The Evolving Leader

Harness the Power of Open Innovation with Steve Rader

• Steve Rader • Season 7 • Episode 4

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Steve Rader is a leader in the thinking behind open innovation, open talent, and the future of work. In this episode of The Evolving Leader podcast, Steve joins Evolving Leader cohosts Jean Gomes and Scott Allender to share how leaders can learn from his important work and use their learnings to solve complex problems. Steve tells us that open innovation and open talent are key strategies for organisations to stay competitive in a rapidly changing world.

References from this episode:
"Co-Intelligence: Living and Working with AI" by Ethan Mollick (April 2024)
RapidMiner (https://altair.com/altair-rapidminer)

Other reading from Jean Gomes and Scott Allender:
Leading In A Non-Linear World (J Gomes, 2023)
The Enneagram of Emotional Intelligence (S Allender, 2023)

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The Evolving Leader is researched, written and presented by Jean Gomes and Scott Allender with production by Phil Kerby. It is an Outside production.

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Jean Gomes:

How will your organization continue to keep up with the rising pace of innovation and disruption being driven by exponential technologies and the 50 million startups that appear every year with little or no barriers to entry? Many leaders still believe that their new product development and partnership models will sustain their long term growth despite the growing evidence to the contrary. In this show, we talk to a pioneer in open innovation who has built the strategic capability of harnessing external resources, crowds and communities to accelerate innovation and growth in one of the world's most challenging systems, space exploration. Steve radar highlights that 90% of the scientists who have ever lived live today. Think about that for a moment and its implications. If your organization is struggling to own its future, this is an important Show to tune into. The

Scott Allender:

Hi friends, welcome to the evolving leader the show born from the belief that we need deeper, more accountable and more human leadership to confront the world's biggest challenges. I'm Scott Allender.

Jean Gomes:

And I'm Jean Gomes.

Scott Allender:

How are you feeling on this Friday? Mr. Gomes,

Jean Gomes:

I'm feeling pretty good. The weather is amazing. I've got a great weekend lined up of rest and renewal and time friends and family, and so that's good, and I've got a couple of really interesting books I'm reading. I've just spent a couple of hours talking to a pretty remarkable guy about AI and some of the things that are happening there in his work. So that's left me both slightly bewildered but excited. So yeah, I'm pretty good. How are you feeling, Scott?

Scott Allender:

I'm feeling rested and curious today. I'm feeling a bit playful, and I'm feeling thrilled to have Steve Rader on the show today. A few months ago, you and I heard Steve talk about his work, and it was incredible. And I know we were both having thoughts bouncing around in our heads for days and days following that talk. And so Steve is a thought leader in the open innovation space, working to infuse both challenge and crowdsource innovation approaches in the teams that he works with he is passionate about the power of open collaboration, specifically as it applies to leveraging communities and adjacent industries to drive growth within a more traditional organization. We're delighted he's here. Steve, welcome to the evolving leader.

Steve Rader:

Oh, thanks for having me on.

Jean Gomes:

Steve, great to see you again. Welcome to the show. How are you feeling today?

Steve Rader:

Feeling really good, very optimistic. There's a lot of good stuff going on, a lot of bizarre things going on, but I think, generally, really good. Yeah.

Scott Allender:

So Steve, you've spent a big, big portion of your career working in the innovation space. It's a word that I hear used a lot, but I don't, not sure it's universally understood in the same way. So let's say we're at a dinner party with a group of people who know nothing about innovation. How do you answer when your guest asks, Steve, what do you do?

Steve Rader:

Yeah, I get that a lot, and it's, it's a, I'm not sure I give very satisfying answers it. You know, innovation is taking the best tools and capabilities that we have in solving hard problems and by solving problems, that includes just making things better, right? So it's not just the problems that you're like, oh my gosh, I've got to solve this. It's getting from where we are to where we can be. A lot of my time has been spent at NASA in the space industry. And, you know, we can get into space, but we need to go farther, right? So it's, how do you make things even lighter and more reliable and things like that. So I think in the world we have we're not going to run out of any problems anytime soon, but a lot of that is, how do we do things safer and better? And the innovation comes when you take these new technologies or tools or new ideas or new approaches and and make those work for you, to make those problems go away, or make those make the progress on those, those issues and those things that that we really need to do.

Jean Gomes:

And in this show, we want to focus on something where you've been at the vanguard of innovation and a particular type of innovation. And we would really love to just start. Getting a deeper understanding of what this term open innovation means, because you did start it at NASA, and you got to shepherd the organization through its adoption and do some pretty remarkable things here, but we stand back and understand what it is. Yeah,

Steve Rader:

yeah. I was introduced to open innovation back in 2010 when I read Jeff Howe's book on it, which is really just coming into being in the early 2000s and open innovation is is crowdsourcing and that. You see it around in lots of different forms. You see it in crowdfunding, with, you know, Kickstarter and Indiegogo. You see it in sites like Wikipedia, where people in the public can come and contribute and be part of that community that has now created the, you know, the largest encyclopedia in the world, right? We don't even have any competitors to that anymore, but it's specifically in the innovation space. Is where we use contests largely to reach out to many, many people to try to find solutions to problems. So we'll actually post what we call a challenge out there, and often we'll offer a cash reward. But there's actually for crowdsourcing. There's four kinds of incentives. People have gold Guts, glory and good. We call them the four G's, right? And people will actually participate if they think they're doing good for the world, or they some people just really like a hard problem, and other people are trying to build a reputation that they can then use to go make money somewhere else. And then some people just interested in the prize. And there's a lot of what we call curated communities out there now, which has made it really interesting. The internet really enabled platforms to be built that could gather people around. So, you know, Wikipedia is a great example of that, but there are over 700 different platforms out there where they've stood up a website, they've started inviting people in, and often they'll invite them around community, connecting with other people that have that passion. So TopCoder is this 1.8 million software developers and data scientists, and they they join that community to connect with other people who have that passion and to learn. And then these challenges get layered on top of that as a way to learn even more, start to build a reputation, start to have an impact. And so there's this kind of lattice work of these communities that do different kinds of problem solving. GrabCAD, for instance, is run by Stratasys out of Estonian, and it's, I think they're up to 11 or 12 million mechanical engineers and designers on a single platform, and they're there to trade CAD models and 3d models, but they will run contests, and they'll run challenges to help that community kind of try out their skills against each other and see see what they can come up with. And we get amazing work out of them. It's amazing. Some of the designs that you get on we actually had when NASA did, when they had a Nobel Prize winning scientist that ran a contest on there for $10,000 to build these starshades, to kind of find planets, ended up with 50 or 60 amazing designs, not simple designs, very complex designs. And I think from the government side. It's a great way to involve the public. But what it really comes down to is we live in a world that is really, really complicated, and we're evolving to a pace of change that is really hard to keep up with, and I call it the tsunami of technology that has resulted in my talks, I talk about, since the population's done this, the number of scientists and engineers, especially now that their countries are more wealthy and there's a lot more education out there, the number of scientists has just grown exponentially, kind of with the population, to the point that right now, 90% of all scientists that have ever lived are estimated to be alive, right? Everyone who created the science and technology that we all take for granted, you know, starting with like Socrates and working your way up to Einstein and the folks that lived in this even last century, there's nine times as many people as all of them put together, all working with new technologies like blockchain and advanced software, APIs and machine learning and 3d printing and nano material and and all of. These are working across multiple industries simultaneously, and so if you're in an industry and you're trying to solve problems and innovate, you have this kind of unlimited buffet of new technologies. The problem is you can't find it like not posted on the internet, like people think you can just google anything, no new technologies, especially solutions that grow and mature in one industry, they remain kind of hidden because they are proprietary, or they have jargon around the the problem they're solving. You know, if you go to John Deere right now, they're one of the world leaders in automation and machine learning kind of weird farm equipment, but they do. But if you were to see some of their most advanced work, you might go, Well, that's that's not really applicable to over here in say, aerospace, but someone who understands their problems and their nomenclatures and what they're doing, and they understand what you're trying to do over nearest they can actually connect the dots and say, no, no, this new thing they're doing, if we just do these mods to it, can be our new solution that can give us a 5x 10x improvement in what we're doing. And this is where open innovation comes in, because if you're in your industry, you can't see that you can't understand those problems, those solutions. You don't know it's out there. You see these things like blockchain and nanomaterial, but you don't know that it's actually getting combined with other solutions. And so crowdsourcing and open innovation is this way that you can kind of cast a statistical dragnet across all the industries and all that world. And this is where it kind of warps, warps the mind a little bit, because humans, we aren't great at large numbers, right? But some of the communities you know are millions of people, and we don't reach millions of people when we run a campaign, but a fraction of many, many people is still many people. And so I actually use stadiums, pictures of stadiums when I do talks, because they say, you know, Texas, A and M stadium happens in Texas, happens to be a good one to use, because its capacity is like 102,000 people. And I said, here's a picture of me at a game looking at 100,000 people. And whenever you go to those events, it kind of blows your mind a little bit like if you're if you're nerdy like me, you look around and you think, how many doctors Am I looking at? How many, how many top scientists? Am I looking at how many, you know, innovators, am I? And the thing is, anytime you got 100,000 people, there, statistically, are a little bit of everyone. But when you start to look at these crowds, there are multiples of that. You know, 11 million people in GrabCAD. That's 110 of those stadiums like that's that's some significant numbers. And so when you start to think about that statistics, someone that can connect the dots goes way up, compared to me trying to sit in my cubicle with my team. Even if I have some of the world's experts, they're never going to be able to connect the dots to those other things. And this is where open innovation becomes. This, in my mind, an indispensable tool these days. It's not even an option anymore. You have to find the time to go out to the crowd sometime, if you're just doing everything internal, you probably are going to fall behind. So I'm

Scott Allender:

thinking about leaders listening right now, who are thinking, Well, I'm not gonna be able to run a contest in my organization, right? So how can I leverage the crowd, and really do it in a way that is truly innovative and builds on solving the problem versus what I see a lot of which is, let me look externally, find what I call a quote, unquote, best practice, and try to overlay that onto my problem, and then it doesn't work, right,

Steve Rader:

right? Here's the thing, it's a super effective tool. It does take some learning and some investment to go out and find it. A lot of people find it's really hard to get over the idea that your experts can somehow have people that aren't the experts help, right? That there's ego there. There's a little bit. And so one of the first things is maybe do an internal crowd right, where you actually can get a platform where you say, hey, look, we've got several 1000 people in our company. Let's at least go outside our individual silos to make sure that we're harnessing the power of the group we have that gets people a little bit used to that. Now you have to be specific. With the challenges you put out there, you can't do these general give us all your ideas and make it a suggestion that will create a disaster. You have to have the challenge owner, the person that owns the problem in your organization and can do something with the solution. They have to be the owner. And then you can ask, does anyone have ideas if you're going to go outside, really, it's what are you trying to do? Because there's really different kinds of crowds. You know, I mentioned TopCoder and GrabCAD. There's herox that does a bunch of innovation challenges. There's Kaggle and driven data that do algorithm contests along with TopCoder. There are tongil Who does videos, you know, in this creative work, creative work is great, by the way, I should just pause on that for one second. If you think about any kind of work that you do where it's a complex kind of, you know, set of options, and you might actually ask, like an artist, give me five renditions of this. The crowd is a great way to get lots of different, varied input to help you kind of narrow that down. So we do a lot in the video and graphics and things like that. Gen AI is starting to mess with that a little bit. So I get it. But when it comes to design, you know, getting lots of different designs for something, especially in like actual engineering designs, you start to get some, some stuff that you can can really get better performance, because what you're really doing in innovation, right, failure is an important component, right? Like you have to be able to you have to try something and fail. Try something fail. Crowdsourcing is simply taking that in in certain instances and making it massively parallel to where you get 150 people working on something. Well, 140 something of those are going to actually fail when you compare the results in the performance, but those top five to 10 that actually outperform the rest, they're they're actually the ones that give you what you need. And it's not that that failure is bad. I tell people, open innovation as a participant, is one of the only places in the world, in your entire life that you can fail, and nobody cares. You can just do it for the sake of learning and the possibility of what it might mean. And that's a good thing, because we learn a lot from failure. But if you're at school, you can't fail. You have to get a good grade. If you're at work, you can't fail, and you're just wasting the company's money. And they'll they'll get there'll be repercussions. But in the challenge world, in your own time, you can actually fail. We actually had had an experience once where a lead researcher in this area participated in a challenge, and she ended up winning, and she said, I'm so glad that that we did this as a challenge, because my boss, my PI, never would have let me pursue this idea I had, right because they're trying to get grant money, and they're trying to be conservative, and in The research world, you've got to, you've got to have that conservancy sometimes. But we know innovation is about risk taking and failure, so open innovation starts to open up that that idea a little bit more. I think I got away from your original question. It was, how do you how do people go implement a lot of it is learning, learning about platforms and realizing that a lot of those platforms can do this largely for you, you contract it to them so they define the channel. You bring them your problem, they will form it into a challenge. Host it. Tell you how much the prizes should be. Can work with you on your budget. One of the things that NASA did that was kind of smart was they created a contract that had 30 different crowdsourcing companies, and then they could basically say, here's our problem. Compete on those. And that was a really great construct. It's kind of harder for some folks to do, but get to know those that you need. If you need technology searches, you know yet two is a great company if you're doing a lot of algorithm work driven data, Kaggle Pop Code, or those are great companies. If you're just doing general science with Zoku hero X, you know, and, and you can start relationships with those companies and, and they can help you do it. One of the things I advise people not to do is stand up your own website and try to post it. Post a job, it is harder than you think, and it takes skills that you probably don't have, even the work I've done in the past. And that's a we rarely do stuff direct to the public. It's almost always through these curated crowds, because they've already worked. Through the barrier of entry. These folks that have participated have participated on multiple challenges. They It's not scary to them anymore, and they still do big ad campaigns to try to bring in even new people, to try to make sure that that crowd that you're using has both the depth of expertise, if it's a technical problem, as well as the breadth across multiple industries, never a short answer. Sorry,

Jean Gomes:

what have you learned about helping the Problem Owner actually set the really good brief. What are the kind of mistakes people might make and sometimes maybe confuse a solution with a problem statement?

Steve Rader:

I would say the biggest mistake technical people especially make is not spending enough time understanding your problem. You know, what do we do in an organization? If somebody says we've got this problem, let's work on it. Well, everyone stands up at the whiteboard and starts brainstorming. Well, there's science that says that that's the absolute worst thing you can do, because as soon as one person puts an idea up on that board, you've eliminated something like 70% of the original ideas that could actually have been innovative. And so there's some techniques around that, but we encourage folks to really take the time to list out all their assumptions, list out all their constraints, really understand the performance parameters. Where are you today? Where are you trying to get stakeholders? Oh my gosh, that's the one thing I see people forget all the time, is they think, Well, I can just implement something. Well, if you better involve all the stakeholders, because if it's software and the IT department's not involved, they're going to tell you why it won't work after you've spent a lot of money trying to get it to work right, because you didn't know you had to have some security certificate, or you had, you know, that kind of thing becomes really important. And so when we're formulating problems, we always like to take a lot of time to find out everything we can about the problem. And then if you can find out everything you can about what exists in your organization, knowledge around that, that's where we have used our internal platform before to kind of pull to say, Have we already solved this? And we just don't know? And then you can actually use technology searches to search for, are there products or companies already out there that I just don't know about? And often the company, like yet two or nine sigma is really good at that kind of work. And then if you're still not seeing, hey, the kinds of solutions I need and you or you really want to be on the cutting edge, then you can run a challenge to try to find that really unique innovation, but it takes more money. Each of those things is a little more money. And so if you're trying to be efficient about this first figure out, if you've already solved it inside you

Scott Allender:

what's the what's the mindsets that leaders need to adopt to do this? Because as you're talking through this, I can almost imagine that some people are sitting here thinking, This sounds like a long process, right? I feel a lot of pressure. Innovation seems like it's distracting from or detracting from the day to day pressures of me delivering now. So what's the sort of shift, and do you see a lot of resistance when you go into organizations where people are like, this sounds great, Steve, but it's not going to work here.

Steve Rader:

Yeah, lots of resistance. There's Harvard Business scale school case studies, one on Victors and spoils and Havas, where, you know, John Windsor took his crowdsourced advertising and tried to plug it in to a traditional, large scale marketing and advertising company. And literally, you know, he's like the technology head of the company, and as he walks down the hall of the headquarters, people are closing their doors because they didn't want to talk to them. Because for them, advertising was this experience that they had slogged their way through, and the crowd could never be a thing, right? There's a great case study about NASA actually called Houston. We have a problem where NASA initially rejected it, because if you come to an organization, you feel like you're there to be the innovator. Why would you ever because you also find out when you get there that you only get to do that 1% of the time, right? It only comes along every few years, and then you're getting to do that, which, by the way, you have to change your messaging to say, Look, you are the innovator, but you need the absolute best, most up to date starting point to innovate, and that's what crowdsourcing brings you, is those ideas, expertise, technologies, that you just don't know about, and then you have to assemble them into a solution that is the new cutting edge, I would say, the big. Thing for CEOs and heads of organizations is ambidexterity, right? So Michael tushman at HBS has some great literature on that. I think there's a book out on this idea of balancing two competing priorities in your organization. So one is the exploitation, right? You've got to go make money. You've got to be producing something that there's no doubt about, that. You've got to keep the machine going. And oftentimes that's that's the result of some earlier innovation, right? That really produced a product and made value. But there's this other piece that is the strategic investment, and you've got to strategic investment at its core runs sideways to the exploitation, to the money making, right? If you're spending money trying to innovate, there's no profit today. There's profit in five years or 10 years. It depends on, you know, how big your scale is. But if you don't actually make this investment, if you don't actually make that a priority, then in five years, this product is no longer valid, and you have nothing to replace it with. So that in you know this, this strategic investment can't be chasing all the flash, right? So it's got to be grounded in real performance and real understanding of what problem you're trying to solve. And right now, it's very that's a difficult conundrum, right? Because there are a tsunami of technology you know, just just evaluating the impact of 3d printing is a task. If you have big manufacturing base, just looking at nano materials, if you've got some really complex systems, is a thing quantum in quantum computing, networking and quantum sensing, they're all about to change everything in some complex systems. And so one of the big organizational problems right now is that the complexity of the technology is requiring, and the change, the rapid change in it is requiring organizations to have to access expertise that they don't have. And that the this is, this goes to a whole area that I talk a lot about, which is open talent, which is kind of the flip side of open innovation, which is, if companies have this problem, and almost every company does, where they don't have all the expertise they need, the recruit and retain strategy that we've had for 100 years, recruit all the best people and retain them and get them going on all Your stuff that starts to break, because as things change faster and faster and starts to fragment the expertise you can't hire your way out of that you can't hire fast enough because you don't have a way to exit people, which you know you own. There's a lot of companies right now where you go and talk to them, they're like, yes, we have a skills shortage and a hiring freeze. Well, that's because you're trying to hire your way out of this. And so this, this big shift that happens to have been coming since about 2015 where, kind of by happenstance. I can't figure out exactly why it started happening, but people started moving to the gig economy. Well, that happens to be a really convenient thing for organizations now, because there, there's a lot of people, and a lot of people just think about Uber drivers and, you know, food delivery. But if you look at the full landscape of what's happening out in independent work, most of the top experts are now freelance. They see that they can actually make a better living kind of farming out their skills to the companies they need them. And there's NDAs and ways to protect all of the fears that companies have about sharing their information that's actually getting easier to deal with. But what you're finding is companies are having to now hybridize their expertise, because even the best companies can't hire all the experts they need. And it's both they can't find them, find people willing to move to your to your city and to work full time, remote work in the pandemic really showed people, Hey, there's another there's another path here. They love the agency of it. They love the fact that it's not beholden to a single industry that might have ups and downs. And so there's this kind of new work concept that's out there. And if you talk to a lot of startups, this is what they do, native. They don't go hire a staff of 10 people. They go hire freelancers, and then they get to know them, and that they still work with them as a team. It's not a one off all every time. But they don't own them, right? They don't own all of their time, and that's great for a startup, because your marketing person. You don't need a full time. You need somebody a third time. And the fact that they can make a living makes them a more stable team member, not a less stable and then it's, it's you still have to bring them into your team. So this is a thing I've been working with open assembly. John Windsor, who I mentioned earlier, is the founder of that, and it's a whole group where they're just talking with the communities that supply talent, along with the enterprises that are trying to adapt and use talent. And it's it is every time I think they're two different things of open innovation and open talent, I realize, no, it's all crowds of hundreds of 1000s or millions of people that are being used to match a need to a capacity, right? Kind of like Uber is someone over here needs a ride and somebody over here has a car down the block. Well, I can put those people together and meet needs for both, right? The same kind of thing in these platforms. I've got this expertise in quantum sensing. Somebody needs that for two weeks, two months, two years. I put them together, right? And the most exciting part of this, this is brand new, is AI matched high performing teams. So there's a new company called Team lift that has already started doing this in terms of being able to take a roster of people, match them into small, high performing teams based on their skill sets and personality traits. But you can imagine taking your problem to a crowd of the future, and they maybe have half a million people there? Well, they can stand up 50 high performing teams each that has a task to take two hours and decompose your problem or two hours and come up with new ideas, like all in parable in parallel, all kind of competing with each other, and orchestrate that into an entire product development cycle. So there's some really interesting stuff. And I would say now each of those teams also trained and equipped on using generative AI. So, you know, being able to operate lightning fast. So it gets to be really exciting, terrifying in some ways, by the way, because everything's changing. But

Jean Gomes:

yeah, well, it kind of challenges the fundamental notions of what a company is and how it operates at a business model level. And it kind of sums up my mind this idea that a company could become like a brokerage between problems and talent and resources, which is kind of what you're describing there. So if you take a step back for a moment and go, right, I'm listening to all of this, and as a CEO or as a finance director, or somebody who's kind of thinking, this is really exciting, can you kind of walk us through how you would start in this process, beyond just the tournaments or the challenges, and how that might play out over several years from starting to build something to the system that you might create, yeah,

Steve Rader:

what I recommend is really putting together kind of a center of excellence, right? So a core team that one goes out and learns all of the piece part learns, you know, there's some great books like platform revolution, which is almost a textbook on how to stand up a curated crowd. There's, I think it's platform computer, no machine, platform crowd. Great book. Also is a primer open talent John's books, really good, competing in the age of AI, so just kind of doing the homework of what this is, and then running pilots right. Have a pilot on putting together an internal crowd, simultaneously running your first tech search and your first challenge to see what does that look like, how does that feel? And then really taking stock of your technology portfolio management, right? And trying to start to understand what that ambidexterity might look like, so that you understand what you have to carve out, because this does doesn't come for free, but remarkably open innovation tools, when you compare them with using just traditional go out and get a consultant, things like that. At NASA, they were seeing 75% of the projects had cost savings, and the average cost savings was 50% so significant kinds of savings can actually be had, but you've got to have the program. But just start piloting these pieces and see which ones fit best. You know, if you're software centric company, what's the right? You know, if you have high performing software teams, you don't need to bring crowds into that. They're doing great. But if you're struggling, and there's some areas where you know you need expertise in. Languages or hardware that you don't have. Well, that might be a really good place to say, oh, let's find a crowd that can actually help us do that, or let's find open talent to bring in and help our team come up to speed. But really trying to look at the landscape of what am I trying to do as a company, and where's technology a threat for me. So where do I need good surveillance and good kind of new ideas? And then in what kind of investment can I? Can I make and putting the right framework around that, so that the team the Center of Excellence really knows how far you want to go? In my opinion, open methods are kind of a core skill that technology and tech folks need. So if you have a tech organization training people all around the organization, not every single person, but enough people that you've got someone who's looking at what's going on, and when they see a problem or they see a risk, they say, you know, that's a good candidate. Let's this is the right tool to go do that. I tell people, you'll get really excited about open innovation, sometimes, none more than me, but it's not a tool for everything, and it's not a tool for all the time. It is fit for certain phases of product development in certain phases of creativity. So it's a tool you need, but you need to find out who needs it when, and that's where the center of excellence can kind of be a broker and an orchestrator of those, so that they're working with various folks around the organization to figure out when the right need is to get their buy in from those problem owners, so that that they actually buy in. Because you don't get that, they'll never implement what you bring them, right? So, kind of working that aspect and just just created along the way. But you have to start slow, and it has to, you know, have you have to have people that know, big mistakes people make is they'll put their marketing or HR folks in charge of over their open innovation program with no training. And you know they'll there are some, I will tell people. And I think you guys probably know this, innovation is the most dangerous initiative you can partake in in a company. It's the most crucial. But if you're trying to tell your organization, we need to innovate and you're not serious about enabling innovation, it will backfire on your entire organization. Will become this Dilbert cartoon where they just roll your eyes every time you say you want innovation, because they'll try to bring it to you in lots of different ways. And if you don't have a framework where you have the strategic investment ready, they basically will see you saying, well, we can't do that. And it's like, well, you want to innovate, but you're not willing to do any and over and over organizations. And you know, we call that innovation theater, right? We see it all the time. And I think open innovation is no different. It's a hard tool to just pick up and go use now, you can find people who are experts in this. This model has been done across Procter and Gamble and General Mills, and there's a ton of companies that use this. They just don't talk about it because it's their competitive edge, right? And I think we're more and more seeing some of the startups and some of the early, the younger folks, they just, they know it's out there, and they use it as if it's, you know, yeah, of course, use this. This is just what you do. You know, you need a graphic, go out and run a quick contest, or use Gen AI, or, you know, you need a piece of software. I don't want to go hire a bunch of software people. I'm just going to spin that over here to TopCoder to, you know, see, outsource. And that's, I think, you know, it's a different way of working. And I think it's something that's not always easy, but it's, it's necessary, unfortunately, I think the other thing is avoid the black and white thinking of, I need to hire all freelancers, or I need to do everything in open innovation, like it's one piece of what you're already doing. Like, don't try to upset the entire apple card for a long time. Even though open talent is going to be a necessary thing, it's not going to take all of your your full time employees, right? It's you still need that. What I tell people is things are changing so fast. If you really want to get on board, upskilling is your, is your the thing you need most, and why you have to bring in experts is because it's really hard to upskill internal let your workforce go participate, train them on what open innovation is, and open talent. Let them spend 5% of their time doing these things, learning something new with a plan to tell you how it's going to help you bring back a value to the company and say, Hey, I'm going to let you do this. You. To keep any profit you make, any prize you win, right cash incentives for them to go learn. And then every six months, say, tell me what you learned from these other industries you worked with, or other things, and tell us how that's helping us. It starts to give people a little bit of agency, a little bit of visibility into what's going on, gets them out of the bubble. And it's a free training program like That's right now, training is everything, and if you even augment that with a little bit of Hey, and if you find a Coursera course, we'll pay for that, you start to get a workforce that is transitioning to the new technology with visibility of what's going on out there. And I think that's a really valuable thing.

Scott Allender:

If you can, could you share a little bit about the successes that you and your team have led through open innovation? I'd love to hear about, like, what's your when your standout sort of innovations that have happened as a result of doing this work and, and, and I'd be curious to just a bolt on a sort of sub question around that I'm curious as a leader, how has leading this changed you

Steve Rader:

so success? You know, NASA's had a lot of successes over the years. You know, Jeff Davis and Jason Cruzan started that program and and that program has run over 850 projects. They they actually run projects for the other federal agencies as well. They've worked with 30 different other federal agencies, and I think at any one time now, they're running about 80 to 100 projects. So it's there's a lot to choose from there. Some of the more fascinating ones that that I've seen. There was a really interesting one on for Homeland Security, on, like, increasing the accuracy of scanners, and they spent two and a half million dollars on that challenge. But they the results they got were like, 98% detection rates, which were just blew away what they had been able to do before. There's been work in the medical areas with things like kidney disease and Lyme disease that have been really outstanding. Gosh, again, 800 projects, so there's a whole lot. One of the most fun ones I ever did was one called Space poop. Turns out, in space, you got to handle some things. And when the Orion spacecraft was going to do that burn to the moon, you know, and it gets rid of its last big rocket that's just stuck with the service module, well, once you make that burn, you're not coming back for four to six days, right? So you got to go all the way around the moon and back, and if at that point, the cabin gets a leak, you have to get back into your your pressurization suit and live there for six days. And that, you know, if you've ever had a kid with diaper ash, you know, within hours, that can be a really painful thing. Well, within days, it can be a really dangerous thing. So that challenge was to look at, how could you do this? It was really interesting that the winner of that challenge was a flight surgeon out of San Antonio for the Air Force, and he didn't actually do design drive. He just, like, did these sketches, but he built these prototypes. And one of the things he built was a little prototype of a little airlock. He said, In in laparoscopic surgery, we inflate the belly to like, 15 psi, right, which is about the same as the pressure differential on a space, a little bit more, actually, and they insert this little airlock into the abdomen so that they can pass wipes and little instruments and and, well, that's exactly what you need in this case of trying to deal with Facebook. What's interesting is herox Did that challenge. They had 20,000 registrants and 5000 submissions, and they actually in the contracts NASA had with them, they they had to actually only provide for judging submissions that were met all their criteria. Well, there were only 87 out of those 5000 so herox actually had to call those down to 87 but it was fascinating. Well, I

Scott Allender:

think you've just given us a title for this episode, so that's great.

Steve Rader:

Yeah, it's funny. I actually, in my early part of my career at NASA, one of the first jobs I had was as a flight controller for the life support system, and one of the first assignments they gave me was, okay, the main thing you're responsible for, it is the waste management system. So I very quickly had to learn how to to be comfortable with talking about human waste to my family and friends who are like, Oh, you work at NASA. I'm like, yeah. They're like, what do you work on? I'm like, wow, but space, that's one of the hard things about. Space as you go, it's

Scott Allender:

real deal with all. Yeah, yeah. We

Jean Gomes:

talked a little bit about AI and how it's changing things. How do you think this is going to play out into open innovation over the coming Yeah, five or six years,

Steve Rader:

I spent a lot of my time talking to academia about that, because we're actually trying to study what the crossovers are and what the impacts are, I think what it's basically going to be is, if you're not monitoring Gen AI, then you may as well just pack up, because it's changing everything. But I think for the time being, it's going to provide better and better results, right? Because all of the folks out there that are in the crowd, a good chunk of them are going to start using generative AI to increase their performance, and that means we're going to get better submissions, and it can kind of really condense the timeline of what they can find, what they can produce. And so I think you'll, you'll see increasing, uh, quality of submissions as we get better and better. There's it depends on the area. Open Innovation can be used, like, I say, for for multimedia, for technical challenges, for prototypes all the way through. And so the things we think will drop off first are, if they haven't already, or graphic and multimedia, right? We're just watching and the different kinds of challenges. Again, I think it'll you'll see a steady increase. I do think that the big gear change for open innovation is going to be when we get the capacity to orchestrate high performing teams rapidly. Because high performing teams, where each team member is trained on using generative AI, they're going to be able to tap into that unique human creativity that generative AI is going to take a long time. I mean, generative AI is literally predicting the next word to say it is not being creative. It is finding the creativity out there that we most have tapped into and giving it back to you. So it's not as great on finding things at the edges, because they don't statistically. You know, extracting that statistically is hard, and so I do think once you start getting high performing teams really using generative AI to augment, you can get some really fantastic result results. Right now, open innovation is actually only good for narrow problems, because you're asking individuals, unless you well, you can have higher and higher prize, price prizes, right? A million dollar prize, and you'll get a team to assemble. But if you're doing $100,000 prizes, kind of harder to get a team to form. Well, that means you don't have all the disciplines to do a complex problem, so it has to be narrowed down to a piece of a problem with these high performing teams. Now you can put teams that have the entire design team, that have an electrician or an electrical engineer and a chemist, and, you know, all the people necessary for that team to really build something more complex. And I think that's going to be a key thing. Now, there may be a time when generative AI, you know, just gets so good and so cheap that that's what you go to. I think we're a ways away there. In so it'll it'll be a path, but you definitely want to be watching both of those things, because they're both really important.

Scott Allender:

What else should we be asking you Steve in the time that we have left?

Steve Rader:

I want to go back a little bit to the open talent thing, because when I first started working all this, I was seeing a lot of new technology and a lot of automation and Gen AI. And the first thing it goes through your chest, really, not even through your mind, but through your chest, is, oh my gosh, everyone's gonna lose their job. And what do people do? Right? And what was really interesting is a couple years into that, I started seeing this open talent movement, where people were moving into kind of working through platforms to access global need in that new matching. And what I found there was some real hope, because people have agency. They're encouraged to do lifelong learning, which is ultimately the secret to the adaptation to these new tools. Because the new tools, we're not going to run out of problems like in fact, they create some new problems, right? And we have to go solve those, but it does up the game, right? So I tell people when they made Disney's Snow White right back in it's like one of the first ones they did right back in the 40s. They had something like 250 animators work for like two years to make 2 million different frames at. That had to be pieced together, and most people agree that a team of about three to five could recreate that in a few days now with the technologies we have. And so you'd think, wow, filmmaking super cheap now, but it's not, it's it's much more expensive. But what do we do? You if you look at Iron Man three, right? That is actually an animated film. Looks totally real, but it's animated. And the animations we're able to do are super high fidelity. They look like reality, but it took something like 3500 animators to do that. So when we get tools that allow us to do things faster, it doesn't necessarily mean we just get rid of everyone who who's not keeping up we do harder things. And that's kind of where the hope comes, for me is we have more accessibility to education than ever before. I think education is going to change drastically and and you know, especially now with generative AI tutors. I don't know if you've started to watch this, but tutoring is a massively useful tool in education, and there are now generative AI tutors. And interestingly enough, people learn better when they don't have a human, frustrated teacher, right, where there's not somebody who's like, why aren't you getting this right? So it's kind of a good thing in that regard. Now there's cognitive offloading that works the opposite effect there, but, but if people start to actually train and learn and start to find those, those jobs that they need. Through these new platforms, you start to get a really malleable workforce that can keep up with that cutting edge and that can get redeployed. And eventually, yes, the hours per work we might shrink, but if you're, if you're working in a freelance environment where most people are kind of working as independent workers, that that actually changes in a way that's easier to adapt, right? Because the rates adjust to where you can still maintain a living wage provided the supply demand works out. So gets really interesting. There's a lot of theory there. I don't think it's all worked out. There is a rate issue there, and this gets kind of nerdy, but at the same time, there's hope there. It's not all gloom and doom and everyone's just going to be out of a job tomorrow. So

Jean Gomes:

no, I love that. And I think the notion that there's, there's kind of, there's hard work to do, because the technology kind of pushes you to have to actually add human ingenuity at a higher level. A lot of what people do in organizations now is shuffle around other bits and pieces of information, which is very

Steve Rader:

ironic, if you think about it, this new technology that's that's basically doing things that took us hours and hours, but the onus then becomes on us to learn how to use those tools and do more. It's it. You'd think it just allow us to go sit around sipping my ties. But turns out, we haven't really oriented ourselves to a world where that's that's the case, right? So

Sara Deschamps:

If the conversations we've been having on the evolving leader have helped you in any way. Please head over to Apple podcasts and leave us a rating and review. Thank you for listening. Now let's get back to the conversation.

Jean Gomes:

It seems like there might be an inversion of all that about to take place. Yeah. What given? Given that? And I know you, you, you teach on MBA programs and so on. What would you say to younger generations now in terms of the skill sets they should be thinking about?

Steve Rader:

Yeah, no doubt. Establish yourself as a lifelong learner. Be curious. Learn how to be curious, learn how to learn, learn how to learn quickly. There's so many free tools out there like I think everyone should go, for instance, download. I think it's RapidMiner, which is a free machine learning tool, and take the few hours course it takes to learn how to actually write a machine learning algorithm. You literally only have to understand statistics, which I actually don't really, I'm not a great statistician, and yet I was able to do that and learn how to actually create a machine learning algorithm. Now, does that mean I'm gonna go be a machine learning algorithm guy? No, but the fact that I understand how that works now helps me to know when I should go build an algorithm, right? So same with a CAD model, like there's free CAD models out there. Learn how to go create a CAD model. Go learn how to do something new. Go buy a 3d or go to a 3d printing lab. They're all over these maker spaces. Go learn how to use one and learn how to print out some goofy thing. But these new skills are the building blocks. Yes, and companies are going to need people who know how to use the building blocks of the future. And that's, you know, go spend$20 to get a month on chatgpt, not the free version, but the actual four. Oh, and see what you can do. There's a great book by a guy out of North, Northwestern whose name is escaping me, I apologize, but he basically says, look, go spend three. Ethan Malik, sorry. Go spend three or four hours just asking Gen AI questions and seeing how far you can push it. You can literally tell it to create a PowerPoint deck, or create it the code for a site, and it'll just go do it, but you've got to kind of embed yourself with it for three or four hours and get lost in it to really start to clue in. Oh, this is how I need to use this as an extension of myself to go do things right and do my work. So if you're young, there's never been more opportunity to plug in and go do things. Go establish yourself on some of these freelance sites. Start building a reputation. Do a gig for practically free just to see what it's like. Participate in a contest. Start doing you know, you can literally the world is your oyster right now, and it's all really hard, right? You've got both of these things happening at once. What's really great about it is most of the gatekeepers are gone. So when it comes to equity, as long as long as people can get in onto the digital and there's still divide on equity, on digital platforms and the rest, but if you can get online, man, there is a ton of opportunity there for people from every country, culture, walk of life. The most exciting to me is some of the folks that are on the spectrum that could never make it through an interview now have a chance of having a really rich life and making a living, because they can actually a lot of people on the spectrum are really good at like, math and some data stuff. Well, now they can get those jobs and do stuff so people with severe disabilities, it just starts opening up the job market in ways that the interview process and the whole people factor kind of, you know, created along with in person, doesn't have to all go away, but, but it is this new world we live in where we really have access to some increasingly special resources that we didn't have access to before.

Jean Gomes:

That's really helpful. And give Scott nice some homework as well. I think

Scott Allender:

Steve, how can people connect with you if they want to learn more? Bring you into their organization to help them out. Yeah,

Steve Rader:

LinkedIn is usually the easiest way, because you just connect to me, and then she can message me, I'm out there, you should put Steve Rader. Usually, that will get to me that's not finding me the word, sometimes that'll hit me, but just shoot me a message, and I'm happy to I have a side gig, approved side gig at NASA. So today, for instance, I'm not representing NASA. I'm representing my my crowd resources consulting, but I I do talks for organizations to really, kind of explain this stuff and kind of get them motivated around it. I do consulting where I'll come in pretty light, kind of to help folks get oriented on this. And then sometimes I'll do workshops or teaching or lecturing, excellent

Scott Allender:

well, we'll put all that in the show notes. And thank you so much for joining us today and sharing all of your amazing insights. Super, super useful, really good stuff.

Steve Rader:

Thanks so much. You guys been great.

Scott Allender:

And folks, until next time, remember the world is evolving. Are you?

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Jean Gomes

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Scott Allender

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