The Hidden Power of Messy Teams with Johnathan Cromwell

What if the teams that struggle to define their problem early are actually your best innovation bet?

Dr Johnathan Cromwell, Associate Professor of Entrepreneurship and Innovation at the University of San Francisco, shares findings from a study of over 1,100 real innovation teams at a Fortune Global 500 company. The results overturn one of the most deeply held assumptions in innovation management: teams that started with low problem clarity but gained it by the midpoint achieved an implementation rate of over 80% — compared to approximately 50% for teams that defined their problem clearly from the outset.

In this episode, Mark and Johnathan explore:

• Why problem and solution co-evolve rather than follow a linear sequence — and what that means for how you run innovation projects
• The critical midpoint transition: why the halfway point of any project is the moment that determines success or failure
• How to manage a portfolio of front-end innovation projects when ambiguity is a feature, not a bug
• Why Jobs to Be Done are not as stable over time as the framework assumes — and where the five whys technique changes the game
• How AI is not just solving existing problems faster but revealing entirely new problems companies did not know they had
• The three modes of AI adoption: exploiting existing problems (Instacart), expanding the problem (Khan Academy), and exploring new ones entirely

Johnathan also shares practical advice for the VP of Innovation under pressure to show return on investment — and why dismissing projects without a clear problem definition may mean dismissing your best future innovations.

Published research referenced in this episode:

• Cromwell & Harvey (2026). The Hidden Power of Messy Teams. MIT Sloan Management Review, Spring 2026.
• Cromwell & Harvey (2025). A Problem Half-Solved Is a Problem Well-Stated. Research Policy, 54(3).

Connect with Johnathan Cromwell: LinkedIn: www.linkedin.com/in/johncromwell/

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Transcript

Problem Framing As Creative Work

Johnathan Cromwell 0:00

The teams that started with lower clarity, but then gained it by the halfway point. This led to an implementation rate of over 80%.

Mark Blackwell 0:14

Welcome back to Arkaro Insights. I'm Mark Blackwell, and this is the podcast where we help business executives thrive in a complex adaptive world. Now there's a legendary quote that you've probably heard of, said to be attributed to Albert Einstein. And it goes along the lines of if you had an hour to solve a problem, I'd spend 55 minutes defining the problem and five minutes solving it. It's clean, it's tidy, it's a bit easy to remember. But sadly, as we discovered a few months ago with Zaranichovich Pringle, he never said it. But there is something that he and Leopold Infold did actually write in 1938, which may actually be even more insightful. They wrote the formulation of a problem is often more essential than its solution, which may be merely a matter of mathematical or experimental skill. To raise new questions, new possibilities, to regard old problems from a new angle requires creative imagination. Note the shift. Einstein wasn't advocating for a rigid linear timeline, like focus with only 55 minutes and move on. What he was focusing on is that the formulation of a problem is an act of creative imagination. This is an issue close to the heart and work of our guest today, Dr. Jonathan Cromwell. Jonathan is a professor at the University of San Francisco, and he's going to join us today to discuss why the problem and solution are not linear sequential steps. Welcome to the show, Jonathan. Thank you, Mark. It's a pleasure to be here. We live in the world of complexity. One of the things that we battle against is these beautiful Chevron linear PowerPoint charts, which present the world as a nice clean living thing when our reality is messy. And I think you wrote about messy teams.

Johnathan Cromwell 2:19

That's right. The hidden power of messy teams.

Mark Blackwell 2:22

The hidden power of messy teams. So this is what we want to talk about because this is our field ground. So I want to just catch our loyal listeners. They may remember a podcast a while ago with Roni Wright-Powerman. And I think that's someone that you know very well. And she first of all brought us to the insight that teams that spend approximately half of their time, if not more, thinking about the problem come up with more creative

The Hidden Power Of Messy Teams

Mark Blackwell 2:50

solutions than those that don't. So would you agree with that statement?

Johnathan Cromwell 2:54

I I yes. Short answer, yes. I have a longer answer, but I'll I'll wait for I'll wait for the right opportunity to elaborate.

Mark Blackwell 3:02

Yeah, so yeah, the fundamental premise holds true, but I think you discovered in your working with messy teams, there's a little nuance on that. So maybe you want to go back and tell me how you did the work and what you discovered.

Johnathan Cromwell 3:18

Sure. And and I and I want to also highlight that a lot of the existing approaches, you particularly in the academic literature at least, will present these sequential steps. And you described them as the chevron boxes that link very nicely together. Um and while these steps are often summarized or or depicted in a linear fashion, there's there's a pretty open acceptance and an acknowledgement among almost any scholar that you'll talk to that it is indeed highly dynamic and iterative. So if we take the dynamic component as a given, and it's not necessarily a sequence of first define the problem and then solve it. What the study that you're referring to was looking at was not so much the sequencing of the problem versus solving, but which component or which side of that process is taking the lead. So if you if you imagine a dance, one partner in the dance is leading and the other one is responding. And when you're doing problem solving, either the problem or the solving, the solution can take the lead in shaping the other and in directing what the other, where you might take the other or what you might view or be able to think of on the other side of that process. So the traditional problem solving process tends to assume that the problem leads. And that's, I think, also reflected in the quote, uh, the the misattributed and accurately attributed quote of Einstein are often placing more weight and emphasis on the problem is the most important and it should be really understood to help shape the solution. And I think a lot of Roni's work also has that. She was one of the first really scholars to go really deep on the problem construction side of the process. And so she has laid a lot of really important foundational work there. And her work along with others have tended to continue championing the problem as the leading side of the solving. So that is the assumption that was being tested in this new study was is it always the case? And for example, there are many times when the solution actually leads and shapes the problem. And anybody who has been first encountering AI and trying to figure out, oh, this is a pretty useful tool. I I wonder how it can be applied to my work or applied to my life. Or and uh you play around with it, you tinker with it, and you say, oh, that gives like really, really interesting insights, or oh, I've never had a chatting partner on this topic that I've always been really interested in, or I can drive in the car and have a verbal conversationalist uh in ways that's a lot more engaging than a podcast, for example, except for this one, obviously. And so what we what we looked at in that study is well, how does the problem shape over time? And then what's influencing the shape of the problem? And what we what we found in this in the study, we looked at a Fortune 500 company. It was in the telecom industry, and they held an innovation contest every year. And by the time we learned about it and were able to study it, they they had grown, I think they were in their 10th year, and they had grown to more than 1,100 teams participating every year from around the world. And it was a very tight timeline. It started in the middle of summer, it ended at the the end of the calendar year. Uh, and the interesting thing about this competition is that they didn't just crown one winner, which they did do, and they had a series of quarterfinals and semifinals and grand finals, and then a uh one winner to crown champion of the competition. But they

Inside A Fortune 500 Innovation Contest

Johnathan Cromwell 7:17

also encouraged all their teams to pursue their project and implement it in the business. And so while 1,100 teams participated, one wins, but about 650 went through and followed through to implement the innovation in their business. So that gives us a nice study because if you just study the winner, you miss out on all sorts of interesting effects and you can have selection bias and all the, so you don't know what happened to the failures or or so this competition allowed us to look at the full scope of all the all the competitors and were they able to achieve this really important milestone of innovation, which is actual implementation. They're not only coming up with the idea, but they're actually getting it to have real impact in the business. So we looked at that outcome and we also looked at how the problem evolved over time from relatively lower clarity to higher clarity. And just as examples, one of the items that we that we use to measure, the goals to be accomplished are relatively clear. So, okay, so that that kind of gives you uh a sense of of uh you know, it's it's on it's on topic with the problem. We also measured how much exploring or or divergent thinking took place among the teams and also how much convergent thinking where they're trying to choose and select and narrow their focus onto a range of ideas. So we so we looked at all these measures and we looked at two points in time at the very beginning of the competition and at the and the exact midpoint of the competition. And what the the main finding, the the big finding that that gets the headlines is that the teams that started with a high, with a very clear problem and maintained that clarity throughout the project, this is typically the most valuable thing you can do in an innovation team is that you all develop a clear, shared understanding of the problem. You know what the goals are, which then allows you to coordinate and organize your divergent efforts towards that shared goal. Canonical research in the innovation literature talks about this. West 1990 was one of the biggest proponents of, well, he was one of the first scholars that studied innovation in in corporate business settings, and and and he has this great quote about the value of a problem and the clarity of the problem early on. So we so teams that started with a high clarity maintained it, had an implementation rate of 55%. Um, the contrast were teams that started with very little clarity and more ambiguity, which according to the literature is is a is a really bad recipe for team innovation because you need that shared awareness and shared goal. But nevertheless, the teams that started with lower clarity, but then gained it by the halfway point, this led to an implementation rate of over 80%. And so this jump in performance leave leaves a big question. Well, what's what's caught what what why are teams that are clearer about what their goals are? Why are they less successful at actually delivering the results?

Mark Blackwell 10:25

Okay, so I'm gonna be the contrarians. There is clearly in the commercial world a love of linear Chevron systems. If you're the manager of a portfolio of innovation projects, you know that you can't fund everything. You've got to have a disciplined approach of killing projects early to enable the good ones to survive. So you want to have some guidelines to say, hey, when am I going to stop this? Because I've only got a finite innovation budget, and if I don't kill them, the real winners will not receive the funding. So that's why the stage gate process has got an allure. It's got an allure to say, hey, well, there are three fundamental questions at the front end of innovation. Is it desirable? Is it viable? Is it

Stage Gates Without False Certainty

Mark Blackwell 11:12

feasible? So translating that desirable, is there a value proposition? Do customers want this? Yes, no? Is it viable? Could I make money out of it in my business? Maybe someone else could, but can I make money out of this idea? And is it is it feasible? Can we can we actually develop a prototype? Is it this going to work and make sense? And you want to answer those hurdles pretty quickly. But for my first listening to what you're saying is actually you're making that first step even harder for a manager of a portfolio of projects to answer the question.

Johnathan Cromwell 11:43

I would not say, I would say no. I I there's way a version of it where it might be harder, there's a version of it that makes it easier, I think. Because the end point or midpoint of all these innovations is to get to a cohesive match between problem and solution. That you're and that state is what you are looking for, the signal in a stage gate process is that you want to make sure that your solution is solving a problem that matters and that it's viable, feasible, desirable, all those criteria you mentioned. The recommendation, I suppose, or the the implication is that if you're on one of those team projects, that you're more comfortable that you accept that ambiguity as not necessarily a problem or an issue that is going to undermine success, but you embrace that ambiguity and you allow that to what that what that gives you is it gives you more degrees of freedom during the search for that match between problem and solution. So if you anchor down the problem early, that might look like you have shared goals, that you are able to achieve milestones and make progress toward those shared goals. Um when you have a clear to clear defined problem, it's easier to evaluate what good ideas are better or worse. You can feel yourself making progress towards it, but you may not necessarily find a solution. And so if you constrain the problem and only explore the solutions, then you're missing out on degrees of freedom when if you're trying to find a match between those two things. So you can also embrace the constraint of the solution. What can we do? What tools do we have available? What skills do we have available? How can we combine these various skills and tools and components into new things? Constrain yourself there, but be more open on how do we apply those potential solutions to different market categories, to different target customer needs. And by opening up the process to allow flexibility on both the problem and the solution, that gives you more degrees of freedom to find a successful match during one of those stages.

Mark Blackwell 13:59

So, in preparation for a future podcast, I was reading a book by David Hearst, he's a future guest. And I think he proved your higher your theory, he made a lovely case study about the approach taken by Japanese automotive manufacturers versus American automotive manufacturers. Typical behaviour of an American automotive manufacturer was rigid specifications of what parts they needed from a Tier 1 supplier for the design of a new car. Think of something like a bumper length, for example, or something, you know, something rigid and say the car is going to be exactly this long, so this metal has to form within this narrow specification. Whereas the Japanese automotive would go to their close partner, tier one suppliers, and say, Well, here are some rough boundaries by which you're going to be constrained by we want to seek the optimal solution within those rough boundaries. And that seemed to be a beautiful demonstration of your point that uh you get a better result if you're not too constrained too early in the process and you can live with ambiguity.

Johnathan Cromwell 15:08

Yeah, yeah. I I sounds it sounds like a great example. I might have to borrow it from my course.

Mark Blackwell 15:15

I'll send you more information on this, but it was in the most businesses, businesses like Bosch, they're famous for it. So they give teams $100,000 or so, or they give which is equates into a certain amount of time to spend that $100,000 and they've got to make a go-no-go decision by then. So you're saying that if you haven't really finalized your problem definition by 50%, that's the optimum time from your work, as I understood the work. So that sort of helps square the circle that we're facing. How do I manage the tension of managing a portfolio of front-end innovation projects, but also allow the flexibility? That was one thing that was came to me.

Johnathan Cromwell 15:55

Yes, the there's three main phases, and this is based on some Teams research that was first developed in the early 90s that we we built our our theory on is that is that this uh concept of a midpoint transition is a super important time in any team project as well as innovation projects. And it's been shown as a just a natural phenomenon, whether it's a one-hour project or a one-year-long project. There's a natural tendency for teams at the midpoint, or you know, give or take 10%, to naturally reflect on what they've done and then start planning towards the future, which is you realize, oh, I have a deadline, we gotta, we gotta get things into gear. And so if you miss this midpoint transition, if you if you float through

The Midpoint Transition That Matters

Johnathan Cromwell 16:42

the midpoint of a project and you're still not, you haven't found that coherent match of problem and solution, that that should be an alarm bell. And uh it might naturally arise anyways, that your team members might raise the alarm early, but what your job as the leader is to facilitate that, is to understand that this is a natural process, a natural dynamic, and not to panic and to help others not panic and guide the process from phase one, which is open-ended, relatively open-ended exploration, to the convergent phase of let's find out what's really gonna what we're really gonna deliver by the end by our deadline. And then when you get to that point, phase two, uh, the end of the project is really about execution and coordination and working in that really cohesive team where everyone is doing their part and contributing and and and it's it's feeling like a synchronous experience.

Mark Blackwell 17:42

So did you um sort of double click and do any sort of root cause analysis and what was going on?

Johnathan Cromwell 17:48

Yeah, for sure. So that that was the the the question was well, why is it that teams that are clearer at the beginning struggling to implement their projects by the end? And and so we we talked about one of those mechanisms was the flexibility that you have on an innovation project. More flexibility, more degrees of freedom on the problem and the solution just allows you and the team to find a successful match that you can actually achieve. And then the other mechanism that we looked at, and the empirical data we have for that is showing that teams that did early divergence, so they were lower on the problem but higher on divergent exploration of ideas, it had a weak effect, but but significant effect on whether that they were able to successfully find a clear problem to focus on. So early divergence, so this is the early solving is shaping the problem, is shaping the ability to clarify the problem. So that's that's where things flipped in the traditional script of the dance between problems and solutions. The second uh stronger finding was that the convergent process later was a very powerful predictor in the growth of clarity. And so as you evaluate, let's say you have five or six different ideas, and you're trying to say, what as a team, you're saying, we we can only do one of these things. Which one do we do? And the natural evaluation, oh, I like idea A because it seems more profitable and the target users, they're more ready to adopt. But option B maybe has higher long-term potential. And so you start uncovering these different criteria, and that process forces teams to identify what their priorities are and ultimately make a decision where they all have a stronger shared understanding about what the goals actually are, what the job to be done actually is. So that mechanism of early divergence, later convergence, even though the problem isn't known, can be a mechanism to find the problem.

Mark Blackwell 20:05

Yeah. Well, I as I was reading, I there's two ideas that struck me, um, a little to understand, and I want to test them if they have any validity at all. So you mentioned jobs to be done. It's I think one of those theories that as all of these theories look beautifully simple, but the application is much harder than you realise if you do it properly. And a true job to be done should be timeless, independent of a solution and independent of time, in consequence. And to for our listeners, the the often told simple example of this is Henry Ford, when he said, If I told people what they wanted, they would have a faster horse when you know the solution was really a car. And if we put translate that into jobs to be done

Jobs To Be Done Deeper Than Horses

Mark Blackwell 20:52

thinking, you know, the real job to be done is move from A to B, which is independent of solution. But for goodness sake, you can c understand why people think the problem to be solved is finding a faster horse. Um and so I wonder how much of it was that sort of discipline in the problem framing in the in the beginning.

Johnathan Cromwell 21:12

Unfortunately, we we don't, I don't know.

unknown 21:14

Yeah.

Johnathan Cromwell 21:14

If if I'm if I'm gonna play devil's advocate back at you. Yeah. Uh I I the the the horse versus the point A to point B, I think what jobs to be done facilitates is it it gets you to the the the core reason why you want to do what you think you want to do.

Mark Blackwell 21:33

Yeah.

Johnathan Cromwell 21:34

And so if you if you ask, oh, do you do you want a faster horse? You say, Oh, well, the real underlying reason is you actually want to get from point A to point B, and that allows you to explore alternative ways to get from point A to point B. But you might ask another question below that is well, why do you want to get from point A to point B? And it could be because for you know there's a lot of different reasons why. Yeah. And in the specific reason why can The potential solution space.

Mark Blackwell 22:01

Correct.

Johnathan Cromwell 22:02

So if your goal is to communicate with, I need to be present so that I can talk to my bank account manager. Or I need to be present so that I can witness my friend's wedding. One version means can be solved with a phone call. And so if you know in those days the phone didn't exist yet or wasn't adopted yet, that might be a solution just to facilitate communication. The other one is like the physical presence really matters. So I think what jobs to be done is it helps you drill down to the core reason why. I don't know that all real, true jobs to be done are timeless. I would imagine that the as technology evolves and as new things, we have new experiences and new capabilities. The nature of problems at their core, at least the core of what they need to be to be an effective innovation in the jobs to be done framework, that probably changes over time as well. Like our needs today are just fundamentally different than they were five years ago. And they were fundamentally different than they were 15 years ago. So it's not so much looking for a timeless problem, I would say, but understanding what is the underlying driving force of why that goal is considered important to your customer target user.

Mark Blackwell 23:23

Totally with you on the five wise approach. You know, I've got a slide which starts off with someone saying they need a nail because they got a bookcase and ends up them buying a Kindle by exactly the same logic that you said. So with you there. But you're touching now on another area, which is how the world is changing around us. And you published a paper in the Harvard Business Review about AI. Because my other hypothesis as I was reading the book is that we are finite in our problem space because we are at some degree constrained by our known solution space. And all of a sudden, AI comes in and just opens up jobs to be done to address that we would not even thought about addressing.

Johnathan Cromwell 24:13

Yes. Right. So the yeah, the the article you're you're talking about, uh that was the third and final phase of how you can leverage a new technology, an emerging technology that has seemingly unbound capabilities or or capabilities far beyond that that we

How AI Expands The Problem Space

Johnathan Cromwell 24:32

we could previously imagine. And so that third phase is exploring new problems.

Mark Blackwell 24:36

Yeah.

Johnathan Cromwell 24:37

The first two exploiting your problems. So the the first wave of innovation that we saw, the example was Instacart, very early to adopt AI. Instacart helps people get groceries in delivered groceries in less time. So that's helping them save time and be more convenient. AI comes along, and Instacart says, this tool can be useful to help us get customers their groceries more groceries, better groceries in less time. So it didn't change their problem, but it helped them do it faster, helped them accomplish their same goals at a higher, a better rate. And then there was the second mode, which is expanding the problem. The example there was Khan Academy, that traditionally was started as an educational service for students to self-learn, self-guide through various topics. And so there's probably thousands of videos at this point on various topics that are typical in junior high, high school, maybe even younger. And the way that Khan Academy was originally set up is okay, we have all these videos. Students, figure your way out, figure your own way through these or just explore them as you need. And maybe there were some guides that that helps, or you know, there were packages and playlists that you could go through. It was all self-guided and it was, it was, it was passive. Um, and then AI comes along and they said, we can not only help students guide themselves through this material, but we can actually proactively help them learn in ways that they we couldn't do before. So that's one way they expanded. They expanded their services to the students. They also expanded their services to teachers. And they said, oh, we can actually use AI to help teachers do lesson plans. So they significantly expanded their scope, still within the same, you know, relatively same problem space of education. And then the third level was the companies that are really on the forefront of exploring new problems that we didn't even know we had before. And the quote there is uh the Steve Jobs quote from when he was reflecting on building the original Apple and the and doing market research for the iMac. I think he was asked, did they do any market research for the iMac? And he said, it's really hard to design products by focus groups. A lot of times people don't know what they want until you show it to them. And so I think that really captures the essence of you're proposing a problem to people that that they didn't know that they needed solving. You could you could ask them like in the focus groups, and and they I don't know, it's it's hard to come up with an example now.

Mark Blackwell 27:21

To me, that's a classic jobs-to-be-done proof point. Is it people are experts in their problems, they're very poor in creating solutions. So people inevitably design poor solutions to what they want. Yeah, that's why they will say they want a flying car or they want a faster horse, which is what they can immediately imagine as a known solution space, but why the discipline of jobs to be done helps us really think, okay, this is what we want to do. But I did like the discussion you have, like what these guys were suddenly doing is opening up personalized education. I bet you they didn't start their innovation saying we're out to build a personalized education module, but it was that pure discovery of playing and out of the play emerges this personalized education module.

Johnathan Cromwell 28:14

Yes, yes. I think uh so the so learning about the capabilities and then realizing, oh, we can actually do more things and and for example, creating personalized education as opposed to passive effective education. And I think an example of of where companies are introducing new problems to customers that the customers didn't even necessarily wouldn't have said, I need this, is are these companies that are building personal assistance, uh and like an executive assistant for non-executives, for example.

Mark Blackwell 28:49

Yes.

Johnathan Cromwell 28:49

And if you asked people before, like, what do you have trouble with? I have I have trouble with organizing my schedule and I have to manage my kids doing this, and they have to go to these meetings, and it'd be really great if I if I had a calendar system. So so they're they're they're gonna be constrained to the existing solution space to solve these individual problems that they think that they have. And what these companies are there's several companies now that are saying, well, we can actually build you a comprehensive personal assistant that for most people, unless you were a certain income bracket or a certain pay grade at a company, you never thought I really need a personal assistant right now. And so I think that's an example of showing people what a nicely packaged product is. And then they can start seeing all those things click into gear, like, oh wow, I can use it for all these different things that have been causing me problems or you've causing me troubles that I I know were there, but I never would have articulated it or seen it as a cohesive problem.

Mark Blackwell 29:56

There are certain things that people didn't realize they could solve, but the flip side of that is innovators attacking spaces that they feared to do before. So the education wouldn't knowingly have gone into personalized innovation, but came across it by chance. The learning I'm getting from this is we should break some of the rules about solution thinking and start playing with some of these new technologies because they might expose ourselves to problems that we feared that we couldn't even tackle.

Johnathan Cromwell 30:29

And to really allow yourself to deliberately explore the problems. And I and I think this is happening, and I think people are doing it already and naturally, but what they they may be struggling with it because it's a different skill set, it's a different mental skill set. And and I do have other research showing that this is a distinctive mental process, and people tend to struggle with it more compared to more traditional divergent processing, where you you know what the problem is and you're just trying to come up with many different ways to solve it. That tends to be about 30% uh easier for people than this new mode of problem solving, this alternative mode of problem solving, where you're you're trying to infer problems from something that's more concrete and tangible.

Mark Blackwell 31:20

So to wrap up, we have the classic closing question. So you have the vice president of innovation sitting next to you. And what should he do differently on Monday morning after this conversation?

Johnathan Cromwell 31:33

Vice President of Innovation, what do you do?

Mark Blackwell 31:35

Vice President of Innovation in a large company, he's got he's got pressure from his boss saying, Where's my return on investment? And he's got a lot of projects, all of which have high uncertainty because that's what they are at the front end of innovation. What should he do differently than he's been doing before?

Johnathan Cromwell 31:52

I would say that there he absolutely needs to encourage and be accommodating to projects that have not figured out the core job to be done yet. And to allow to create

Monday Morning Advice And Where To Follow

Johnathan Cromwell 32:07

a a new set of metrics at a different set of time at a different time point in the process to evaluate whether the project has the true potential to go the distance and not only just go the distance, but be high return on investment, a breakthrough product in their catalog. And and the key distinctions would be uh looking at the relative timelines of those projects and the relative clarity of the of the problem and and not dismissing projects that don't have a clear problem yet. That's not the the only barometer of future potential success that should be looking at.

Mark Blackwell 32:48

Thank you very much. Now, where can people find out more about your work? And maybe what are you looking at the future?

Johnathan Cromwell 32:54

The best place to stay up to date on my work would be LinkedIn. So you can connect with me there and I post all my studies. I try to put um put them into terms that are easily understandable on there. Future work I'm looking at are is to dive deeper into the nature and structure of problems and how they shape our perceptions of solutions. And so while the process of coming up with problems may not be linear, the way that you communicate a project is is almost always prescribed to be a linear, design the problem, set up the tension. Here's a solution that solves the tension. So you tell a story with it. So we're looking at the uh the structure of those problem statements and can they systematically uh help people feel like the solution overall or the concept overall is more attractive? And then also looking at the leveraging AI as a research tool. So doing the emergent thinking process that I've been that we've been talking about for a lot of today, can this be leveraged for research purposes in new and valuable ways, particularly with uh psychological and experimental designs?

Mark Blackwell 34:02

Yes. That would be a topic I would love to explore. Listen, circle back to where we were a few months ago with Scott Burlson on this. We were coming up to with a thinking that AI is great at coming up with jobs to be done listings, but prioritizing importance and satisfaction, that's an innate human thing on the convergent phase of the activity because we're not as rational as we often think we are. So I would love to know what you come up with on that. Um, it would be fascinating. So please come back, Jonathan, when you can.

Johnathan Cromwell 34:36

Well, is is whenever you'll have me, I'll I'll be happy to join. Grilliant. Thank you very much, Jonathan. You have a wonderful day. Very kind and thank you for being on the show. Bye bye. Thank you, Mark. Bye. Bye bye.