The Customer Success Playbook · 2025-07-03 · 14 min
Key moments - from our scoring
Substance score
57 / 100
Five dimensions, 20 points each
The conversation centers on AI Experience (AIX) design - Jake McKee's framework for building relationships between humans and AI systems rather than just optimizing individual tools. Rather than viewing AI as a point solution to replace specific processes (like chatbots deflecting support calls), McKee argues companies must think of AI as a business transformation similar to digital transformation 10-15 years ago. This requires rethinking workflows, data ethics, responsible innovation, and internal operating models. The discussion emphasizes that trust in AI systems mirrors human trust: it's built through micro-moments of reliability - when someone says they'll take out the trash and they do, or when an IT team understands your actual problem before rolling out a process change. McKee illustrates this with examples like a 13-screen internal process that took 2-3 hours and lacked a save button until screen 13, destroying user trust. For customer-facing AI, the focus shifts to humanistic design: AI should communicate when it's hallucinating or uncertain, allow time for users to warm up to an experience, and feel like a collaborative conversation rather than a robotic interrogation. The hosts emphasize that internal tool relationships matter as much as external customer relationships when building empathy into AI systems.
Point-solution approaches treat AI as a tool to replace a specific process (like ChatGPT writing Facebook ad copy), while transformation thinking rethinks entire workflows and organizational structures - similar to digital transformation 10-15 years ago - requiring changes to how teams share information, work with partners, and handle data ethics and responsible innovation.
Trust happens in micro-moments over time - if you say you'll do something, you do it; if an AI system says it's uncertain or hallucinating, it tells you honestly rather than later apologizing for making things up. McKee emphasizes showing users that you understood their actual problem, not just your own, before deploying a tool.
This approach misses the point of AI transformation; it's often just replacing human interaction with a scripted experience without rethinking the underlying process. Instead, companies should ask whether the change genuinely adds value for customers or if it's just another frustrating mandated tool that undermines trust in the organization.
Rather than starting with robotic questions immediately, AI interviews should warm candidates with conversational context, use a genuinely robotic voice that's honest about what they're interacting with (rather than a fake-human voice), and ask warmup questions to help candidates settle before serious evaluation begins.
Initial feedback may be colored by what users think product teams want to hear, then novelty masks real issues, but after two weeks actual experience emerges - that's when genuine product insights appear, making continuous testing with passionate customers more valuable than single-ask feedback.
Our reviewer’s read on each dimension, with quotes from the episode.
The episode offers several substantive ideas about AI experience design and relationship-building principles, particularly around trust in micro-moments and the importance of organizational transformation context. However, much of the content consists of lengthy tangents, throat-clearing, and reiterations of the same core themes across different examples, diluting the insight-per-minute ratio. The 13-screen process example and hallucination handling provide concrete thinking, but these are offset by circular elaborations.
trust happens in micro moments as much as big moments
we're in this stage where we're really thinking about how does AI transform the way we do business
The guest presents a relationship-design framing for AI that is somewhat fresh compared to typical tool-optimization discourse, but the underlying argument - that context and human relationships matter - is not particularly novel or contrarian. The comparison to digital transformation from 15-20 years ago is illustrative but not original thinking. The micro-moments concept and community-driven product development are established methodologies, not first-principles breakthroughs.
designing that relationship with the AI system and the humans that are using it
AI business transformation, right? Very much like we saw, you know what I. 10, 15, 20 years ago, the digital transformation
Jake McKee positions himself as a practitioner working on AI Experience Design and community-driven product development, with references to real corporate examples (the 13-screen internal tool, AI interview experiences). However, the transcript provides no details about his company, scale of impact, or specific credentials that would establish him as a senior operator who has led major AI transformations. He reads more as a consultant/thought leader on the topic than a practitioner who has shipped at scale.
the Community Guy
my website is jake mckee.com
The episode suffers from a lack of concrete data, metrics, or named companies. The 13-screen internal process is a vivid anecdote but lacks specifics (no company name, timeline, or outcome). References to ChatGPT hallucinations and AI interview tools are generic. No numbers, dollar figures, success metrics, or detailed case studies ground the abstract principles being discussed. The advice remains at the framework level rather than actionable specifics.
literally 13th screens. It wasn't until the 13th screen that they had a save button
I asked Chad CPT for something and it, and it gave me an unsourced thing
The host (Roman) asks reasonable opening questions and makes effort to connect concepts back to customer success and internal relationships, but does not push back, challenge assumptions, or demand specificity when Jake's responses become circular or abstract. Kevin makes a brief validation comment but largely goes silent. Neither host probes into the actual execution details, metrics, or harder truths about AI implementation failures. The conversation is collaborative and friendly but lacks sharp questioning or productive tension.
keep me honest here, Jake, external internal relationships are all the same
I love Jake, your examples, because I think sometimes you hear relationship
Computed from the transcript - who did the talking, and the words that came up most.
Send us Fan Mail 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.
Transcribed and scored by The B2B Podcast Index.
Customer success. 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? 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?
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? 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. 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?
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 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 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 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. 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.
Other episodes covering the same guests and topics, from across The B2B Podcast Index.