Making AI Work In Product Teams: Roundtable Insights

What happens when product leaders stop talking about AI theory and start sharing what’s actually working?

In this special Product Rebels episode, Vidya Dinamani and Heather Samarin unpack insights from four AI roundtables with CPOs, VPs, and product leaders. 

They explore AI slop, ROI challenges, role anxiety, rapid prototyping, and why judgment, customer insight, and product fundamentals matter more than ever in an AI-driven world. The future of product management may be changing, but its core purpose remains the same.

Transcript

SPEAKER_02 0:00

We had some really great discussions, roundtables, specifically focused around AI. The first was AI is making teams faster, but not necessarily smarter. This concept of AI sloth. A lot of leaders even brought that up as the term that they're using. We saw frustrated leaders developing things like I built my own persona to hand off to my team so that they could evaluate their documents against my perspective. Wow. Because they were just getting junk from their teams.

SPEAKER_00 0:33

Hey Product Rebels, I'm Vidya Denamani. And I'm Heather Samarin. And you're listening to the Product Rebels podcast. Today we're going to be doing something a little different. There's no guest, it's just us over the past couple of weeks. Woo! I know. We have been running these AI roundtables with senior product leaders. There have been CPOs, there's VPs, there's directors of product, people in the thick of it, all working on AI. We ran four sessions and we came out with a lot to talk about. Today we're going to be sharing what we heard, what surprised us, and what it means for product teams right now. Wanna kick it off, Heather?

SPEAKER_02 1:18

Yeah, shoot, we had some really great discussions. We had three roundtables specifically focused around AI. Two that were more leaning in on building AI products, and then another one that was really leaning in on utilizing AI to drive efficiency in the organization. And I think there were some really interesting themes that came out of it. The first was I think across all roundtables was really AI is making teams faster, but not necessarily smarter. And we'll talk a little bit more about this, but it's this concept of AI sloth. A lot of leaders even brought that up as the term that they're using. We saw some really frustrated leaders developing things like I built my own persona to hand off to my team so that they could evaluate their documents against my perspective. Wow. Because they were just getting junk from their teams. And so things like that really, there's a lot of manual effort going on to help in address the AI slot. Another one that I thought was super interesting is the inability to see the true ROI in the investments they were making, whether that was AI for the sake of building efficiency in the product development lifecycle or in developing AI products, connecting those dots to the outcomes, whether that be the outcomes for the business and the customer, or just the outcomes in sort of efficiency. Everyone could measure efficiency, which obviously is a pretty simple thing, right? We got 25 points done in this sprint versus our normal 50, whatever, right? So that's an improvement. But did that really drive ROI to the business? That was one big question that came up across all roundtables. The third, which was really interesting, I thought we were going to get a ton of different ideas or inspiration on where AI was making a huge difference in efficiency. And what we saw was prototyping was the number one sort of task in the PDLC that was across the board where people were standardizing on AI, but nowhere else. Everything was just still experimental, trying different things, failing at different things, and certainly not seeing huge benefits from AI other than in prototyping. They're using prototyping now, these AI prototypes as stage gates in their PDLC, which I thought was super interesting. And then I think we'll talk about this more today, which is just role anxiety, right? I think the definition of product management specifically is being blurred. I think everyone is now a builder. And so being able to figure out as a product leader, what is my role? What is my team's role now? How do I ensure that we're harnessing the power of AI, but still yielding the outcomes in customer delight and business growth from this acceleration and from this new technology, right? How do I do that as a leader? What's my role? And how do I define the definition of success of product management? So I think those were the really big themes.

SPEAKER_00 4:28

You know, it's so interesting. Just the consistency of those roundtables and what we've been hearing when we've been talking to product teams and product leaders. We're going to dive into all of these. I just want to give a quick recap. I did just do one round table because I wanted to focus on what's the future of product management now. It was really interesting because everyone came in with like, where are we going? What is our industry? And again, this is a leadership group. So interesting are the themes that came out here. The first, there's a real concern, I think, as more senior leaders. It's my way of saying we're old, Heather. I already knew that video. I think we kind of grew up where there was a lot of mentoring. We grew up coaching our product teams, and gosh, we're product coaches now because we take this so seriously. And we're used to folks coming in, and maybe product managers weren't straight out of school. They came out of MBA or they came out of a different part of the industry, but they had experience. And there was this idea that you're starting from scratch. And so, where is that talent pipeline going now? And across the board, across everyone in the roundtable, they said they are cutting intern programs completely. There's very few junior PMs coming in. There's an expectation that, you know, that is going to be done elsewhere. And so as we think about this, and I think we're all so proud of people that were on our teams, that are now in VP roles and chief product officer roles. Well, who's training the CPOs in 2030? So that was one of the things that we spent a lot of time talking about. As we were talking about this, another theme came up, which is we've always talked about product managers, is we're connected, we're the connective tissue. And this term was coined called the cardiovascular PM. We talked about PM now being the heartbeat, which I think is so interesting, right? Because it's like directing the flow, it's setting the heartbeat of the product. And I love it for a couple of reasons. One, because it shows that it's so much more pervasive in terms of like where you are, but also just in terms of heart, you are the heart of the product in more ways than one. So that was a fun term that came up spontaneously, which was really interesting. So the future of product management. So the heartbeat is absolutely necessary. And then we got it a little more on the downside. The first is around, gosh, AI is getting us to, and I think this is starting to cross into the themes that you heard, Heather. But AI is getting us to 80%. But what 80% is it? Because at 20%, it's not really the finish line. It's happening and it's missing at the beginning. It's missing in the foundations, it's missing in setting the right directions. So we're moving really fast. AI can get us to 80%, but don't think about this as the first 80%. And so that role of product management, the future of product management, understanding how to leverage that 20%, where we're absolutely vital in that, again, connective tissue. And then the last one, and I think this again speaks to the future of product management, which I'll give you the big reveal, is like we all absolutely believe it's strong, it's as important, if not more important. And I've started to see over the last couple of weeks some articles around product is the career of the future, which I think is maybe it's just someone making us all feel better, but I believe it. And this idea that you know prompting like is table stakes, right? But judgment and discernment is really the skill. And that requires a human, it requires someone and product, it understands someone thinking about the end-to-end. It's understanding the cost structure, it's understanding all the different pieces of it, knowing where the human needs to be, understanding the customer at the beginning, at the end, all the way through. So those are some of the themes that came up for the future of product management. But I think I want to start diving into some of the things that Heather first started talking about, which is really this, you know, I love this term, and I slop. It's such a so sad. It's so sad, but it's so real. And it's also so descriptive. When Heather talked about her sessions and the conversations that we had afterwards, so many leaders described PMs producing these huge documents that nobody read, these PRDs full of jargon that nobody really explained. I don't know who's reading these, and prototypes that didn't necessarily have real customer grounding. I remember you saying that one leader actually said, So what do you think we should do? And they got nothing back.

SPEAKER_02 9:04

So literally the product manager was like, you know, no answer because it was like, well, AI produced this and it looked good. And yeah.

SPEAKER_00 9:12

Yeah, nothing to do. So here we are. And with leaders, when they're building their own persona, their prompt libraries, their bots individually because they're frustrated. This is a pretty scary system. So I want you to talk a little bit about this idea about AI slop across your sessions. Do you think it's really a skills gap? Is that what came out in individual PMs? Or is it something more structural about how teams are being set up right now?

SPEAKER_02 9:39

Gosh, it's a good question. I think it's both. If I think about some of the challenges we hear, you've got this tension. You've got senior leadership saying, How are you AI'sing? It's a technical term, your organization. What are you doing to harness AI? And so you have all this experimentation and utilizing AI for absolutely everything you possibly can, in some cases, just to say, hey, this was AI driven and producing as fast as you possibly can, right? And that tension then doesn't necessarily mean that we're building the skills to utilize AI in a way that produces the best outcomes. And so you see a lot of product managers experimenting, quickly throwing in prompts and getting responses and saying, hey, it sounds good, right? We've even seen research that the more polished the output sounds, the more confident you are in the output, even when it's not right. And so I think you're seeing this need for speed. I feel this need for speed. What movie is that from? Anyway, so they want speed as fast as they can. They're trying all these tools as fast as they can and not necessarily seeing the output that is representative of the context that all of these product managers have, right? They have the customer context, they're closest to the customer, they're closest to the business, they understand the idiosyncrasies of the market and the like. Whereas LLMs are all just pattern recognizers and pattern finishers. And so if you treat the LLM as the answer man, you're gonna get the slop without the skill sets to best prompt it and review what you get. And so I think it's a combination of that tension from leadership and then this overzealous need to try these new tools as fast as you possibly can and show the production of what you've done without really taking the time to learn how to use them to get the best outcomes. That's great. So I think you surfaced a quality trap too in your roundtables. And so everyone wants to be a builder. PMs are building, they're designing, they're writing code with the different tools. Talk to me a little bit about this quality trap that you discovered. And is that different from what we just talked about? This AI slop and this 80% versus 20% is right on. But how did you hear it come out and how is that manifested? What the leaders are doing, and how do you prevent it?

SPEAKER_00 12:12

Yeah, it's such a great question. And I think it sort of builds, I think, on that AI slop conversation we just had. I think that one of the things which is going to warm your heart, Heather, because it certainly warmed mine, was really reinforcing this idea about understanding why, having PMs be in a position that they could really describe the why behind some of these artifacts. So moving, I think, okay, we don't necessarily need to author all of these documents. I think we constantly teach, start with AI, that's fine, input the material, and a first draft can be AI. We don't have to be the authors, but this idea of us turning into something like an editor-in-chief, right? And so this ability to question, to be able to really defend why, why are you making the statement? What's the sentiment, the insight, the customer learning behind it? This difference between this 8020, those are the places. And you can start to see how we started to say, where are those intersections? Where are you, the editor-in-chief, that you're stepping in? You're not making the first draft, but you're sure as heck aren't sending that to your CPO who's like, now what do I do with this? Without actually understanding and being able to explain the why behind it. We spent a lot of time talking about that role and that ability to be able to question. The other thing that I think was interesting around this AD20 was being able to double down on something that's, I think maybe the best way to describe it is like mucky customer evidence, right? When you have unreal or synthetic personas, when you are, I don't want to say making up the insight, but you're not really using primary research. When you can then mandate, we talked a lot about mandating primary research. And this is a non-negotiable stake. You've still got to be close to your customer, you've still got to be as in your learning plans, understanding. And that again, use AI by all means. This is the 80% to summarize, to give you synthesis, to help you with framing. However, you've got to be able to defend and answer the why. So those are the couple of the things that we really talked about. And then I think the last one I probably quickly mentioned, and I think it speaks to that product leader who said, I'm going to create this persona. We've heard this before. We've heard about product leaders saying, these are the templates, these are the standardized frameworks. We're not going to go and all recreate the wheel or all do different things. What's that structure? If you don't have that structure, go find one, go work with people like us who can give you a great one, but don't let everyone kind of like do it on their own. This is still a team sport. The leaders in the room are like, even though things are changing fast, those foundations, and again, I think it was just so rewarding to talk about sort of those foundations and the customer being so critical for us to be able to be effective. So those are a couple of themes there. I want to move now to this idea about value and outputs and value. So one of the things that I remember you saying was this idea about AI connecting to business outcomes and you know, your your note about sort of the story points. And I've heard several product leaders say to me, we have made this investment and it's ending up costing us more. They really didn't think through sort of that end-to-end cost output and what really was the business outcome, to the point where I think you had someone say they would never have launched if they'd understood this. So when you think about this, like when leaders go to their boards, because there's so much pressure for going fast and building more effectively and getting these prototypes out and cutting people. Like, what do you think they're actually showing? And where's that gap between what they're showing and what the board wants to see? What was the conversation around that like?

SPEAKER_02 16:21

Gosh, I think there's two separate conversations, right? One is really around the internal efficiencies. How do we leverage AI and how do we define or measure the ROI of infusing AI into the system beyond just tackling more story points, right? And the second conversation is how do we measure success of the AI products that we build? And how do we course correct as quickly as we possibly can to ensure that we're achieving the outcomes that we think we're trying to solve for, right? I would say the latter discussion is an age-old problem that we have had since day one in product management. It's how do you get aligned on the definition or measures of success before you launch, right? One of the examples that we talked about in a round table was launching this chatbot for support. It was augmenting their technical support. And because they hadn't articulated what the definition of success was that first contact resolution, was that the number of times a customer needed to be handed off to a live person? What are the measurements that really do tell us that the ROI is there, right? Because that wasn't set up front, then everything after launch, there was no measurements, operating mechanisms, or understanding of the implications of that tool, the chatbot, and whether or not it was actually generating ROI for the company. And so to me, this is the age-old problem. It hasn't changed. It may change in terms of the criticality of defining it before you launch, because your learning and pivoting with an AI product happens usually right after launch. It's not going to happen prior through concept testing or the like. It's going to happen when it's in reality and people are actually utilizing it for the tasks that you're asking it to use. And how accurate are you? And how much trust are you building and the like. So the dynamic of how we do this and how we measure ROI has changed a bit with AI, but it hasn't changed the need to get alignment up front, which is what you and I do all the time, Vidya, in product rebels, right? We really focus on establishing the measurements that matter early and make sure you're instrumented before you even go live, even if it's beta, alpha, whatever that may be, right? So I feel like that is still the age-old problem that I don't think we realize the implications of not doing it are even more painful once you launch AI. Because this particular leader struggled because trust went down. The brand identity and value went down because they didn't have those measures of success early on. They didn't know how the AI was solving the problem that they set out to solve, how well it was being solved, and then the collateral damage of when you lose trust and the like through an AI product. So I feel like that one, that RLI discussion, unfortunately, is an age-old problem that I think it's just more critical now and in a different dynamic that we're trying to solve for. Internal efficiency, I think, is we're still not connecting the dots to the quality of what's being produced, right? Yes, the speed is there, but is this AI slop, has that taken over? And what we release, is that still really the quality? Is the delight? Is it achieving the outcomes? Do we still have the early indicators of achieving the outcomes that we need to? I just think that one is just connecting the dots to more of beyond efficiency. It's really about making sure you're balancing the efficiency aspect of infusing AI and the customer outcomes that you're trying to solve for through the products that you're still building. So yeah, it's tough. And it's just more critical now because we move at lightning speed and the cost of launching something and failing that much faster with that much more collateral damage, I think, is just more painful. So, how did this come up in your discussion? I feel like it came up in every discussion, this concept of ROI and how we present this to our boards of here's the investments we've made, here's the implications it's had on the business beyond story points. How did it come up in this future of product management for you?

SPEAKER_00 20:41

Yeah, it's interesting. The way that I think we talked a little bit more about it was when and how to use AI. And because we're really talking about the future of product management, the conversation, rather than very specific examples such as you gave, was more around sort of decision making and this idea that how you both show up never to delegate fully, but also how you are coaching your teens never to delegate fully. So when you're thinking about outcomes, you have to be constantly thinking about, you have to understand what you're solving for. You have to understand what insights came into prioritizing this specific area. So when you go into the boards and you're justifying it, you're still so important that you're rooted. You can see that we took a little bit of a different tone because I think it's almost a bit of a defensiveness of there is a future of product management. We all came in a little bit like, well, everyone else thinks so too, right? And so rather than specifically talk about necessarily ROI decisions, it very much focused on our role in ensuring that those business outcomes, and as you said, it's a problem we've always had. How do we maintain our ability to influence those types of decisions going forward? So a little bit of a different Spin on that question.

SPEAKER_02 22:01

Yeah, but I think it's the right one, right? We as product leaders and product managers should always be the connective tissue. And I know I want to talk a little bit about this sort of switch from connective tissue to cardiovascular PM. I think that's a really interesting shift. But I feel like that's our job is to maintain the connection between what outcomes are we solving for, what problem are we solving for? Who are we solving that for, and making sure that whatever we do, whatever speed at which we do it in is connected back to those foundations. But let's talk about this idea of cardiovascular PM. I want to hear about that shift because there's still this concern that where is PM going? Do I have a role? Even some of the leaders in my roundtables had said, do I have a role here? How do I become a great product leader in this new era of AI? And your round table really talked about this idea of cardiovascular PM. How do we rationalize those two?

SPEAKER_00 22:58

Yeah, no, it's interesting, isn't it? Like I think this is a lot of the dilemma that we as an industry are going through. You've got to be all of these things. You've got to be a builder, you've got to be everywhere, you've got to do everything yourself. And you are the connective tissue. Okay, in some ways, you could argue that we've always been that connective tissue now, the cardiovascular system. But now there's this question that you have to do it hands-on yourself. And so, you know, boy, I think we're all struggling with this. And I think the first thing for us to acknowledge, and for everyone listening, we're all in this together. There is not one product leader or product team that I would say has nailed this or understand this. We're all figuring this out at the same time. One of the things that you're going to hear from us and as product rebels is how do you hold on to those things that really matter as a core discipline of product? How do you ensure that you don't delegate those pieces, how you understand how to work in this ecosystem, how you use AI as a thought partner, not that decision maker. And then how do you increase the confidence in your decisions by the way that you interact? I think there's a lot of experimentation right now, which is great. Heather and I have both talked. We've gone back to building because you need to understand this. But this ability to really get fluent, to be able to move into this new role and this new way of being. While we don't actually have a clear vision of that yet, I think the the traits that really matter to us, that curiosity, and that is right now showing up in the way that we interact with tools, the way we interact, the way that we create prompts, the way that we're using AI as a strategic thought partner. Those are the things we need to lean into, not create 50-page PRDs that nobody ever reads, right? And we're seeing, I think one of the things that came up is there's definitely a fluency gap in a lot of organizations. There's a lot of surface level sort of interaction and playing in writing prompts, but they're not actually, there's a depth of understanding that is lacking. And I think that in order to truly feel like you're the heartbeat, like you're flowing through all the systems. You understand, maybe I'm kind of taking this analogy a little too far, but you're really having to understand what that interaction looks like and get hands on and then choose deliberately and strategically like where it is that you are going to intervene. Where are those places that you need to be? And so this, gosh, we could probably spend another hour talking about tool stack and teams and how to work on all of this, but really that conversation is we're all in trial mode right now and just know that you're experimenting. There's no specific, this is the new PM, whether it's a builder, with whatever that could look like. I think we all understand that we're in this and we're experimenting together. And I think learning from each other in these round tables, please do that. If you don't have a group to talk to, get in there because you can feel like either you are ahead of the curve and maybe you're not because you're not necessarily using the prompts directly, which is some of the conversations that we've had, or you feel like you're really behind, but you're not. The experimentation is just fine. So this turned into a little bit of a therapy session, but that's really kind of like a little bit where the conversation went, honestly, in these round tables.

SPEAKER_02 26:28

Okay. And I love the idea of the cardiovascular PM. Here's one reason why. One of the things that we're seeing a lot among our clients, even, right? You know this, we've talked about this, is the PM being the facilitator of the best context for AI, right? And making sure their team members, whoever's building, right, whether it's the engineer, the designer, or even the PM, have we seeded our AI tools in a way that yields the best outcomes that are tied to the context, that are tied to the customer insight and the like. There's nobody really doing that right now. And so this experimentation yields empty output. And so that concept of, you know, the responsibility of the PM is to ensure that our tools are seated with the right insights, the right data, the right business context is actually huge. I feel like that's the blood flow throughout all of the AI tool stack, right? Anyway, I know I love that.

SPEAKER_00 27:25

No, I know this is why I'm so glad that you picked up on that because I was like, gosh, I'm overusing this, but I also love that analogy. I want to talk about a little bit about building because I think one of the people in your session said, if we can build anything great, how about we don't build everything? So true. And along with that, I think if you vibe coded it, you maintain it, which boy, can you imagine that? I loved that. Oh my God, I love that. Isn't that so cool?

SPEAKER_02 27:53

It's a big issue. And I will say that leaders are split. I heard some leaders talking about hey, building is so cheap now. Who cares if you make a mistake? Making mistakes are cheap. And I heard then another half of the room going, Are you kidding me? The second we build something and we take it to our legacy systems and we start investing in that, we are sunk. It's hard to unravel what we're doing, right? And so I think it depends on the business, right? Startups, a lot less to unravel, a lot cheaper to make mistakes. Great. I get that. But most companies out there that have been established that have some legacy system, even if it's two years old, three years old, four years old, mistakes can still be very expensive. And if you're building at light speed, all these different prototypes and really cool stuff, you're still gonna churn. And it's still an expensive proposition if you aren't taking the time to understand what's the right thing to do, what's the right problem to solve, and what is the thing that we should be doing to make the biggest impact in the market. So I think it's a balance and I think it's still mixed. I'm still a little worried about this. Well, making mistakes is cheap. I get it, but I don't think that's the case in many organizations. And so that critical thinking, that judgment, that prioritization is even more critical now because we're moving at light speed.

SPEAKER_00 29:21

You know, I'm gonna go on a limb and I'm gonna say that's gonna bite people in that yes, A S S. You can say ass. Okay, thank you. In about three to six months because yeah, it's just like the prototypes. This isn't really anything more than sort of the A-B experiments we used to run. It's like until you said stop the madness, stop counting this back to outcomes, what's important, stop counting the number of experiments you're doing. Start figuring out are you actually achieving the goal that you're looking for? Here we're in this place where it's cheap to build, but boy, you've got your customer reputation, you've got experience, you've got loyalty, you've got expectations, you've got brand, you've got all of these things. You've got operations that have to operationalize what you're building. Yeah. Oh, you've got costs. I mean, we could go on and on. I mean, there are real repercussions. And I laugh too when they said you maintain it. One of the product leaders, um, this is not at the round table, but I think this is a really interesting story. One of a product leader I was talking to recently essentially said they couldn't get their CTO to build. And so they did it themselves. And I'm like, I get it from a let's show you how it's done, let's show you what I'm thinking about, let's get this on board. But at some point, this partnership, I think, between technology and product right now is a little fractured. And this ability to really make sure that again, you are partnering, you understand the repercussions, you understand the support. This is product 101. We never would have released something that couldn't be supported by our company, the infrastructure, but yet somehow we believe that we can just go build something and throw it over the wall right now. To me, luckily we didn't hear much of that in the round table, but I am hearing that outside of the group of product leaders that came into these sessions. Are you hearing something similar?

SPEAKER_02 31:12

Yeah, what I'm hearing is even the product leaders are building their own stuff. And there was one like epiphany moment that I think we all had in one of the roundtables where the CEO came to the product leader and said, Hey, I think this is really cool. Let's build this. Can you ask routine bevel? He's like, better yet, I'll do it over the weekend. So he builds it over the weekend. He said it took him a couple days. He came back and he goes, Let me show you this. It's not a product. This is not gonna make big impact in the market. This is a cool idea. Yeah, I did it in two days, but I wouldn't put any resources against it. Right. So I see that sort of thinking and that, yes, we can build it, but should we? And so it's coming, but I still feel like there's this, I hate even saying this novelty of, oh my God, I can build it. So let's build everything. And the mistakes are cheap. I just feel like, like you said, I think it's gonna start biting people on the butt if they're not operating, like the product leader that I talked about, which he built it over the weekend, but realized this isn't actually a big impact. It's basically technology, which is cool, but it's not really a problem solver. Right. Get it up in the system, get it in your system. But the thing that I love about the speed and AI is you can build 20 different lo-fi concepts in minutes to solve a problem. Then go out and test it. Test it. But don't build the code and start trying to infuse it into your legacy systems and operations. Test it. So leverage AI for the speed to discovery. Awesome. But I'm just not there yet on, hey, let's just build 25 features in a day and go launch them because it's low risk. I'm not there yet.

SPEAKER_00 32:50

So let's carry on that theme and talk about when the rubber hits the road. Like actually generating new revenue from AI, right? Customers aren't going to pay extra for agents to use a product that already works for them. Yep. And this idea about building, like we're just going to continue this theme of why on earth would you just throw more agents onto existing SaaS platforms? And in fact, you're just saving your own cost structure. You're not generating more value. You're not generating value, you're not generating revenue. And in fact, if you don't do it properly, you can actually add cost to your bottom line, right? That's right. And it's this idea about, I'm not going to go as far as to say AI discovery doesn't work. I think there's a lot of really interesting things pulling customer information in, about being able to, as you said, generate a whole bunch of different ideas that you can test with and understand. That I think is really good. But this whole revenue problem feels like in your round tables, has anyone got an answer for this? How are they thinking about it?

SPEAKER_02 33:48

No, I think there's a couple things. I think leaders have been faced with what you said. Customers are unwilling to pay more for the same service, even if the technology has changed, right? And so what I'm hearing is we have to understand the problem and find big problems we can solve uniquely with agentix systems, right? It's stuff that you uniquely have particular data that can only solve this problem that creates a moat for you or your company, right? You've got the data, you might have some unique or patentable business logic that then lends itself to a unique offering, a unique big problem to be solved that you couldn't otherwise solve it. But I think it comes down to the discovery and the pressure testing of problems among your customer base that can be solved better through agentic systems and AI than just automation and the like, right? And I don't think we've unlocked that recipe, right? Of how do you find it? I think it comes down to the data, the unique business logic, and no one else can do this, which is getting pretty hard now in this day and age with AI. And so I didn't hear anyone having unlocked that challenge yet.

SPEAKER_00 35:14

But I can say, I want to end sort of on a hopeful note, but I feel like the teams that we've been coaching outside of these roundtables, one of the things and really understanding, we always start with truly and deeply understanding the data that you have, the mode that Heather just talked about. Like this deep understanding and then a structure of being able to start with the data, then be able to apply. I'm gonna call them more effective prompts. I don't know if they're the right ones, but we've tested our way into things that we believe are very effective. And then using AI as a thought partner to be able to interrogate and strategically think through this and then that foundation around customer. So much of this is gonna be recognizable to our listeners. It's core product, but it's core product, like leveraging AI in ways that really we believe make a difference. And there, I think we've seen some pretty like great breakthrough ideas. Now, we have to yet see if they'll be revenue positive, but indications are that they're on a really good path. So again, I think this probably felt a little bit like therapy and the roundtables. It's great to know we're all figuring this out together. Let's please keep sharing. And if any of this resonated, we would love to hear from you. We're going to keep running these roundtables because the conversations are just so good and they're too honest to stop doing it. We love them. So if you're a product leader who wants in on a future session, just reach out to us. You can find us at productrebels.com or you can just DM either me or Heather on LinkedIn and we'll hook you up. And if your team is navigating any of these challenges we talked about today, whether it's a judgment gap, it's the ROI question, or figuring out how your product team should operate in an AI world, we would love to talk. We can give you some more specific examples when it's not like a public forum. So just schedule some time with us. It's in the link in the show notes. Thank you for listening, Rebels. We'll see you next time. Good luck. Thanks for listening to this episode of the Product Rebels Podcast.

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