Customer Success Playbook S3 E36 - Ken Sandy - AI Will Revolutionize the Skills PM's Need

It’s AI Friday on the Customer Success Playbook! In this forward-looking finale of our three-part series with Ken Sandy, we explore how artificial intelligence is reshaping product leadership. From discovery and prototyping to ethical decision-making and team collaboration, AI is not just changing the tools—it's changing the very skills product managers need. Whether you’re a product leader or part of a cross-functional team, this conversation offers a roadmap for navigating the AI evolution without losing your human touch.

Detailed Description: In this must-listen conclusion to our three-part series, Roman Trebon and Kevin Metzger sit down once again with Ken Sandy, author of The Influential Product Manager, to dissect how AI is revolutionizing the way product managers work.

Ken kicks things off with a big-picture perspective: AI's impact will be long-term and transformative, but not immediate or magic. Drawing from past technological revolutions, he explains the familiar hype cycle—from inflated expectations to eventual disruption—and positions AI right in the middle of it.

But the real gold is in the practical insights. Ken dives into how AI will affect key aspects of product management:

• Discovery: Use AI to mine customer support data, user behavior, and feedback at scale, unlocking deeper, faster insights.
• Prototyping: Rapidly build and iterate concepts using AI-driven tools, allowing for early validation (and quick abandonment of bad ideas).
• Experimentation: Run more robust, scalable tests that bring clarity to customer behavior and optimize solutions in-market.

Ken also delivers a reality check: AI is powerful, but it doesn’t replace collaboration. The partnership between product, design, engineering, and CX is more essential than ever. He warns against operating in silos or outsourcing ethical judgment to machines. AI may hallucinate; your team still needs to lead.

Plus, the group tackles the tricky topic of technical debt, the future of documentation, and why empathy remains a product leader’s superpower. This isn’t just a conversation about AI—it’s a compelling call to reimagine how we solve problems together.

If you want to lead with AI rather than be led by it, this episode is your launchpad.

Now you can interact with us directly by leaving a voice message at https://www.speakpipe.com/CustomerSuccessPlaybook

Keywords:

• Artificial Intelligence
• Product Management
• Customer Success Playbook
• Strategic Innovation
• Discovery
• Prototyping
• Experimentation
• Cross-functional Collaboration
• Technical Debt
• Empathy in Leadership

Check out https://funnelstory.ai/ for more details about Funnelstory. You can also check out our full video review of the product on YouTube at https://youtu.be/4jChYZBVz2Y.

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Transcript

Roman Trebon: 0:05

Customer success. Welcome back to the Customer Success Playbook podcast. I'm your host, Roman Trevon, here with my co-host Kevin Metzker. Happy Friday, Kev. We're wrapping up our awesome three part series with Ken Sandy. You ready to dig in the uh, AI Friday?

Kevin Metzger: 0:27

AI Friday. Let's go. So over the past week, we've explored how product managers can build influence and juggle competing demands. Today we'll dive into how AI is reshaping product leadership and what that means for strategic decision making. I. Ken, from your perspective, how does AI factor into the product managers toolkit these days? And specifically, how can data-driven analysis and predictive insights help deepen customer understanding?

Ken Sandy: 0:56

Well, let me start by saying that AI will revolutionize the skills profile required about product managers, the way we work, our process tools, how we collaborate, uh, and ultimately, it'll need to be embedded in each of the products. Now, that said, uh, with every new disruptive. Technology we do tend to greatly overestimate the immediate impact, and then promises aren't met. Execution isn't as fast or as harder than we thought. We kind of reaches a little like trough of disillusionment. And, uh, and then even dismiss it, and then it sort of turns around and then we greatly underestimate the long-term impact that it's actually gonna have. As gradually the true killer applications emerge, and then like just an unstoppable force, the disruption is powerful, steady, and all consuming. So. When we look at ai, I, I like to think about it o on the longer term and, and sort of being thoughtful about how it's gonna impact us in the short term in ways to change our, change our world, while not at all, uh, underestimating the longer term. And if you look at past innovation, it's easy to dismiss a lot of the. Revolutions that we've had, dial up email browsers, early search engines, smartphones, the cloud and all. All that sounds very, very quaint now, but at the time they were a big deal at the start and so confusing and just everyone was scrambling and then to some level they disappointed and then. Then they came back and totally disrupted. Uh, so there's, and no, no one even questions their existence now, so it, it can take 10 to 15 years to run its course. So I'm super excited about ai, but I'm also realistic about how it might, uh, might impact us. So. Where will this impact? I'll maybe talk about the short term first. Um, you know, I'm gonna skip over, given you've asked the question around the, the, the more the data side. Uh, it's clearly gonna have time saving benefits, automating routine tasks, generating, uh, documentation and reports and summarizing things. Uh, I look forward to the day that it can, it can automatically write my weekly summary email that I have to out, that will be great. Although I know they'll be on the other side of that. AI to summarize my summary, so maybe have some AI engine talk to. But, uh, I look forward to all of that, just the time. But let's, let's, let's go beyond that.'cause those efficiencies and knowledge, you know, sharing echo stuff is, is pretty obvious. I'm actually most excited in, when you think about insights and customer insights, is the impact that it's gonna have in, in, uh, enabling us to do what we call the discovery phase. The exploration of the problems, uh, understanding the customer needs, and then, uh, moving into synthesizing those insights, developing solutions that are are possible, and then being able to prototype and test them to validate them. So, sort of sometimes we talk, talk about the double diamond of really understanding the problem and really understanding which solutions the right to solve. Now, that's done so poorly in most organizations today. Still. When you think about the quote unquote revolution of like agile, iterative methods, they sort of took a long time for us to get really good at delivery and still some, but we never really nailed, um, being able to do discovery efficiently, effectively. Uh, it's often undervalued, so I'm very excited about how it's going to help us synthesize insights just already. There's so much data we have out there. R user, behavior user. We're actually inundated with data of what users are really doing. It's all locked up in these systems. Customer support is a great example. All of the sentiment that's coming in all of the, all of the, all of the, the, the conversations that we're having with customers and we cannot really digest those. Being able to bring all of that insight that wouldn't otherwise be easily accessible or obvious at a much accelerated rate. So really being on top of what's changing, what's emerging, what are some of the, the challenges and just increasing that rate. And secondly, the confidence that we have around the decisions we're taking are really going to. Be based on understanding what's really going on. So I'm very excited Bahaa is gonna help us, particularly when it comes to the people on the front end who are interacting with customers every single day, that we just don't use enough in product management. Um, secondly, uh, as we understand more about the problems, um, what I've often found is solutions are hard to build. And so we often build one and we over design it trying to think of every single thing. And we go to market with something that has a lot of risk in it. And uh, what I'm excited about is AI's ability to really help us prototype DIY concepts quickly, uh, rapidly iterate with customers. Imagine the power of being able to take customer feedback and tell your. AI engine to change the solution for the next discussion. And that's already by the time like that ability to iterate super quickly and learn, I mean AI and its associated, uh, like, uh, tooling. Already can help us do databases, web interfaces, business logic, or without using our engineering resources or even design. Um, and I'm gonna caveat that in a second, but PMs will be able to quickly build, validate, and, uh, tweak and better still abandon bad ideas before they even make it into, uh, into the, uh, downstream. And then finally, when it is actually, when we are actually building something, I also believe it's gonna help us. Hypothesize and run experiments and build and bring data back to us faster than we could even imagine thinking of that ourselves. So being able to run a barrage of tests and test out hypotheses and being able to, uh, do those at scale, uh, much quicker than we have been able to do is going to mean that our. Iteration and optimization after a product is, is in market, is gonna improve. And all of that is about getting data insights and really reflecting the voice of the customer. And what's really been holding us back is the inefficiency, obnoxious mindsets that we are right all the time to just be able to say, let's actually use these tools to synthesize insights, generate and, and explore solutions, and then optimize the solutions we have. Roman, should I give you a chance to go because I have question. No, I know you're

Roman Trebon: 7:42

questions. I know you're rich in the get in, Kev, so go, go ahead.

Kevin Metzger: 9:11

I had, as you were talking, I kept writing down different thoughts that, that you were triggering in my head. Um, one of the things that I, as you got towards the end, you started kind of hitting on, but I'd like your perspective on things like technical debt. One of the things that seems to really drive us away from being able to innovate and grow is technical debt. I kind of feel like where we're headed right now, uh, with these tools, we should be able to eliminate a lot of the technical debt much more quickly. Are you starting to see that yet in

Ken Sandy: 9:47

practice? So, technical debt's an interesting one because, uh, what what it does help us do is potentially not accumulate debt that we don't. Shouldn't have, such as building features at scale that turn out to be not useful. And, and so it does help us avoid, say, building shortcuts and, and adding features and bloating out product out by trying to hedge and saying, oh, let's add the feature. Anyway, it's, it's help, it will help us and is helping us to sort of be a little bit more. Uh, a avoidant, but what I'm not sure, and I'm not actually sure how it would, is, is how it's going to help us avoid technical debt with the following exceptions. I think it will help us document I. Which is very important for bringing new people on board. I think it will help us, uh, track, uh, where things are not working, say, where scale is beginning to, uh, get stressed. Uh, it will help us, uh. Um, uh, maintain and EE adv advance code. Uh, but, uh, and I, I kind of wanted to make this caveat. I do not see AI in any way replacing the partnership that's needed between I. Product design, our, uh, customer facing partners and engineering is just gonna change the nature of the work design. You can't just say, you know, today, I no doubt AI's gonna do beautiful designs, but will it necessarily, uh, think through the complexity that, uh, that, uh. Uh, a good partner in interactive design. I have no doubt that visual design will be replicated easily by a, but when you get into our customers and really understanding what they, what they need, I can't see that, uh, that partnership going by the wayside. It'll make it more efficient because we'll be less relying on them maybe to simply design new interfaces, like goes with engineering. It may change that. It's not about the, uh, the engineers understanding the ins and outs of ai, but like many of our tools and services, it's about them getting the best out of it and figuring out how to use those tools to the most effective to be at most effective. And it still comes with a cost. So it is not cheap. Compute power storage. It's not cheap still. I mean, it's come down in cost to me immensely, but so has the complexity of our products and what our products are expected to do, and the sheer volume of transactions and data we're expected to, to deal with. So we still need, I. Good practices, uh, to, to guide us. And so having, um, strong architects, technical leads, who can think about our, the right solutions technically, and it's still very, very necessary. Yeah. I dunno if that answers your question, but it's definitely, definitely an interesting angle.

Kevin Metzger: 12:54

Yeah, no, it, it does and it's, it is an interesting angle. You know, one of the things that I think a, a point that I think you're kind of making and something that, um, I think is, is valuable is as you have various experts working together and using AI together we'll collectively be able to build tools, leveraging the tool, leveraging the AI tool. To get best results. The, you know, I, I, as a user have certain experience. I, I, I as a builder, a developer or a support person, have certain experiences that can play into it and bring in valuable information. You as a product manager, certain users' experiences, uh, uh, CX person has certain experiences that can bring when you start combining all of that together and using. AI as a tool to say, well, we've got these ideas and we wanna collaborate and build a, a product that delivers. Whatever our end goal is, we now have a more powerful way of getting there, uh, together. So it is, AI itself doesn't, doesn't actually, each of us individually can't build the best product. It's, it's, I I wanna double

Ken Sandy: 14:11

down on that. I really think it's important that we don't fall into the trap of, because we can now do so much with AI writing right down to the design and the tools and the, and the actual technology can kind of. Practically build itself. Uh, the danger will be to operate in a silo and to say, oh, I've done all this discovery and all these solutions, and I've come up with what we need to do next. I have high confidence and sort of throwing it over the fence. Old school. Right. Um, it's, it actually has to be a, a, a collaboration from the outset. And it's all about, you know, you're gonna be more efficient, but you're also gonna be more effective. And if you can think about. It is a tool, uh, that's gonna be cri uh, critical. The other, the other danger is even more, uh, operating kind of in a silo and really like relying on AI almost to a lazy level, is that, um, you, you, you, you're in danger of abdicating decision making responsibilities to AI and even getting into ethical, like questionable territory. So you have to remain creative and strategic. Figuring out the unmet needs and using AI and, and kind of desk checking it, like, is this aligned with. Our values as, as a company, what my customers would want me to do. Uh, is this actually really, uh, so that empathy and the ethics of the, of is this actually in their best interest? And having a team coming together and looking at through different angles will, will help you navigate that, uh, much more effectively. Uh, and in particular, like ai, we know it hallucinates, we know it gets it wrong sometimes. Uh, you do not wanna abdicate. Completely to it. You've gotta, you know what? We are humans, I think will continue to be good for some time. It'll, it'll get there. But we are good in ambiguity and embracing the ambiguity in seeing AI as a tool to help us build confidence in our direction while maintaining a healthy respect for the risks that are involved. Uh,'cause AI can easily mislead you.

Roman Trebon: 16:15

Yeah. Ken and Kevin, I, I love this conversation, Ken. Thank you so much for joining us all week. Ken, just so you know, this year, I think we told you this, we have these, like we're, we're doing shorter episodes. You have so much good stuff. I almost wanna blow that out the window and just do a whole long episode right now and get really dig, dig into this even more. But, but to our audience, you can find, uh, more, more about Ken. Go to influential pm.com. You can find his, he speaks, he consults. He does not have coffee recommendations in Melbourne. We, we learned, but he can do a whole bunch more. You can also find the book on Amazon. Uh, you have an audio book too, Ken, right? So I don't know, did you, did you, did you voice it? Is it in

Ken Sandy: 16:56

No, my publisher would not allow, allow me to do it. Something about some strong Australian accent thing off.

Roman Trebon: 17:03

Yeah, I gotta, you gotta get the new Australian setting if you want to hear Ken on narrated for you. So, no, Ken. Ken, real quick, where else can our audience find you?

Ken Sandy: 17:10

LinkedIn, uh, please feel free to connect. I, I love connecting to the, to, uh, anyone in the ecosystem. It might take any time to respond. So just LinkedIn slash in slash Ken Sandy, or one word, K-E-N-S-A-N-D-Y. Um, please, please do, uh, check out the book. Always open to feedback and, uh, and there are, you know, I'm a regular speaking and stuff like that, so please, uh, come up and say hi. If you, uh, are in the same place and time as me. Yeah, and you got a ton of

Roman Trebon: 17:41

resources on the website, so audience, go check out the website. There's resources on there. There's great stuff. So, so Ken, we really appreciate it To our audience, we, we, uh, appreciate you listening as always. Uh, you can also find Kevin and I on LinkedIn. You can find me at Roman reon you find Kevin at Kevin Metzker. Make sure to check out our customer Success Playbook page on LinkedIn. That's where you'll find out, uh, which guests we have upcoming. Kevin, we have a great lineup for, for March, uh, in April. So we're excited for that. Make sure you subscribe, you comment you like us. That helps us get, um, uh, more and more people listening. We really appreciate it, Kevin, as always. Keep on playing.