Customer Success Playbook Podcast S3 E66 - Jake McKee - AI Experience Design

The final episode of this transformative series tackles the ultimate challenge: scaling AI experiences without sacrificing empathy. Jake McKee reveals why most companies approach AI transformation backwards—focusing on tools instead of relationships, replacement instead of enhancement. This customer success playbook episode demonstrates how successful AI transformation mirrors the digital transformation of the past decade, requiring fundamental changes to business processes, not just technology adoption. McKee's framework for maintaining authentic human connections while scaling AI across enterprise environments provides practical guardrails for companies navigating the complex balance between efficiency and empathy. From addressing AI hallucinations transparently to designing trust through micro-moments, this conversation offers a roadmap for AI implementations that enhance rather than diminish human relationships.

Detailed Analysis

McKee's perspective on AI transformation represents a sophisticated understanding of organizational change management applied to emerging technology. His comparison to digital transformation provides crucial context—just as companies had to fundamentally rethink business processes when moving from analog to digital, AI transformation requires reimagining workflows, decision-making processes, and human-machine collaboration models.

The conversation reveals critical insights about trust-building in AI systems, emphasizing that trust develops through consistent micro-moments rather than singular grand gestures. This mirrors human relationship dynamics and provides a practical framework for designing AI experiences that build confidence over time. McKee's examples of internal process failures—particularly the 13-screen system requiring hours of work before allowing saves—illustrate how poor experience design destroys trust regardless of underlying functionality.

Perhaps most valuable is McKee's nuanced approach to AI transparency and hallucination management. Rather than attempting to eliminate AI limitations, he advocates for honest communication about system capabilities and uncertainties. This customer success playbook approach recognizes that users can develop healthy relationships with imperfect AI systems when expectations are properly set and limitations are communicated clearly.

The discussion also addresses the critical challenge of scaling empathetic AI across large organizations. McKee's emphasis on relationship design over feature development provides a sustainable framework for maintaining human-centric experiences even as AI implementations grow in scope and complexity. His insights about contextual AI behavior—understanding when users need speed versus thoughtful interaction—offer practical guidance for enterprise AI strategy.

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Transcript

Kevin Metzger: 0:05

Customer success.

Roman Trebon: 0:10

Welcome back everyone to the Customer Success Playbook podcast. I'm your host, Roman Reon. Here with me as always is my co-host Kevin Metzker. Kevin, we've made it to Friday the end of the week. I know you're excited for AI Friday as we wrap up our three part series with Jake McKee, the Community Guy. You ready to go, Kev?

Kevin Metzger: 0:30

Yeah, let's get going. It's AI Friday. Jake's walked us through trust in AI and community driven product builds. Today we're gonna talk about how do we break bake empathy and authenticity into AI as we scale. So Jake, when companies roll AI pilots into full production. As, or when they plan to anyway, what guardrails do we need to think about to keep the experience empathetic instead of robotic? And how do we ensure that the AI applications are gonna work, work at scale? In other words, how are they gonna make sure that they stay within those guardrails? What are the things that companies should be thinking about?

Jake McKee: 1:09

Well, like we talked about on Monday, this design practice that I've been working on called A IX AI AI Experience design is really focused around that sort of, you know, working principle. That, that the tools are not really the end result, the end results, the relationship we're building up with, um, our creative and critical partner, the, the AI system. Right. But I think, you know, to answer your question, I would actually step back a second and. Think about what's happening right now, right? Instead of thinking about just when we build a tool, how do we make the tool good? I think it's really important, um, that what we're seeing happen right now is, is a, is the AI business transformation, right? Very much like we saw, you know what I. 10, 15, 20 years ago, the digital transformation where we did, we saw bookstores turn into e-commerce, right? And, and all what that required. That moving away from, uh, you know, it driven, uh, tech teams inside companies and moving into how do we really trans transition the way that our teams work, that we share information, that we work with partners, the way we work with customers through much, much more digital means than analog means. So I think we're, we're in this stage where we're really thinking about how does AI transform the way we do business? And we have to start there, right? Because you can't build trust and empathy if you're not really starting from what are we really doing here? And we shouldn't be anyway, just trying to create a chat bot to offload basic sales questions that we think if we put a fairly scripted user experience journey in place, that that. Customers will get on the chat bot, they'll ask the questions, and then they won't call us and we'll have call deflection and it'll be saving so much money, right? Because we're looking at AI transformation as a way to really rethink how we do basic business processes, how we automate a activities inside the the company. And that means thinking about internal processes and thinking about things like the, the data ethics and, and responsible innovation, uh, the overall operating model that we're using around, uh. How we create with ai, what are those guardrails? What are our upskilling and training and requirements for approvals and that sort of thing. All this stuff that goes into making AI more successful for the business as part of transformation. That's where we start. Right? And if we start there, I think what, what naturally starts to happen is we stop thinking about an AI tool as a point solution to replace a very specific singular process. That. Uh, and we see this a lot right now where, you know, marketing teams are saying, geez, we love, we write a lot of Facebook ad copy and this would be great to just outsource that all to JGBT and we'll be done. I think it changes the workflows, I think it changes the ability for us to create, I. Uh, in that example, you know, various AB testing, uh, copy that we can do a whole lot more experimentation, which is awesome, right? And, and, but that's a change to the workflow as much as it is anything. And so back to this idea of how do we make it more humanistic? It is like we talked about on Monday, designing that relationship with the AI system and the humans that are using it. That is happening within this general, uh, AI transformation. So if we're trying to replace internal processes or add an ability to, to connect associated, uh, data sources and make better, more interesting conclusions for our, uh, data analysis teams, these are things that are really meant to do to, to, to have outcomes that, that will help us to transfer, translate, transform. That's the word I was looking for. Transform the business. What do we then do with that? Right? So, you know, trust is, uh, uh, one of the A IX principles is, is trust happens in micro moments as much as big moments, right? That how we build trust with, with other humans, right? Back to this relationship building construct. How we build trust happens. Sure. That we're not committing fraud, that not being violent, but of course those big, huge things. But also that if I say I'm gonna take out the trash, I take out the trash. If I say, Hey, I, I keep forgetting to lock the front door. I'll work on it, that I actually work on it. Whatever it is, you know, really making sure that some of those little moments are continually built up over time. That when I hear from the IT team that this process is going, going to improve something I'm doing internally to track my hours or whatever it might be. That it really does, and that they're coming to me with not just something that they've done in a vacuum like it teams often do, and then roll it out and say, you gotta use it, and we're all like, this is terrible. My favorite example of that being the 13th screen internal process for a company that I worked with, literally 13th screens. It wasn't until the 13th screen. It took like two to three hours to do all this work in this, but it wasn't until the 13th screen that they had a save button. If your browser crashed on on screen four. You were starting over, right? Well, of course that got feedback pretty quickly, but that development team had already moved on. Gotta stop what they were doing, get back and re refresh their brains on what was going on. Trust happens in a bunch of different ways over time. That is all focused on. Building that relationship and that connection. There's faith from the user that somebody was thinking about me, somebody was trying to do something specific. They obviously understood my, my problem, not just, uh, their problem. Right Back to everybody goes home. Happy mantra from from Wednesday. So I think that's a, a, a, it's a very long-winded starting point, but that's the starting point is really thinking about this relationship building and, and how, as you've transformed your business, are you really transforming all the parts of the business that go into that experience? You really understand the experience, you're able to add value, not just replace it with some, yet another change. I think all of us that work in corporate America are overwhelmed by change, constant change, constant reorgs, constant restructures, constant new bosses, constant, everything can, that can whack our trust as well. So you know, that's a huge part of how we design the systems. Less and, and, and yeah, there was ways in the system you can design trust. But you know, I always like to talk about this from the very beginning of when trust starts and that that is telling me, Hey, I've got a new tool for you, but I understand your problem well enough that I know you're gonna be excited. Not just frustrated that it's yet another tool just because somebody got a new contract over in it. Right?

Roman Trebon: 8:17

Well, I, I love Jake, your examples, because I think sometimes you hear relationship and, and you know, we're the Customer Success Playbook podcast, so a lot of it's customer focused, right? Like in terms of the relationship between the, the organization and the customer and the journey mapping and all that. But you're so many times forget ai, any new tool internally, there's still relationships there too, right? Like, uh, like you gave the example, like the IT department says, Hey, we've done this and go use it. And it's like, whoa, whoa. Has anyone understood like how, how this impacts me, how I use it and Right. But as relationships, it, it's the same thing, right? Like, and keep me honest here, Jake, external internal relationships are all the same. I mean, that those relationships are just as important. Mm-hmm.

Jake McKee: 8:58

Agreed. And, you know, back to this, the, to the, to the front end, the experience piece. So. I'm sure that that, um, some of the listeners, when Kevin, when you asked your question and they were like, okay, cool. Tell me all about the things I need to do to make a good AI tool. I think we know a lot of those already. We're learning a whole lot more about those, but they're, they're really, they're still in this vein of relationship, right? That you're making things quick when I need it to be quick. You're making things slower. When I could use a moment to slow down. If you're not, and we hear about this one all the time, if you're gonna hallucinate, tell me, or at least be clear about the nomenclature for what's happening for a project I'm working on right now. I did this earlier this morning where I asked Chad CPT for something and it, and it gave me an unsourced thing, and I had to ask, is this. Based on anything. Sometimes I do and it's, it is based on something they just didn't tell me. Right. And other times it's not based on anything. It made it up, but it's able to tell me and being able to bring some of that up earlier, you know, telling your wife to be home late is, is one thing, but not showing up on time and then just getting home late and telling her, oops, I didn't tell you, is a whole different story. Right. There's a lot of those little humanistic behaviors. I think we can start to pull into that. But, but again, I, I just, I so desperately don't wanna forget that. It's the context in which we have these tools that's as important as the tool itself. Mm-hmm. You know, I could have a great, uh, AI interviewing function, uh, that collects, you know, if you're doing something with a candidate, you have an AI tool that's interviewing them as the first clearinghouse of, of people. Okay, fine. No problem. If a, I believe that that's actually gonna be paid attention to, it's not gonna get turned into an automated transcript that then goes through an AI filter and no human ever sees it. And so what's the point of me doing this? Really? I. Issue one. Issue two is. Help me warm up to be my best self. Don't just start asking me questions and now I've got a, in a robotically human voice instead of a robot or a human voice. Can't really tell who I'm talking to, and it feels uncomfortable. It takes me a minute to settle into the experience. Gimme a few minutes to warm up. Yes, it's those humanistic traits of I'm gonna have good conversation. What does that look like in real life?

Kevin Metzger: 10:59

It's funny, I, I was doing a interview. I had an AI interview me recently on something and it conflated. Some of the numbers I gave it and got it wrong. I tried to correct it, still got it wrong and I was like, all, you've got it good enough. You get to a point where from a relationship standpoint, it's like, okay, I'm talking to an ai, you're not going to understand it. Yeah. I'm not gonna explain it 15 times because you think you understand it a certain way. It's never really understanding. Right. It. Only a predicting tool, so don't know what garbage bin that went to, but, well, and one of the reasons

Jake McKee: 11:35

why, you know, when we talked on, on Wednesday about the community driven product development work that I do, where you bring in passionate customers from, from your community base into the product of design. Cycle itself. One of the values of that process is that you're not just asking people once for a thing, but you're seeing over time how they experience and learn and grow from the thing. So if I put a, you know, a new product in front of somebody, the feedback that they'll give me upfront, maybe a little colored by what they want. To say to me based on what they think I want to hear as a development team, right? As a product manager. And then that kind of fades away, but then they get excited about using it'cause it's new and they're being invited to this experience. But then two weeks later they may actually get to the point of, okay, this is the real experience. But that took a minute, right? That took a, a second to, to make that connection. And I think that that's, you know, thinking about stuff like that with, with AI experiences as well, where. The robotic human voice instead of the robot or the human voice. Right? Either I need a, a very humanistic voice to get into this dynamic. And then what we're prompting the, the interview questions, how we're going about, do we submit them in advance and let the AI read it to us, and then extrapolate from there? Or is it creating its own paths, right? And really understanding, uh, as we develop tools like that. Processes, how does this fit into the process? How does a person feel using this? How does the system come off to them from an emotional standpoint? You know, when they're nervous and doing a job interview with an ai and it doesn't, you know, if it started off saying, I'm the robot, I'm here to pre-filter a human will absolutely look at this because I can't be trusted as my own. Robot experience, you might kinda laugh for a second, then it asks a few warmup questions and you're like, oh, this is kind of funny. A, a robot's asking me how my day goes. That's a very different experience than a fake human trying to pretend like it's asking its own questions. Even though you may have fed it those questions in the beginning or it's, it's responding just to create conversation with no particular point. Right. Um, these are process questions as much as they're technology questions. I think

Roman Trebon: 13:40

come, I think gentlemen to the end of our three part series. Jake, awesome stuff all week. I appreciate all your, all your insights and, and for joining the show. Can you give us, uh, tell our audience where they can find you at? Very

Jake McKee: 13:52

simple. So my name's Jake McKee. My website is jake mckee.com. Jake mckee.com/ix for, for more information on the, the AI experience, design work and, and practice that I'm doing. But, uh, yeah, that's it for me, jake mckee.com. Awesome.

Roman Trebon: 14:06

Easy enough to remember. Audience. We will have it in our show notes as well, so you can find it there as well. If you love these episodes, please subscribe. Rate'em. Share'em with your team. It helps us bring actionable insights to a broader audience. Kev, we're back next week with more strategies for our audience's, customer success playbooks. I'm excited for what we have lined up over the next couple months. Until next time, Kevin, keep on plan.