you wouldn't freak out because you'd say, oh, you know what, they charged you that because you were late by a week. Let the bot figure it out and your bot will talk to my bot, which

is happening by the way. And instead I've got to call up and okay, we'll raise that charge. Why did you waste my time? You knew darn well. I'm a longstanding customer and you're going to erase it. So how about if my agent speaks to your agent and you negotiate this, you know, because you say, oh yeah, you're a good customer and you don't really do that every single month. It's a rarity. Oh yeah, I am a good customer.

Thank you very much. Canceled. And all I get is a message saying, your $5 charge was removed. So even in that case, it really, it's contextual. Does it rise to the occasion of this? And if not, and I just slipped in there, by the way, agents speaking to agents. We're going to talk about that a little bit about where things are coming.

Yeah. But there are two things we said. One is the emotional component, but the second one was the complexity. And I gave the example of the divorce and bankruptcy because there's symbolic analysis there. That was Robert Reich's term, symbolic analysis. It's like, hmm, it really depends what you're trying to achieve. Buy the house, rent the property,

stuff the money in the 401k, buy a treasury bill, take a gamble on this new nuclear energy fusion company. Let's talk about that. Let's talk about your long-term, your young this, your old this. Those are the kind where you say, let's pop to a human and have a conversation. What about the experimentation component of this?

Because I think as everyone is exploring new AI, I guess what's the mindset we should be approaching it with? Few things come to mind. One is, can we have a little fun here? And let's not think that everything has to be built here. And so we have companies that are light on IT and they totally get that. Like I don't have the IT staff anyway.

What do you got? Well, you're trying. You have to understand the buyer's mindset. So people who are over on one end of the continuum, they're super innovators and they're up for taking some risk because they see the big reward of jumping out in front. They're super innovators and they're up for taking some risk because they see the big reward of jumping out in front. They're a minority.

At Gartner, we call those Tai-Bas. I'm willing to absorb more risk than normal. I don't care about de-risking because I want the advantage to get ahead of you. And then you see pragmatists who start saying, hmm, I see what they're doing. I'm willing to take a little bit of risk. I don't really have a budget right now, but I can redirect some budget that I was going to put over here.

I'm going to try this out. And I'll even pay you some maybe a little bit of money for it. The type A is not going to even pay you money for it. Like, I'm taking risk. You're going to take some risk. And we're doing that with some of our big customers. You want to go big? Let's go big together.

We'll figure out the answer later. Then over here, it's like you put some money in, we put some money in, we make a little, you're going to get some value, and we have to define the value. We're not there where it's Main Street. That's a Jeff Moore term of the pragmatist, and goes through the tornado and out to Main Street,

and over here is the, because the Main Street needs to de-risk, and they're not loving this stuff right now. The pragmatist is, the pragmatic person is usually someone in IT whose line of business, like the services, they say, hey, everyone else is doing this. This is FOMO. Everyone else is trying this. They're doing these cool things with email or with chat or they're like, we have one of our great clients in banking in Brazil.

They're not doing almost all their interactions through our conversational AI inside of WhatsApp. Once you WhatsApp, you speak in your normal voice right now, you still have to type, but we're getting to voice very quickly. You tell it what you want in natural language, get rid of that stupid interactive voice response thing, press one, press two. They were already there.

They're inside of WhatsApp because that's the line of Japan, this is the rest of the world. We have iMessage, but you know that. WhatsApp. All my messages from foreign people are in WhatsApp. Exactly. Exactly. It's like, okay, all my European friends are in this chat.

All my Americans are here. So, suddenly they said, oh, we'll put that for the lower end of the market. The lower end of the market, we can't serve them with high touch. And guess what happened? All those low touch customers, lower end of the market are just jamming on that WhatsApp, loving that conversational bot. And when it has to escalate, because it's a little bit beyond what the bot can do, all the context flows through WhatsApp to the agent. And she said, hey, hi, Maria.

What can I do for you? I see you're trying to do X. I love it. It's fantastic, because it's a continuation of the dialogue. But guess what happened? Now their high-end customers saw it. And they go, why can't we have that? I want that. Because we're going to get high touch.

We don't care about your freaking high touch. This is something, we go back. This is a binary thing. I just want to cash this check. I want to pass this money. I want to find out the current loan conditions. I don't need a human. And so they're actually driving the bank to think more and more

about this stuff. So A, is have a little fun. Start with little use cases, and also start doing your homework. Why don't we think about this from the customer journey? And this is a very important part. And sorry, I'm going a little bit long on this one. No, you're like hitting on something

that is so important to me, so go for it. You can have all the time you want. How do I reach this person for this different thing, right? And then,, Oh, we have 45 channels. But should you really? Because they don't do that at Amazon and they don't do that here and they don't do that here. Why are you, Oh, okay. We need all these channels. That's fine. But how do customers want to reach you for this task? And it turns out this one, they want it on mobile messaging,

whichever one they choose. This one, they want to do it through your app or on your website, which is very information rich and it's a large form packet. That's two. They might want to do conversational through your IVR, but not through your IVR. They want to just pick up the phone and speak.

They say, hey, I'm trying to get a phishing license and I'm over 16 and under 60, what do I do? And it gets sent right out to me, right? That's all there is to it. That's how I'm rolling with you. It's through your app or your website. It's through your voice response thing that you're going to now evolve into voice-driven or it's going to be through one of these things. And of course, the other one is if you have Internet of Things, now the car is talking, now the bridge is talking, now you have machines as customers.

That's a cool and very neat thing where the machine itself, just like your phone is doing all its own updates and your apps are doing their own updates. Now your printer is ordering itself its own printer fluid, and your dialysis machine is getting the reactance and the scheduling when the technician's gonna come. That's the fourth case that many of us have when you're moving into this world

of the machine itself as a customer. But that's what I'm saying is figure it out for each one of those, where does your customer wanna start? So now I got the channel and now ABCD, which ones they should be no touch. So these actions right here, as we just find earlier, they should be no touch. The customer should be in an automated way.

If we have our information architecture in order, they can look it up and get the answer. This kind of brings us back to what we were initially talking about and just thinking of the possibility and getting outside of the box is first off, I just want to reiterate what you're saying is we need to ask our customer what they want because so often we're like, well, it's easier for us to send this type of message through this channel and it's easier for us to send that type of message through this other channel. But like, what does the customer want? And we really need to ground in what the customer's experience is throughout the journey in order for it to be a great experience that builds trust and has them wanting to come back. But I think you're pushing it a level further, which is, do they even need to talk to us about

this? Do we even have to have a conversation? Or is this something that we can just automate and take off their plate completely? And I think that that's this next frontier of what AI is opening up for us is oh we don't have to wait until they tell us their fridge is broken because we've built a system into the fridge where the fridge will tell us if it's broken and so Now life is easier and they trust us more because we're thinking about the customer's best interest all the way through. That's right. And we're doing that already. I think about your health. You're wearing all these devices that tell you about your heart rate or they're telling you about whatever

it might be. Your body itself is starting to stream information. So when you get your doctor for your checkup, they already know. And it's going to get only better and more advanced. All the devices are doing these things. As we build new ones, there's that whole world that's coming online. And the most incredible thing coming forward is it's all going to be voice driven.

Just like you say, Alexa, whatever. We're starting to see that the new world of applications, the application disappears. I think one of the most exciting things for so many people is this idea that the interface is your voice and the AI, and the workflow, and the accurate data in the CRM process, they're the one that fill in what we used to think about that field, or that spreadsheet, or that form.

They're doing it on your behalf and they're negotiating on your behalf. They're doing that exchange of value that you yourself stipulated and defined, and you can change it as you change, or it can actually change it for you as your needs, just like when you put money into a 529 for a child, as the child gets older, the risk factors change and they automatically move the investment. That's how it's going to be with your relationship with the business that you work with. It's exciting.

Well, I think that's a great place for us to wrap up the conversation. And Michael, we have two questions that we ask all of our guests. And the first is, I'd love to hear about an experience that you recently had with a brand that left you impressed. What was it? I was putting my expenses into our expense system, which I will go unnamed. So I go into my own system, lodge our expense system, and I see a charge I don't recognize. It happens.

I do my best. I'm looking through all my stuff. I cannot figure it out. So I call our credit card company. The credit card company looks at it and they can't find it. There's the date. There's the sum. Can't find it. You know, I want you to say that it was fraudulently or incorrectly,

and we'll just write it off. Like, that doesn't sound very ethical. Maybe I did. I think, okay, maybe it's from Amazon. I'll go onto Amazon site and I look for my orders on Amazon. At that date, can't find it. Something, I really feel just not right about saying it's a fraudulent charge.

I think maybe I can call them. I've never done this in my life, but I found that they actually have a chat feature. I went into the chat feature from my order system, would come in your orders, I go to chat and the one says, ''You know what? How about if I call you?'' I'm sure a second later my phone rings.

First of all, the chat launched in about 10 seconds. Next, my phone rings, and I see it's from Amazon. I'm like, what the hey? And it's this woman, Dot, and she says, explain this to me. It's like, well, here's the thing. And I explain the whole story. She goes, oh, yeah, yeah, yeah, yeah. You know what?

That's a third party. It's not Amazon. And oftentimes, the third parties take up the 30 days to submit the invoice. So let me go back 30 days. And she goes, oh, I found it. $17.20. It's this, it's this. Let me send an invoice to your text message right now.

That's good for you. Good for me. Is there anything else? I wish I could give you like a digital hug. You are crazy good. This is amazing. But that's how life should be. So my own company couldn't do it.

My expenses couldn't do it. My credit card company couldn't do it. And self-service on Amazon couldn't do it. But they had a fallback for that one in a thousand. They had a fallback and guess what? She knew what I was looking at. She knew where I was on their site. She had all the different forms, right?

She had my phone. She had my text. She had my records. And that's what you want. You want to be all known. You want a single point of knowledge where you can have things resolved. Stat and your full satisfaction.

That's the exciting future that Salesforce is delivering. I would love to sit with Dot and see what her dashboard looks like. No, a long time ago it was just training. She sees that, she's like, oh, and those, that's where the humans are so good. She's, ah, I could engineer that with AI, but you know how much it would cost me? And this is like one in a thousand. Let me impress the guy that we have a human touch and she gets the invoice to my phone.

And I just uploaded that into our expense system. And I was done. Amazing. Do you think she knew who you are? No, who knows? They might know. I know that this guy is gonna be on a customer experience podcast and he's gonna say my name.

Let's have a dot here next time. Yeah, exactly. He's like, I know that this guy is going to be on a customer experience podcast, and he's going to say my name. Let's have Dot here next time. Yeah, exactly. Dot, if you're listening, please reach out. We'd love to talk to you. We love you. So my last question for you, Michael, is what is one piece of advice that every customer

experience leader should hear? Yeah, oh my gosh. I'm going to tell you this. This is something you're not going to hear. If you're working for a company where your executive leader is not down with this initiative, find a new job, get out of Dodge. You're probably a really talented person. They're holding you back.

You're trying to soar with an eagle and you're walking with turkeys. Get out. Because this is a profit first, customer second, and you want to be at a place like Salesforce, which is customer first. I can get the customer by treating the employees right. You build great technology and you're off to the races. You know, I mentioned that, you know, like, you know, Julie Sweet from Accenture or Colleen Wegman, I mentioned Wegman. They're all about treat our employees right, give them the right tools, give them some autonomy. And I did this from the top pushing down excellence. It's when Michael Bloomberg ran New York City. He's like, no, they're not, they're not citizens. They are customers because realtors work here. They come in from out of town.

Financial people come in here, tourists come in here, they're all our customers and we have to, and he drove it from the top until it was done and it's never done, because you're better, better, but never best. So that's my thing is like always be inspiring, think about it. This woman, she started the whole customer service

for Land's End. And she was amazing because her boss knew nothing about customer service and he came to her and he said, well, what do you want me to do? I really need just to have a customer service support thing. And we're in Land's End, for goodness sake, the end of the world. What's the first thing you as a customer experience person

need to do? And she said, open a daycare center. Like, no, I'm talking about you need an IVR, ACD do you need? And she's like, no, I need a daycare center. If you can give me that, I can get the best people in town. Like that's the mindset you want to have. What is going to drive me to be the best? What's going to make

me attract the best people? And then what's going to make me attract the best customer? Every part of you, every part of you is building the brand. You either building the brand or you're killing the brand. I don't care if it's the person doing billing or the person like me on the phone with you right now in this great thing. By the way, thank you. Such an awesome opportunity. But if every person in the business, that's your customer success. Everyone is aligned. It's like in a skull team, when you're in crew,

if one person is rowing out of sync, you're not going faster, straighter. You're just going nowhere. So we have to kind of have everyone aligned and then you're just bound for success Yeah, and Leaders need to understand this. Well, they have to drive. They have to drive They've got it. They've got it own it and that's the best companies you always see that from the top like make it so, you know I'm you're the experts you and sales marketing service billing field support. I trust all of you. You're the experts, you in sales, marketing, service, billing, field support.

I trust all of you. You're great people. But there's no, I don't want to play. It's like you're either on the team and you're a great member of the team or you're off the team and there's nothing else to do. Go somewhere else. We need greatness. And leaders say, we need greatness.

And there is no alternative. IT can't hide, the business can't hide, there's no excuses, you've got to measure it, report it up. Is it making the customer more loyal? Are they attracting other customers? Are we growing? Are we lowering our costs? It's very simple stuff in the end of the day.

It's not that complicated. Cue the standing ovation. That was exactly what I wanted to hear. Thank you so much, Michael. It's such, such, such important advice, both for leaders to own that and also for employees. If you don't feel like you are supported in your role and really a part of a great customer experience, you also have a choice to go somewhere else.

We have a choice. And what I love about millennials and Gen Z is that they will just go where they feel the passion because it's not just a job for them. The best employees we hire, it's so amazing because they're here for more than just the doll. They don't care primarily about the paycheck. They're here for the whole package. Completely.

Well, thank you so much for coming on the show today. It's been such a pleasure to have you. I cannot wait for this episode to be out in the world. And I hope you have a beautiful day. Thank you very much.