From AI to ROI: Keeping the soul in product development

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Today’s AI tools allow more people to turn ideas into prototypes and products faster than ever. But as the barriers to building products fall, product leaders face a new challenge: ensuring that speed and automation do not come at the expense of judgment, creativity and human connection.

In this episode of CGI’s From AI to ROI podcast series, host Helen Fang is joined by Jim Wicks, Professor and Director of the Master of Product Design and Development Management program at Northwestern University and the designer behind iconic products including the Motorola Razr, and Mike Hyzy, Vice-President, AI Strategy and Product Development Lead for CGI U.S. CSG.

The conversation examines how AI is reshaping product design and development, where organizations should accelerate and where they should deliberately slow down, and how product roles, teams and skills are beginning to evolve. It also explores what organizations must do to preserve community, culture, critical thinking and the “soul” of product development as AI becomes embedded throughout the product life cycle.

Key takeaways from the episode

1. AI is raising the floor for product development, but raising the ceiling still takes human expertise and judgment.

AI tools can help people without traditional engineering or design backgrounds rapidly create websites, prototypes and working products, and also help experienced professionals return to hands-on activities as their careers progressed.

However, producing something quickly is not the same as producing something with quality, and human judgement and experience is still critical to be able to both leverage effectively and asses the best use of AI tools.

“AI really has the ability to raise the floor of product design and development. But the question should be: How much does it raise the ceiling in terms of quality and innovation?” — Jim Wicks, Northwestern University

2. Faster prototyping makes prioritization more important, not less.

When almost anyone can create a convincing prototype, organizations can quickly find themselves facing “death by a thousand demos.” Leaders may be presented with hundreds of ideas and proofs of concept, each competing for attention and investment.

The ability to build more therefore increases the need for disciplined prioritization. Product teams must remain focused on which problems matter most, what creates genuine value for users, and which features strengthen rather than dilute the product’s value proposition.

“There’s a real risk that people make too much stuff and add all these features. We can do it, so let’s do it. It might be white noise for the consumer.” — Jim Wicks, Northwestern University

3. AI accelerates research, but understanding people still requires human insight.

AI can dramatically accelerate activities such as analyzing interviews, synthesizing research, reviewing customer sentiment and identifying patterns across large volumes of information. These capabilities can give product teams a stronger starting point and free people from time-consuming manual work.

But tools may capture what users said without fully understanding why they said it. Ethnographic research, direct observation and conversations remain essential for identifying nuance, emotion and unspoken needs. Product teams must decide deliberately where efficiency adds value and where slowing down for reflection, curiosity and human engagement is required.

“You can kind of distill that with a lot of tools right now, which is really fantastic. But you can also miss the nuances.” — Jim Wicks, Northwestern University

4. AI is reshaping roles and responsibilities, but strategy and collaboration remain essential.

As product managers gain design capabilities and designers gain the ability to build working products, traditional boundaries between product, design and engineering are becoming less distinct. This may lead organizations to reconsider how product teams are structured and which skills different roles require.

Despite these changes, the fundamentals of good strategy remain consistent. Organizations still need a clear vision, an understanding of their biggest obstacles and the discipline to make trade-offs. Teams also need the ability to challenge one another, consider divergent perspectives and carry insights from early research through the development process.

“What really hasn’t changed that much is really good corporate strategy. What’s your vision? What’s your aspiration? What are the biggest bottlenecks that are in your way?” — Mike Hyzy, CGI

5. AI should be a cognitive enhancement, not a cognitive replacement.

Adopting AI is not only about encouraging people to use new tools. Organizations must help employees use them in ways that strengthen rather than weaken their ability to think critically, solve problems and make informed judgments.

For AI to be leveraged as a form of cognitive enhancement rather than cognitive replacement requires thoughtful education, opportunities for mentorship and environments where people continue to learn through discussion, experimentation and collaboration.

Community and culture are central to how people develop judgment, exchange ideas and create products that have meaning for the people who build and use them.

“We need to get people to adopt AI, but in the right way: to be able to use AI as a cognitive enhancement, not a cognitive replacement.” — Mike Hyzy, CGI

Featured guest: Jim Wicks
Director, Northwestern University mpd² program
Clinical Professor, Segal Design Institute

Learn more

To learn more about how CGI applies technologies like AI to addresss business challenges and deliver outcomes, visit our artificial intelligence page. For more information about A3F (AI Adoption Accelerator Framework), visit the page here.

Read the transcript

Introduction

Helen Fang
With today’s AI tools, it feels like anyone and everyone can build prototypes and products now. It’s led to a lot of exciting things, like expanding the accessibility and opportunity and lowering some of the technical barriers to entry. But sometimes I question if we’re just building things for the sake of building them, or if with the speed and ease, we’ve lost some of those intangibles, the very human part of building products for other people.    

To help explore some of the opportunities and nuances around AI in product, I recently sat down with Jim Wicks, currently a Professor at Northwestern University and the designer behind iconic products like the Motorola Razr, and Mike Hyzy, my colleague, Vice President, AI Strategy and Product Development Lead for CGI U.S. They know each other well, as they work closely together in CGI’s partnership with Northwestern, and as two deeply experienced product builders and leaders they have a very hands-on view on AI and product.

I’m Helen Fang from CGI, usually producer of our AI to ROI podcast, and sometimes, like today, also the host. What I loved in this conversation was how although we’re talking about AI and product, we’re really talking about people and the community and the impact. I learned so much from this conversation, and I think there are so many valuable lessons for everyone in here, not just if you’re working directly in the product space.

Helen Fang

Mike, Jim, it's really great to have you today. Maybe we start off with a quick intro. Mike, you could go first.

Mike Hyzy

Sounds good, thanks, Helen. In my role at CGI, I work all across the product development landscape from helping executives understand what their strategy is, operating models, all the way into how do we build faster and better and more efficiently with AI. And then one of my favorite areas, you know, which I've worked with Jim on, is the adoption piece and the human part of how do we connect the human to the AI and using behavioral science to kind of transform the way we work and the way we think with AI.

Jim Wicks

All right. Well thank you, Helen and Mike for having me today.

So I direct the Masters of Product Design Development Program at Northwestern called MPD. With that, we host usually around 30 to 35 students a year. The intent of our program is to help students really kind of master the kind of blend of product design, development [and] management. So a blend of kind of really hardcore product design development curriculum along with kind of MBA type business curriculum.

I started teaching here about nine years ago. Prior to that, I was senior vice president for design at Motorola, Google, and Lenovo for about 15 years. And before that, I was with Sony for about 15 years in New York, San Francisco. And started with them in Japan, where I also did my masters in design and comparative cultures at Nihon University in Japan. So yeah, that's me, and I'm really happy to be joining today. Thank you.

How AI changes the baseline for product work: Raising the floor vs. raising the ceiling

Helen Fang

Yeah, great. And we're super glad to have you here today. so maybe we'll start off a bit on the topic of where is the value. So there are a lot of headlines currently about what AI can and can't do. So as leaders and builders in the product space, could you guys maybe talk a bit about how product related work has been evolving as a result of AI?

Mike Hyzy

Jim, I would love your take on this because I just taught a class last weekend on the Claude portfolio. So going from Chat to Cowork to Claude's new design tool to code to build it. And you know it's fascinating to teach that, but like if I didn't spend a decade doing it manually, I would not understand the prompts and the methodologies to insert into Claude to make it get to the export that I'm comfortable with.

So how how do you see this as a as a teacher and innovator in the space? You know, we have these tools that could rapidly build some things, great design templates, but when you get in the nitty gritty of it, you still need to understand the foundations of design and product development to to really be elite with these tools, at least in my opinion.

Jim Wicks

Yeah, no, I think it's a really good point. I I think people often will say AI really has the ability to really kind of raise the floor of product design development often. But the question might be how much does it raise the ceiling in terms of quality and innovation?

If I think about it cynically, sometimes I I think what you talked about right there in terms of having that experience, experience of product judgment and decision making and development on your own, it's a lot more natural for you to get into the process of developing in AI and kind of understand what's working and what's not and have an objective view because you have that context. If you're early on in your career and you don't have that, I think that's a challenge, you know, because you can't rely solely on AI. I think sometimes I cynically will call it like it's driving while intoxicated sometimes for people. You know, it's like they're like, hey, yeah, and I'm like, wait a minute, you know, there's something happening here that's not right, and if you don't know that, right, if you don't know you shouldn't be driving, it can be a problem. So it's you know, humor aside, I I think having you know, the the context, the history, goes a long way I think in be able to do really successful kind of product development within with using AI and AI tools and platforms.

Mike Hyzy

Yeah, on the other side of that too, I see even like using Codex or Anthropic, Claude Code a lot and like reviewing the code and going through it, it's something that, you know, early in my career I would just just do it, right? You know, so we learn how to design and then all right, let's do HTML, CSS and some some Java to to keep it live.

Or I'm doing SQL, you know, development and databases, things like I don't do that much anymore. But then we're using these coding tools and like you work in code so much with Claude though and things start to come back and I think it makes you a little sharper. Those that are paying attention, I think it kind of brings back some of that like coding nostalgia and and helps sharpen you a little bit. So, you know, there there's, you know, there's tradeoffs in in all this too.

And I have so someone in the class who's like, I'm a psychology major and I just build a website in four hours. It's like this like power where you've had this vision in your head. and you know, three years ago you'd have to find an engineer to bring this to life for you. And now you're you're able to at least get something. You know, I think going a little tangent here, I think you still need the human-in-the-loop, right? Reviews and feedback and things like that, but there's kind of a newfound power in a lot of roles that didn't have access or interest in engineering before.

Jim Wicks

Yeah, no definitely, definitely. And I think that can lead to great things and sometimes sometimes not to great things. But I also really liked what you said about, you know, for someone let's say, who has been working for many years, you tend to as you let's say if you elevate within an organization and you know, you're gonna take on new challenges, you inevitably don't continue to do other things that you used to do before, which could be, you know, like you're saying, you know, eval, checking your code, kind of actually building and designing, using CAD, whatever it might be. And then as you move away, you kind of lose touch with that. So, you know, in in that sense, these tools kind of allow you to kind of push you to dip back into it, you know, does actually it's a great way for you to actually stay, you know, engaged and really kind of understand what it takes at a ground level or in kind of detailed level to be producing the products versus over time separating yourself from a a a lot of that which traditionally is has been absolutely required for a designer, an engineer or a developer to actually kind of produce products.

The foundations of good product: Where to speed up, where to slow down

Mike Hyzy

And I think something that you said, I brought up on a call earlier with with our leadership and one kind of line I've been using a lot is death by a thousand demos. I think that's where some of our services come in, because now when to your point, when everyone can build a working prototype, now there's hundreds, thousands of them in organizations and leadership's getting hammered for like, we need to build this out, we need to do this, we need to do that, look at this, look at this, look at this. I think that creates its own challenge, right? Where prioritization and analysis is much more important because you have a sea of all this stuff. So like, how do you decide what to invest in?

Jim Wicks

Yeah, absolutely, and what's most valuable for the consumers and and even how much can someone take at one time. You know, if you look at, you know, like the foundation, you know, of like Bauhaus, right, and this idea of less is more, right? I think there's this less is more thing that happened in the past because you had to be very, you know, diligent and make good judgment about what you're gonna make because it's limited, right? But as you can make more and more, faster and faster, easier and easier, I think there's a real risk that people make too much stuff, add all these features. we can do it, so let's do it, right? And it might it might be white noise for the consumer. It might cloud their understanding of the value proposition, or you know, really diffuse what you're going to message to consumers because you just can make everything, you start making everything, and it's not focused. And I do think that's one of the values in the past of that governor, so to speak of resources and time making you be really, really kind of smart about what you're going to build. And you then had to do really great research to help figure that out, right?

And I think that research right now is one of the things I know with us in the program where we're really kind of, I would say wrestling in a good way with, you know, we can optimize and speed up so much in the front end of the process in terms of, you know, who are we designing for? What do they really need? What do they need when and all that. and there's a lot of the tools that we can use now to to optimize that process on the front end, especially with qualitative and quant research.

But you really need to kind of understand where do you want, you know, to be optimized, right? And where do you want that speed with the tool and where do you really want some kind of diligence and some creativity and some deep kind of thinking through like the nuances of research, you know, and I think that's where you still need the people involved there, I think, personally. At least on the learning side for sure.

Helen Fang

Can you get a bit more specific maybe about where it might be valuable to slow down and to take the time and not to be as efficient as possible? I also saw on your site that you guys had experimented a bit with synthetic users. And kind of being able to access, I don't know, what a twenty five year old teacher with a cat or something like that would would be like as a user persona with AI. Do you have any examples where it might be good to slow down?

Jim Wicks

Well, you know, one of the things I think there's probably a couple areas, you know, I think of. One of the things I think, is often when people look at, let's say, a design process, I think there's two areas that are the most enjoyable and the most challenging.

I think it's the synthesis of data to a value proposition or a definition of a product. And I think that's like super time consuming in the past, especially with qualitative research if you're talking about a new feature or a new product. And there's a lot of tools that you can use to optimize your research findings, whether it's looking at transcripts, interviews, you know, everything. You can kind of distill that with a lot of tools right now, which is really fantastic. But you can also miss the nuances.

And I do think, you know, getting to the why and the what behind what people say, not just what they said. And I think the tools tend to get like what they said and I think this is where ethnography and qualitative research and individual conversations with consumers is really super important. And I think being able to help you kind of take that and revisit and reflect and understand the nuances, I think there's the the the tools that are kind of available now are fantastic. I think that's one spot.

The other one that I kind of sometimes have a little bit of trouble with if someone from a product management perspective or maybe a build perspective will say, we can automate the design process and go so much more faster. You know, and I kind of think of design as bread making in some ways, you know. And I think, you know, good sourdough, good dough needs time to rise, right? And I think a big part of the design process is reflecting as you're designing. You design and you as you're if you're an industrial designer, you sketch to to ideate and understand. You don't sketch just to communicate. That's like that's like the end product. You really use sketching and prototyping to understand the problem more and to get into it and discover things. And I think if you if it's prototyping and sketching and all that is just happening without you truly being emotionally and cognitively engaged in it, I think you can miss out on some really kind of great opportunities in the design process. So I think there's some interesting things there that I think are really great things to be kind of exploring right now, both in education and in in industry right now. Sorry, I kind of rambled on a little bit there.

Mike Hyzy

No, no, that's that's good. And I was thinking, you know, about your response and you know, what I see, my observations from from teams is that, you know, we sometimes we don't spend enough time analyzing the problem and what's the job to be done and those things like that. One of my favorite Einstein quotes is if I had an hour to solve a problem, I'd spend, you know, fifty-five minutes understanding and analyzing the problem and five minutes on the solution. And right now when we have the ability to write a sentence into Claude design and it make a whole, you know, thing for us, like it's so easy to to run towards the solution and we're not, you know, we could be using the these tools to to understand the problem better. You know, deep research, other things, but I think you mentioned ethnography earlier, going out into the environments and observing. Like there are things that AI still is not going to be able to do for us. And we need that that, you know, to exercise our our our critical thinking skills as as humans. And, you know, from a consulting standpoint, it's like, why is all this time up there too? And it's like, well, like if you want a high end elite solution for this, the more time that we spend understanding and researching, the better the end product's gonna be.

Jim Wicks

Yeah. And I think for industry I think you're absolutely right. And I think when I think of, you know, ethnography, for example, even in the past, companies always struggled to invest in that because it take it's so labor intensive. Takes a lot of time, a lot of money, and what comes out of it in the end you sometimes think is kind of like a “duh” type of insight, you know. But it's what's behind that insight that really, you know, that the how you got to it that's really kind of im important, I think, in that. And I think companies will I think undervalue that front end part that you just talked about, Mike, you know.

And and if you want to condense your budget on a project, that's the part that tends to get squeezed, you know. and so being able to optimize that without no visible tradeoffs from someone from a financial perspective probably seems like a really great idea, but I think if you're in the process you maybe see what's missing and what you're kind of leaving on the table when you're optimizing for speed on the front end. I think there's a risk there.

Helen Fang

Yeah, and I think to that point everyone has the access to the same types of tools now, right? So if you're going through the same process with the AI tools and similar right, like you will also get very similar products and results to everyone else. So you have to really think about kind of like what do you bring to the table that's different? What parts of the process are important to you? Where can you add in value? What really matters.

And I think a lot of what you said also about on the process of sketching, right? It's also a good parallel to, you know, using AI in writing, for example. Like you can generate a lot of AI writing now, but a lot of the value in writing is kind of the process and working through does what I'm saying make sense, does it really matter? Who am I really writing it for? You can generate a lot of AI written things now that sound nice but don't really say or mean anything to anyone. Because you haven't put in the time or the thought behind it. It just looks pretty.

Jim Wicks

Yeah. Certainly. Certainly. Yeah.

Mike Hyzy

I don't know if we're getting to this, but you what I for on the research part, what I love AI for is sentiment analysis. So I can pull up a client and I'll say, hey, look at client X, look at all their reviews. Go on social media, go on Google, go on Glassdoor, get their employer reviews, get their user reviews, get all this, and build a sentiment analysis about that. Or go into their leadership team. What conferences have they spoke at? Like what opinions do they hold about these things? What's their process? And just the amount of research that I could pull doing a deep research, right? So you're looking at 25, 30 minutes and then just deep analytical cited reports about what consumers are saying. To your point earlier, right? You know, it's like the the Ford analogy, right? If I asked what they wanted, they want faster horses. And I know you can't take it at a hundred percent, but it still gives some perspective of like what are the issues and problems that they're dealing with. And I think that's really helpful to start framing the foundation of of the problems that we're trying to solve.

Jim Wicks

Yeah, and I think absolutely. And I think that works really, really well in that type of a scenario or B2B instances and business. I think when we get into like consumer products, you know, how do we kind of weave that into the process, you know? You know, I think we talk a lot about hyper personalization and what we can get with AI and and I think absolutely in theory it makes sense. I go back to when we were doing this Moto X phone with Google and we created Moto Maker and the idea is you could design your own phone, your memory, your finishes, all that stuff. And there's definitely like that's fine, but there's way too many choices for people, right? So how we dial in this sense of personalization, you know in a way that is maybe sometimes explicit and sometimes implicit, you know. You don't know as a consumer how personalized everything has gotten for you because you're maybe not in control of that personalization and there's other things that you explicitly, you know, make choices in. So really kind of really interesting space when there's that much data, that much content, and then these tools to process it for you. You know, how do how do we use it?

Mike Hyzy

And that's one of the things that we're using in the the project that we're working on, right? When the user comes in there, we're pulling that that data because if if we had hyperpersonalized it, I think we'd have to ask them like eighty-five questions before they got onto the platform, take some of that data and especially as they use the platform, right? Decisions that they make, choices that they make, we're holding on to that and then making those kind of implicit you know, design decisions or personalization decisions based on action.

CGI and Northwestern project

Helen Fang

Can you explain for the listeners maybe what project you're working on together, Jim or Mike?

Mike Hyzy

That's a great one. Maybe we should maybe we should let the let the audience know.

So I'll give a quick background. I wrote a book on gamification a couple years ago and I was on the book tour and saw one of our clients there and she came up to me and she asked, she said, you know, game mechanics and AI and hyper-personalization in games is great, but I'm I'm struggling with adoption. I have half the people that have AI licenses who won't use them because they think it's gonna take their job, and the other half are running wild, putting information that they shouldn't in there and and it's a mess.

So that started a separate research project, for us, is not just game mechanics, but how can we use behavioral economics, game mechanics, and like habit psychology to not just train people on how to use AI, but to transform and make sure that they're they're they're using AI to be helpful in their jobs and they're getting the most out of it. So we went through probably a year of doing research, you know, testing it internally, we had a couple good client zero stories, us, a couple of our clients, we're doing it manually. It's basically a process. We're taking behavioral science on top of OCM.

Organizational change management doesn't implicitly take, there's like about 90 scientific ways that you could change human behavior, and it they don't directly use that. So we layered in some behavioral science on top of the OCM in a process that we've used to drive adoption. So using tools like Copilot, ChatGPT, Claude, things like that, and where we're like, well, we want to scale.

And and scaling it would was starting to get expensive on our clients' part because there's a lot of humans that needed to be involved to scale this. So the idea was, you know, what if we could automate this? And Jim and I actually met through, a client introduced us and we hung out and came to an event and we were talking about this and we decided to go in as a sponsor for the program.

Jim Wicks

Thanks, Mike. Yeah, so the program, one of the kind of cornerstones of our program is a capstone project. And our capstone projects are three-quarters, so they last the whole year. So you're able to go from zero to one and really get into depth from the very beginning, you know, understanding the problem as Mike said, and then get to a definition of that and who it's for, and then start to iterate and through all this is informed by iterative research, through a design solution and at the end start to work through the business modeling of this. In fact, obviously it's it's always around feasibility, viability, and desirability.

So connected with Mike, had some coffee, started talking with the capstone. Mike also spoke at the program here a couple times with the students, and just came about this idea of like, okay, is there a really you know leveraging the A3F framework and what Mike had talked about?

Maybe there's a really cool project here for the students to consider how you can design this solution, these tools, to be used by the individuals within these companies. To actually be motivated, be rewarded, and feel like they are given the time to actually kind of develop their skills in a meaningful way that can also kind of help progress their careers within their organization. because I think what we found is many people are like, I don't have the time, or I'm not motivated, or if I am motivated, is my performance review going to reflect it? There's so many big things around it, it's really cool.

But so you know, a team of five students that that took that on and it's been really fantastic. They're learning a lot, you know, there's challenges along the way with something new like this, right? And when you look at kind of adoption within, it's really fantastic to see and I'm very grateful, yeah really enjoying it.

It’s that kind of work is you know a cornerstone, you know, of you know what we do here in the program. One of the reasons we really like it also is that you have a team and they're working together and they're working with a sponsor. They also have a mentor on the side and I will say I think one of the things that I always worry about with not always but I become concerned about with AI tools is sometimes it gets so automated we lose the soul of product development, and I don't know, I think whether you're a designer, an engineer, you came in here to to do these things and design and development, to develop products and problem solve because you love that. But in the end, part of that, a big  part of that is the people that you do it with, right? And the community. I I kind of say like three C's, community, culture, and chemistry. I mean, there's just something really cool that happens in that when you're making something together, right? And for us to be able to do that with CGI and the students to kind of do that within the capstone I think is I'm hoping that it just even though we're using these tools and these advanced tools and trying them out and testing and and trying new things that we we don't lose that that chemistry, that community, that culture.

But honestly I think the lifeblood, the real protective mode to really innovative companies are those things and I really don't want that to disappear for people in the workforce as we go forward because I think it's just one of the best things about working.

How organizations and skillsets are changing and what remains critical

Helen Fang

And I think to that point, do you see anything I mean, you guys have both seen a lot of new technologies and lots of major changes in your careers, right? Like do you see anything changing about how organizations are looking at teams and how people interact and maybe kind of the skillsets, how you designed that part of it? Maybe are you seeing anything now that's already changing and are you seeing anything that's coming?

Mike Hyzy

I I think there's a lag. I think that now the the tools are shifting the paradigm. When product managers can become designers and designers can become engineers and all the roles are shifting and blending. I think that there's going to be a natural kind of reorganization of what does a good product team you know, look like and and that's still progressing. What I think lags behind that is the organizational design. And it's gonna be different for different companies. You know, there are large corporations that are multi-matrixed organizations and, you know, who reports up to what, what kind of structure that they have. There's not gonna be a cookie-cookie approach to that and how you design the organization. I think it's gonna be by the by the industry and and by the types of divisions and in what area. You know, like, you know, pharmaceutical R&D is going to have a much different org design than you know, manufacturing computer chips in Arizona, they're gonna be different spreads and and the roles are gonna change. I think AI is gonna help us with that. But that also comes back to the top is that it's really important that these roles and these these different formations are gonna come is because what's your AI strategy? Yeah, the technology has changed a lot over the last ten years. And Jim, I'd love your opinion on this too, but I think what what really hasn't changed that much is really good corporate strategy.

You know, what's your vision, what's your aspiration that you're going for as a company? What are the biggest bottlenecks that are in your way? What trade-offs are you gonna make to break those bottlenecks? And what's the focus and what's the role? And you see good companies, they're both forward thinking, forward looking, and, you know, the ability to to be flexible and and adapt. So I think corporate strategy is still a component, the organizational design and those roles are gonna continue to shift, I think kind of just naturally as things progress.

Jim Wicks

Yeah, I think I agree with that. I think we always hear it from students and we hear it in industry, people want to see where do they plug in. Where does what they do really relate to what we're doing as a business? And it has to start with clarity around what we're doing as a business and what's important for us and then this is how we kind of plug in. And I think that's a real kind of human kind of need for people. so that clarity around strategy, that kind of forward-thinking around strategy,

I think is really important. But I do think the thing that's really gonna make those organizations sustain is the organizational design and the culture and how they work as organizations. Because I think a a refrain I often hear with students, whether there are part-time students that have been working for, you know, fifteen years or students that are very early in a career is like things like mentorship, right?

Like how you kind of work as a team, diversity of thought, all these things that are a big part of what make kind of organizations kind of different and unique and valuable and and then they tend to produce really cool stuff, whether it is something that's like in the pharma space or chips or consumer products.

And I just I just think that's a a really important thing for us to as you said Mike, I think it's lagging right now. I think the key is for a lot of the companies is how do we get in front of that, you know,  in my mind to bring back the soul. I think the soul is missing. I think it's losing its way with everybody like not spending as much time together and, you know, a lot of the automation. I think that's something that it can be a really great goal for a lot of companies because I think you know, employees and, you know, people team members will kind of you know, respond to it.

But I do love what you said, Mike, is I do think roles are changing, right? You know, the traditional, okay, there's a PM and there's all these other entities and you're bringing everybody together as the kind of CEO of the product and you know, you can, those can now be you and agents, right? You know, doing this. And roles are melding, like I I don't know, I I talked to certain people and it's like, well, we can automate the build part, but we can't automate as well that front end area where there's nuances. So there's people in design that I talk to, they're like, what's the future of design? Woe is me or this.

And then I'm like, the future is bright. I mean if you're in certain industries, I mean, you have the ability, assuming you're a broad thinking designer, not just a form giver. I don't mean that just a form giver, but just you broaden your design beyond form giving or you know screen design that you're able to be involved in that front end and synthesize this information and understand the nuances and very importantly communicate it both visually and orally and written. I mean, a lot of times people who spent their life building, that's not their strength, right? And so it's hard to automate that front end. So there's a great opportunity for people that work really, really well on that front end to start to carry through into the building of products. And I think that can be a really, really cool thing to start happening.

Mike Hyzy

And then Jim, what are the tools that you could use to do that? Cause I love where you're going with this. So as a as a broad thinking designer, we talked about, you know, synthetic data, running scenarios. You know, we worked on a project for a major airline. I think we did a billion different scenarios. You know, how is that front end? What are the second and third order effects? What is this gonna look like in three to five years? I think there's all these things that used to take a lot of time before that we could plug into that process and we could run all these A through Z tests, right? And like what scenarios back so by the time that we build it, we have a much more validated, educated, intellectual kind of front end that that is adapts and adopts like it's supposed to. What are your thoughts there?

Jim Wicks

Yeah, and as you said, I think it really depends on the business, right? In the industry. If you're developing a product or a feature or something in financial services, or if you're doing consumer product, I mean you will develop a lot of, you'll have a lot of kind of data on that front end to use, but I do think the ability to kind of discuss and reflect and process that. It is around design thinking. I kinda hate the word design thinking or the words design thinking because it's overly done, same as innovation, you know, but but I I do think being able to…this is where honestly, teamwork, I say also being able to work with divergent opinions. Like someone doesn't agree with me, so instead of ignoring it and walking away, I'm gonna okay let me understand that. What do you mean? Because when you run all those scenarios, whether they're kind of driven through data and prompt and and everything, or if they're findings that are coming from primary research on something that's smaller, those things all need to be discussed, I think, and reflected upon because I just don't think the front end is a math problem.

problem, you know, I think it's a social problem often. And, you know, how we kind of work through that as a team early on and then when we have the real essence of it, start to build it, I I think it's really important that those teams that are building can carry those nuances that they understand on the front end through the development process, which we're often it it disappears sometimes when you're building. I don't know that it's in it. It isn't an answer. It's just a kind of a kind of a a thought response to your question.

Looking back to look forward

Helen Fang

Yeah, and how does it I guess, right? it's interesting to think about, because a lot of AI tends to validate, there have been research studies on this, validate where you're thinking agree with you more often than people would agree with, right? So when you're building things a little bit more in isolation or doing more steps by yourself, you maybe are used to not getting so much negative feedback or always getting validated versus there is a lot of opportunity now that people can do more things on their own or build products more quickly. So you have both the opportunity and this this risk of losing, I guess, the soul part of it.

Have you seen anything that's worked really well? Or if you think back, for example, to some of the first products you built and worked on, was there anything there that you wouldn't change at all? Or is there anything that you would do differently now that you have access to AI?

Jim Wicks

Well, first of all, anything that we I did in the past I would do differently. I would have done differently a month after I did it too, because you learn. There's tons of those. Yeah, that's a long, long list. yeah, I don't know. I mean what I really, really love when I like I I always think of like hardware, software and services. And I tend to be biased towards consumer, but when I look B2B, everything. I think, well, I like to talk about consumer right?

Helen

I think you can start with consumer. Because now I feel like actually with AI and with everything, like B2B starts to feel more like B2C right? Like all of these things are kind of blending together in a way.

Jim Wicks

Yeah, yeah. Yeah, there's consumers inside the B to B, right? So yeah. Yeah. yeah, I don't know. I mean I think there is something really valuable to be able to kind of produce something very quickly. Right? I I liked, I mean, in the hardware industry it took forever, right? It takes your tooling and and all that. And that's an industry that's lay li you know, kind of lagging software. But when you have hardware and software that's embedded, I mean that's like mobile phones, right? You're kind of you know, or or compute anything, right? Everything these days has kind of an embedded, you know, software. I think what I would say back in the day, we have this vision for multimodal interfaces and interconnected products and all this intelligence living across this hardware and you're struggling with Wi-Fi and Bluetooth and these kind of things and interoperability, which you know seems ridiculous, you know, at this time point, but the the idea I think that AI becoming embedded in hardware and being localized and not having to be connected to the cloud and and this connectivity of all these devices, I think that, you know, that wow, you know, if you could start to have these things working the way that you dreamed them to work, you know, fifteen years ago, which you can now, that would have opened up so many doors and it opens up a lot of doors now. And I'm really excited what happens with more kind of embedded AI into products over the next, you know, three to five years. I think there's gonna be a lot happening on that edge. So I don't know if that answers the question.

Helen Fang

And but to that point, do all do products do all products really need AI embedded in everything? I think there's, I'm not really sure how to phrase this question, but I think with any new technology and especially with AI now, right? Like people are trying to find ways to include AI in every single aspect of everything. I guess that overengineering is not really new, but it feels like it's been taking been taken to the extreme now.

Jim Wicks

Yeah. No, you're right, you're right. If you're like thinking, okay, feature, let's get the AI in rather than what's the problem. Like as Mike said before, if the problem is what you're focused on, it might be that this device needs to respond absolutely to the fact that you need me time, you need alone time, you need, you know, I need my individual Luddite time or I need, you know, to be hyper connected. I I think that's where personalization can be really cool, maybe, you know.

But yeah, right now it does feel like AI this, AI that, AI that, and does it really need to be in everything is a good question. I think it goes to that less is more thing we were talking at the beginning, you know. and yeah, I don't I don't know if there's anything without AI anymore. It's like electricity.

Mike Hyzy

Yeah. Well I mean I think you know there’s the AI part in the two different areas of product development. Are you using AI to help build the product or are you building a product with AI? And I think it's gonna fall in one of those regardless.

I think back I had a startup in 2015, and I had an investment and I had about nine months of runway. And I did the research. I validated. I was going around places with Excel sheets. I was got a designer, right? And we designed it and we we tested it and all there, and then started engineering it and ran out of money and didn't get the money for for the engineering to build it out. And I was thinking about that now. I'm like, this is , what 11 years ago? And I'm like, if I had the Claude suite right now, I would have built this entire product. I mean, still do the research part, but I would have designed and built it in like four weeks, and it would have been a four-month project instead of running on a runway at nine months. So I think that you know when you talk about going back and doing things differently, like there's like that that is the one point that I think about of all Also it would have been completely different. I could have got it. You know, I had investors that are like well we’re willing to give you the next round, but we need a hundred paid users before we write you that check. And it was getting the engineering together to to get it functional to get those first hundred users on.

And this now, I mean I could build something today during lunch and then go out this afternoon and start trying to get people on it to test it and and use it and try to get to that hundred user mark. So it's just insane the speed as which we can operate now.

Jim Wicks

And it really sets up well for entrepreneurship, right? You know, to be able to, and that's gonna be a big change. And you see so many young people with the job situations right now are really kind of starting to really kinda say, hey, I'm gonna take a look at this.

I think the timing's really interesting between AI and this ability to develop, you know, in software, particularly your own kind of stuff. I mean, and technically they can do it, and the question is do they have the rest of it, right? That's why whatever you're doing I think education needs to change, right? I mean you know, what's the value of an engineering degree and ability to build all this stuff if you don't have enough other stuff around it for you to make good choices, you know? So, I mean the value of the engineering degree is a massive. I'm not I'm not marginalizing that at all. I think that's really, you know, quite you know that's gonna change stuff a lot.

And this is when things happen in society that kinda happen at the same time, technology happens in different ways that I think we're gonna see this big, big burst of young people building their own things, doing their own things and see where it goes because they're frustrated with the job situation and you never know what can happen.

Mike Hyzy

You know, the data shows. Look at how many companies were created in the 2008 recession. There were no jobs. I think it was like Uber, Instagram, Shopify, maybe. I you look at the list, but it was all these companies, these startups were created because there weren't weren't any jobs in the market. So that that unemployment drove, you know, creativity and the ability for people to figure that out themselves.

Jim Wicks

Yeah, yeah, yeah. And just kind of bridging from that back to what you saw on building the product. one of our board members, advisory board members, Mitul Patel, you know, I was asking like how is stuff happening? Because they do product engineering, engine hardware engineering, hardware, electrical. And he's like, you know, that that lags compared to software development, but everything that you said, like around it, all the tools around the actual physical creation is is being revolutionized through kind of AI, whether it's you know, testing data, how you test, how you kind of synthesize and the test results, how you analyze it. you know, just in in some ways maybe it will allow people, engineers, designers that really their love is to focus on the building to be able to actually build, spend much more time on the creation piece and not on so much of the, you know, the test. I'm not an engineer, but I don't know that most engineers love the test phase as much as, you know, as they do others. But you know, some do, but now you can design it. And I love the fact also that you can design agents to be adversarial and like maybe different types of super like critical agents on testing certain things and others that are not. I don't know. You know, maybe there's there's some cool ways to emulate the type of people you want to work with on a team and yeah, so I don't know, really it's gonna be interesting to see what happens in the next three, five years for sure.

Helen Fang

On that point, I was at a talk yesterday actually, a movie premiere from this famous photographer Chris Burkhard. He does a lot of nature photography and things and he did a documentary on biking across a glacier in Iceland. I promise this is related to our topic. And he said often he looks back in history actually for inspiration instead of always looking forward to the future. So like in his example, there was a group back in the nineties that put some nails into their bike tires to make these super wide bike tires and like cross this glacier and now they have all this modern tech and all these modern things, but to keep kind of that soul and that spirit of what's happening in the past and connecting it to here. Is there anything that you guys really lean on currently, from your past as you try and take in all the AI changes and look to the future?

Mike Hyzy

Well, Jim, you know my views on this. So if I am trying to solve for a pain point right now, by the time I do the research, build the product, launch the product, do the campaign, get people to use it, we're still looking at like six, eight months to to really kind of scale something. And as fast as things are moving right now, that pain point's gonna be much different. The environment's gonna be different. There are a lot of things that are gonna be different.

So I think like my anchor in the ground is no matter what I'm working on, I'm always, you know, doing some scenario planning, doing horizon scanning, keeping up to date on on signals of change and drivers of change, drivers of change that are change that we know right now that's happening. The signals are the small things.

There are some some really great frameworks out there, Institute for the Future, Copenhagen Institute for Future Studies. Jim, I think you teach some some in your class and in in in your program. I think it's things like that, you know, there there's a futures technique of looking back to look forward.

So analyzing, you know, these things happen every hundred years, they happen every you know, and there's how things shift and there's there's a ton of frameworks that I think are super, super helpful.

Jim Wicks

Yeah, I like what you were talking about, the signals, right? The weaker ones, the ones that are weak but they're emerging, right? And it's like the way trends have always been in the past. Some of them are are fads and they blow out, right? And then others like really explode, right? I think they've always said society and you know, doesn't change as fast as technology. And I think technology is changing really, really fast right now, but I think society is changing a bit too. And maybe the social part of product development and how we develop products is changing.

I think there is this kind of whole I love that idea of looking at these really distinctive scenarios, things that kind of create kind of seismic changes. But I do believe and I just think for people that are involved with it and I tend to think about a lot about young people because I think they're being so disadvantaged right now, in the workplace.

They don't you know, I think a lot of things we talked about are hard to make judgment and decisions with context if you don't have that lived experience. So I just feel it's unfortunate for so many younger people. I like what you're doing at the Chicago Futures Salon because it's community. And I think there's just an emotional connection to problem solving and developing products and services of people that are doing it, not just with who their target is and who they're doing it for, but as a team and as an organization that I just I that is to me one of the most enjoyable loving things to be part of if you're in a business is to be able to do that. And I don't want that to be missed for this younger wave of product developers and professionals that are doing that.

And so whatever we do, I want that to have a one of the heavy dose of that to be present because I feel like everybody I talk to that's my age or twenty years younger, it's so much of what informed them were those things, you know? And I just don't want that to be lost in the equation going forward, so for organizations that are trying to build community outside of their even corporate community so that there's a kind of a sense of kind of engagement, belonging and I don't know, love for what you're doing, rather than just like, I'm creating shit, you know, or I'm doing this because I can make stuff so quickly. I think there's a human part to it that we don't wanna have, you know, lost in in the process.

Mike Hyzy

Well thank you. And I appreciate it too just being able to spend time at Northwestern and it's such a great program and the level of talent there is just incredible. And I agree, it's it's the community that really drives innovation. And I was you know, luckier when I was coming up through product development, there was no work from home. You know, I was forced to be in the office every day, and that was just a different environment.

And some things I got, like hallway things, and walk like, hey Mike, come here, you know, see this. Or, you know, there was an engineer that I would bother the hell out of. I learned SQL by bothering this guy every day. I think he might have even bought me a book on it, so I stopped talking to him. But you know, we had those interactions, you know, that it it was it it was really a sense of community. And then after work, you know, we'd go out with each other and and talk about this stuff and we'd get mentors for people that we ran into and I think community is such a big part of this. So, you know, that's that keeps my hope alive that, you know, AI is not gonna replace a lot of things because a lot of the creativity will continue to come from, you know, human involvement and getting together and and stuff like that.

Closing thoughts and takeaways

Helen Fang  

Yeah. And we're coming up a bit on time. So I think what I would ask as the last question would be, although maybe you've already answered it, kind of what do you hope like what's one action or one takeaway you hope people have from today? Or is there anything that you're really excited about looking forward? I guess community is maybe the obvious answer from from what you guys have just said. But any last words?

Jim Wicks

For me, honestly, mine's might be somewhat tactical. Yeah, I did the soul, the community, that's like really like to me is the biggest thing on a human scale. But for me, I'm really becoming much more focused, I think for our program like how do we look at hardware development, not just software development. And we're do we do both here, software and services and hardware and how we look at the program. But I really think because of its lagging, I just a little bit because of the challenges of of you know getting the parametric data with you know prompt-based stuff and the control you lose with it. I think I I'm really interested to really kind of continue to dig deep into hardware software combinations and how AI can play a role in that and you know, how we might want to kind of design and do research. Yeah.

Mike Hyzy

I think, yeah, to kind of build off of what we've been working on, it's an interesting story because we're doing really good at adoption. We're getting a lot of people to use it. And then at South by Southwest this year, there was like the head of neuroscience from Berkeley that came in and said, do you know that, you know, people are cognitively replacing critical thinking skills with AI? And like all these other things. I'm like, you know, I'm driving everyone to use AI and they're weakening their critical thinking skills. This is a problem.

MIT came out with a paper, Berkeley came out with a paper this year showing the effects of you know cognitive enhancement versus cognitive offloading. And to me there's a there's now like a newfound responsibility is like we need to get people to adopt AI but in the right way to be able to use AI as a cognitive enhancement, not a cognitive replacement.

How do we continue to design about things that that matter and and raise the ceiling on that?

Jim Wicks

Yeah, no absolutely. Awesome.

Helen Fang

Then thanks so much for your time today. And also thank you guys for working together and Mike for helping us invite Jim on. And I also, Jim, maybe I'll cut this out of the podcast in the end, but I wanted to thank you also because after the prep call, and I was thinking about what you said about community and about interacting with people and what we wanted to keep, you know, human.

So for example, when I was thinking about what types of questions to ask or how to prep it, I pulled a coworker that I knew had was working in product and stuff, and we had like a really deep conversation about kind of what is the future of product and how are you seeing it and some of the points you made. He said, Well, isn't it expanding people's horizons to be able to use AI more? So that like I think the work that you've done before and also how you're thinking about it really also helped me connect back to some other people that I hadn't talked to in a while and don't necessarily work with in my day-to-day job.

And it's also the one thing that my family, for example, understood. I was like, remember the Razr that we had? I'll text it to you after. My mom still kept some of our old Razrs, but it was my first phone that I had growing up. My mom has kept all of the old ones. And I think kind of that joy from product, like also with the coworkers when I mentioned, everyone was so happy to talk about the Razr and kind of their lives back then and how they were like when we were teenagers and things like that. And I learned a lot more about them as people and like had different conversations than I might have normally had. So it it's meant a lot to me personally and and I hope that a lot of people will get to listen to this too.

Jim Wicks

That's nice to hear. It's really awesome when products are signposts or markers for times you had in your life. So that's really a good way to think about it. I I love that. Well, thank you so much for inviting me. I enjoyed the conversation. And Mike, I'll see you probably on Friday, but Helen, thank you so much. All right, take care.

Helen Fang

Yeah. Thank you guys for your time.

Mike Hyzy

Yeah. Thanks everyone. Have a good one. Bye.

Jim Wicks

Bye.

Helen Fang

Thanks all for listening, and please like, rate, and subscribe to the From AI to ROI podcast. I also wanted to thank my colleagues Raphael Weber and Sonja Bachmann who contributed their ideas and feedback when I was putting together this episode.