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 AI strategy with Agentforce. Start achieving your ambitious goals. Visit salesforce.com/agentforce Mission.org is a media studio producing content alongside world-class clients. Learn more at mission.org
Chapter 1: Who is Michael Maoz?
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?
Chapter 2: What are the opportunities and risks of AI in customer experience?
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're either 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 sales.
Chapter 3: How does clean data enhance AI success?
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 failure. 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 go on from there, but we'll start there.
OK, that's terrific. And you yourself, when you started, you said AI. And then you qualified it with generative AI. And 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. And 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 learn, 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 and it's really not that case. The reality is that it's part of the evolution and we started with predictive and that's gonna be important.
It's gonna remain important because a lot of the things I need to do, just look up what time is this arrived? That is just predict. I know what that is. It's a simple case. And then this new thing generative, it creates. And when there's good things and bad about that, we're going to get to 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, one, we now take the generative We're going to move into all sorts of possibilities around what we'll call agentic.
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Chapter 4: What are common pitfalls in AI implementations?
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 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 data. 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 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. And 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. And it's like, 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. So we have a client in the UK in the health care 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 some very basic things. It turns out there are only three or four things that people ask repeatedly on this site. So I said, 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.
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Chapter 5: How can companies build trust with customers?
So let's point AgentForce at that. And 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. But the 80% went away, the 20% stayed. And of course it's getting better.
It's an iterative process. And now they're, you know, they're, 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, the,
Then you can go a little further and say, we have six projects going, eight projects 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 built these campaigns and said, 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. So the beautiful thing that we did building something like Data Cloud was build, if you want to think about it, as a data ingestion engine and then put all of those data governors, all of those filters on there to say, which data has it been checked? Is it up 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?
Mm-hmm.
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Chapter 6: Why is customer service about more than just transactions?
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 it, 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 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. 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. So now you have the goodwill and then you are not just throwing out one of a hundred 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 of 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, or the frequency which they write, 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?
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Chapter 7: What role does employee trust play in customer experience?
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? It's just like you'll see people put anything and everything on TikTok or Insta or whatever, but don't.
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? Well, 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 at 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 it 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 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.
Oh, I love that you brought up trust. It's one of my favorite topics because in customer experience, it's this like, 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.
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Chapter 8: How can organizations leverage storytelling in customer interactions?
Yeah, very much so. And people flip-flopped around 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'm 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. It's filled with this mundane, repetitive stuff. So far from saying, oh, 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 want to be, you raise your hand. I don't want to work at a call center. Like that didn't happen.
But today, we think that's going to change because 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 this 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 employees. 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%. Mm-hmm.
I think that this is one of the best use cases for AI that is not getting enough airtime, 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.
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