How the United States Air Force Accelerated AI Adoption

BRIAN KENNY: The United States Department of Defense is big. Actually, it’s a behemoth with 2.86 million people. It is the single largest employer in the world. And like any large enterprise that has to find a way to organize itself, the DOD has a hierarchy with policies and procedures that govern everyone and everything, which hardly sounds like an environment where innovation could thrive. We’re told that the most innovative firms encourage employees to be agile, think on their own, challenge established ideas, and above all, don’t be afraid to fail. Exactly, none of which sounds like it would work well in a military hierarchy. But amid a race to understand how to harness the potential of AI for our national defense before our rivals do, innovation is exactly what we need. Today on Cold Call, we’ve invited Professor Maria Roche, and the protagonist in the case, U.S. Air Force Major Victor “SALSA” Lopez to discuss the case entitled, “Accelerating AI Adoption in the U.S. Air Force.” I’m your host, Brian Kenny, and you’re listening to Cold Call on the HBR Podcast Network. Maria Roche’s research focuses on the sourcing, production, and diffusion of knowledge in various contexts. Major Victor SALSA Lopez is chief of autonomy operations for AFWERX and the protagonist in the case we’re discussing today. We’re going to dig into what AFWERX is shortly, but thank you both for joining me today.

MARIA ROCHE: Thank you for having us.

VICTOR ‘SALSA’ LOPEZ: Thank you so much.

BRIAN KENNY: Great to have you here. This is a really interesting case. Obviously AI is kind of ripped from the headlines, everybody’s talking about ChatGPT, I actually thought about maybe having ChatGPT write the introduction to this podcast, but I didn’t go that far. I don’t feel like I’m brave enough to do that yet, but it certainly is a very topical subject. And I think the fact that this is happening within the kind of hierarchy that the DOD has makes it even more interesting. Maria, I’m going to start with you. Can you just tell us what the central issue is in the case, and what your cold call is when you start the discussion in class?

MARIA ROCHE: Of course. So, the real central issue in the case is thinking about the perils of digital transformation in a large bureaucratic organization as you already mentioned. And so this also falls under a broader theme of innovation. So often we think it’s only small organizations that can innovate, startups, but a lot of innovation actually comes out of these big organizations. And so thinking about the trade-offs also of how you organize it and set it up, because in a large organization, you may have to do it in a bit of a different way than in a startup. The cold call to really get started in the case is thinking about, is it the right choice to set up the AI Accelerator as an innovation unit outside of the organization? And then after that, we really want to dig in because it’s not clear that this is really the best way to do it.

BRIAN KENNY: How did you hear about this, and why did you decide to write the case?

MARIA ROCHE: So, I decided to write the case because one of my MBA students who I taught in the Required Curriculum in strategy came to me with this idea because he was a fellow in the AI Accelerator, and had all this great experience. I was like, “Of course, this is so nice. This is the best experience you can have actually writing with an MBA student, a case about this.” So we dug right into it. And it’s also really close to my own research because I’m a strategy scholar, and I’m an innovation scholar at heart. So for me, really thinking about these questions of how can you incentivize innovation, how can you actually adopt these new technologies is core to my research. So it was like the perfect match, and then we got started.

BRIAN KENNY: I would venture to guess that most people don’t think about innovation and DOD in the same sentence, right?

MARIA ROCHE: No.

BRIAN KENNY: So, I’m wondering, I think people might be surprised to know that the DOD does invest heavily in this. Can you talk a little bit about their commitment to finding ways to be innovative?

MARIA ROCHE: This also goes back to one of my research projects that is looking at the Rad Lab, which was stood up in World War II. It was actually one of the first times you have big science happening in the United States, and it was powered by the military, and it happened right here. The legacy still lives on today in the Lincoln Lab, and also part of AFWERX, and the AI Accelerator. They touched upon these things that were happening in the 1940s, and that’s really when things started with military support for research. But there’s many more examples, I’m happy to tell you more later on.

BRIAN KENNY: Can you list us a couple?

MARIA ROCHE: Of course. There are so many, so I cherry-picked a couple because I’m like, “Which ones are the best ones?” So one of them is the field of material science that actually came out of government-funded research, thinking about how to create this new field. Another are the first weather satellites. The world’s first large scale project on personal computing in the 60s called Project MAC, also government. You had the first computer mouse. No, it was not Apple, it was government-funded research. You had the first mobile robot that walked around using AI, actually, also a long time ago called Shakey. ARPANET, without that, we wouldn’t have the internet. We also have the Personal Assistant that Learns, PAL. Without that, we wouldn’t have Siri. GPS. SEMATECH, which was fundamental in creating the semiconductor industry the way we have it now. And so I can go on.

BRIAN KENNY: No, I mean, that’s a huge sort of legacy of innovation within a place that’s not really given credit for that, I guess, in popular context. So SALSA, let me turn to you for a second. Can you talk a little bit, we’ll switch sort of over to AI, about how the DOD has approached the AI to this point?

VICTOR ‘SALSA’ LOPEZ: The AI Accelerator was born out of the American AI Initiative, and it was the nation’s strategy on Artificial Intelligence. That was back in 2019 February. At the time, Secretary Wilson at the Air Force answered that call and said, “We in the Air Force need to be able to answer this Executive Order on how we’re going to move forward.” And we’ve really tried to work on AI ethics and AI safety as well as AI development concurrently. The thought was, “We need to do this in a way that is open to the world.” And there’s risk inherent in that. We are at the heart a military organization focusing on our nation’s defense. And that means that at times, keeping secrets from those who wish to do us harm. However, this is so important that doing AI in a place that was in the public eye, that we could be questioned in a good way, that we could have peer reviewed research, and that we could work in an academic environment at the place where AI is really at the forefront of thought was really important to the Department of Defense. And in doing so, the department of the Air Force decided that MIT was going to be this home of this AI Accelerator.  So Lincoln Laboratory, the Department of the Air Force, and the Massachusetts Institute of Technology altogether working on these problems completely in the open, completely unclassified, publishing academic papers with peer reviews that you can go look online so that we can get that feedback about what is the best way to use this technology, what are the wrong ways to use this technology, what pitfalls are we going to have, and how do we advance not only the state of the art, but also educate and train our own folks into how to use this and put it into practice in their everyday environment.

BRIAN KENNY: There’s been a lot of concern raised about AI and the potential for it to do harm to people.

VICTOR ‘SALSA’ LOPEZ: Absolutely.

BRIAN KENNY: And some of that’s come from within the industry itself. So I’m wondering for our listeners, can you maybe talk a little bit about some of the types of AI that the military is interested in?

VICTOR ‘SALSA’ LOPEZ: Absolutely. It’s very boring, I caution you. I think the real promise of artificial intelligence in the day-to-day, right? In the small innovations, and the same things small businesses and large businesses care about, is the ability to make things a little bit faster, gain insights a little bit better, and make better business decisions at the end of the day. How do we spend the money better? So at the AI Accelerator, if you go on the website, you’ll find a whole bunch of projects. We started with 10 initially, I think they’re up to 13 now. I am out of the organization now. I had to move, the military moves every few years. But for them, one of our top things was, for example, pilot training schedules. If you ever go into a squadron in 2023, there is one to three people who are in charge of having a massive whiteboard with pucks. And on those pucks are names, and on the lines on the board, there are airplanes. And it is some poor souls’ job to take each one of those pucks and put them in the right place so that you have a pilot, and a copilot, and an engineer, and all of the people that need to fly that airplane ready to go until someone, of course, gets sick, their child gets sick, they have a problem, and then this beautiful schedule that you had flowed out for the last week completely goes up in the air because everything is broken. Surprise, surprise, we’re also seeing this in industry. There may have been a few meltdowns of our own aviation industry recently about pilot scheduling.

BRIAN KENNY: Yes.

VICTOR ‘SALSA’ LOPEZ: This is a really hard, massive problem, and the DOD is not immune to it. How do we better optimize for a pilot training schedule? And how can we use artificial intelligence, machine learning technology, to better do that? That’s a very mundane, but very important task.

BRIAN KENNY: It also sounds relatively harmless. So I think-

VICTOR ‘SALSA’ LOPEZ: Absolutely, right? And again, something we can put in the public eye, and something that we can get. In addition to that, we also have a navigation problem there that’s really interesting. GPS as we’re seeing in some of the conflicts around the world, is becoming a more and more contested domain, and we might not have GPS. For the everyday person walking around the United States, your GPS probably isn’t going to go out unless there’s some strange exercise happening. But for the military, we are worried about potentially losing access to GPS and the signal. So how do we navigate using the Earth’s magnetic field, much like a compass, but a little bit more refined using machine learning technologies? So we published a dataset at the accelerator. That dataset with that published got picked up by multiple people around the world. Some very large companies picked it up. We talk about this in the case. There was a small business that actually ended up spinning out using that dataset. And now that small business is on contract actually through AFWERX, moving that technology to navigate very precisely within about 100 meters using the Earth’s magnetic field and nothing else, no GPS, right? But this is using machine learning technologies to clean the signal of the Earth’s magnetic field while we’re flying, and all of the other intrusions that happen on the plane with every other electronic piece of equipment.

BRIAN KENNY: So, there’s no super mega laser that’s being developed-

VICTOR ‘SALSA’ LOPEZ: There is no super mega laser… And I think this is something unique to Western cultures too. I think our sci-fi really puts a bad taste in our mouth for artificial intelligence. But as Maria mentioned, we had this quadruped robot that the DOD built back in the 60s using AI. AI is not new. We’ve been talking about this since the 50s or 60s. There’s some really funny videos out of MIT actually of professors talking about the dangers of AI even back then. And we just haven’t seen them come to fruition. And I think part of it is, when it really comes down to the nuts and bolts about how do you make this technology the best for my organization, it’s about the same stuff we all care about, DOD or company. How do I save money? How do I make better decisions? And how do I optimize?

BRIAN KENNY: Maria, the case does talk about the difference between legacy companies and digital native companies, and that’s important in this context. We’ll find out why a little bit later in the conversation, but can you just sort of lay out what the difference is between those two things?

MARIA ROCHE: Yes, of course. So digital first or digital native companies are organizations that were born to be digital. So, the way they were set up, all of their systems, their business models, how they interact with their customers are already digital, right? Whereas you have more established organizations, older organizations, I mean, it’s not really their fault. They were born before this was available. So, they have to kind of catch up and they keep changing their systems. It’s more like a patchwork, almost Frankenstein like a thing that starts to happen because things keep getting better, updated. And then what do you do? You try and fix here and there, but then in the end you’re left with this weird structure, IT structure, where everything’s kind of all over the place. And so there are these big differences in terms of those kind of companies. You can think of Spotify, Facebook, Google, even these companies were born around being digital.

BRIAN KENNY: I think many of our listeners can relate to what you just said though. And I would say Harvard Business School probably falls into that category of a legacy-

MARIA ROCHE: Exactly.

BRIAN KENNY: … firm because we’ve had many different systems, and we try to make them work together, but it’s hard. And I can only imagine what it’s like-

VICTOR ‘SALSA’ LOPEZ: Yes.

BRIAN KENNY: … at a place like the Air Force or the DOD.

VICTOR ‘SALSA’ LOPEZ: Absolutely. And we run into these problems every day. In fact, one of the new efforts at AFWERX Autonomy Prime is looking at specifically how do we get after connecting all of these systems together. Many of our aircraft and many of our systems have different languages that they speak. So I need a translation layer between them. There’s always a fight about standards, but the fight about standards for communication is, we see all the standards on the table, we realize that we need a standard to unify them all, and now we have N+1 standards to unify them all. And the problem continues, right? And so this is such a hard problem within the Department of Defense. But having the education, I think, to understand where the technology is good, and where it is bad, and where we need to put our resources, just like any business, is really at the core of the education part of the mission for the AI Accelerator, and now AFWERX as well as we start to look to try and integrate some of this technology with the war fighter.

BRIAN KENNY: Can you tell us a little bit about your background?

VICTOR ‘SALSA’ LOPEZ: I am from South Texas, grew up in Houston, born in Corpus. I studied astronomical engineering at the Air Force Academy. Started my career a little bit before that as an intern at NASA, working on mostly earth science. And as I transitioned into the DOD space, really loved space flight. I’m still a nerd. I did not do well. I think I had a B- as my GPA, barely, but I love it. I’ll talk your ear off about it forever. And then moved at Georgia Tech, to a Systems Engineering Master’s program before actually coming to the AI Accelerator at MIT. And then working a few research initiatives there on artificial intelligence, mostly focused on human machine teaming, and also multi-agent reinforcement learning for kind of the nitty-gritty specifics.

BRIAN KENNY: So that background certainly makes sense to have you involved with the Innovation Accelerator. Can you talk a little bit about what the purpose was for the Innovation Accelerator?

VICTOR ‘SALSA’ LOPEZ: Really to conduct fundamental research to move forward the state of the art for really the good of all mankind. Again, very similar to the Rad Lab, very similar to 1940s, right? And as we continued that mission in the early days, we realized one of the big things that we needed to do was start educating our people. It was really hard at the core for the airmen who were on the team. It was the first time we embedded active duty members into a university, not to go to school, not to get a degree, but to just be there, and liaise, and work in a project. We haven’t done that since World War II from the Air Force perspective at least. And so, as we got there, we realized that this education piece was really central to the mission. And this is a prototyping organization. So, there’s 10 airmen, there’s 400,000 or so people in the Air Force. There’s no way that we could do it for everybody. So we said, “We’re going to prototype. How do we do this? What is the best way?” And there’s actually, believe it or not, an education AI research project.

BRIAN KENNY: There we go.

VICTOR ‘SALSA’ LOPEZ: So apps like Duolingo, and whatnot have really refined how do you reach out, and give people the right notifications at the right time to get them the right education. That same research is being conducted through the AI Accelerator with MIT, because we need to know how to do that with our members, not just for things like language, but also for digital transformation.

BRIAN KENNY: What were some of the projects that you pursued, and how did you arrive at the ones that you thought were worth pursuing?

VICTOR ‘SALSA’ LOPEZ: So this is the fastest I’ve ever seen the government move. So I mentioned in February this Executive Order came out. I think in March, they decided that MIT was going to be the home. In April, they yanked 10 airmen, I was one of them, and we were off to the races. So what we did is we put out a call-out to the professors on campus and said, “Hey, what do you want to work on?” That was kind of the first thing. We are great airmen. I had an engineering background. I have not spent the last decade or four decades working on artificial intelligence. So we let the experts be the experts. That was key. They come to us. We had over 200 proposals that were given, and then the airmen had to decide what we thought was the best. So we were reading through them. Having that technical background obviously helps. And then we went down to the force and said, “War fighters, which one do you care about? How do we match you with this?” And then we went to, this is the weird part for the Air Force, at least the program office, these are the people that hold our requirements documents that actually decide what needs to get funded of the one to end things that are needed, right?

BRIAN KENNY: So as we talk about hierarchy and bureaucracy, you’re entering into that.

VICTOR ‘SALSA’ LOPEZ: Correct. So there’s the tactical level of people doing the work, and then there’s people that really need to pay for the things to do the work-

BRIAN KENNY: Sure.

VICTOR ‘SALSA’ LOPEZ: … right? This is important in any organization, and their mission is very important in the Air Force, but they have a list of things to get done. So we said, “Which of these projects? This is what your tactical users want, these are the things we’d like to research. Is this something that you would enjoy as well? Would this benefit your mission?” And it was really important to have that bureaucratic at the top kind of support plus the tactical level support, because if we’re missing any one of those, we likely will not get things from academia over into the institution. And so having a deep understanding of where that moves, and having the authority to speak to them was really crucial to the success of transition.

BRIAN KENNY: Now, you mentioned earlier, Maria, that innovation can happen within a large setting like this, but I would imagine it’s not easy, particularly if you’re one of 10 airmen, and you’ve been assigned to this new unit, and you’re trying to strike up relations in an environment that you’re not used to being in, and then sell that back into the organization. Can you talk a little bit about some of the challenges that units like this would typically encounter?

MARIA ROCHE: So there’s also thinking about the larger organization that there is this concept called inertia. So when you have things in place, changing anything is going to be incredibly difficult.

BRIAN KENNY: Sure.

MARIA ROCHE: So you can think about it as cultural inertia. That is a lot about the tacit knowledge, how do processes even work? So even thinking about, how do you change any of that? Then you also have the architectural or administrative inertia. This is much more about the formal process. So as SALSA was just mentioning, you have this hierarchy. Everyone’s been doing this for years, for decades. How would you even change that? And then thinking about technical inertia or technical debt, as I was mentioning before, if this is the way the structure’s been built out, how do you think about changing it? Do you shut down the organization for a week or two, which is going to be incredibly difficult if we’re talking about the Air force. So how do you then actually do that? And so there are different ways of how you can organize for this kind of innovation. And the AI Accelerator and AFWERX are these kinds of approaches where you actually take a little group, and take them out of the organization. Now, you don’t have to worry about the formal hierarchy. You have a small group of people. You can be a lot more informal. You don’t have to use the official way of talking to people. There’s a lot of things you can do there. Things are faster. SALSA also mentioned earlier the openness. If you are in the Air Force, think about all the secrecy and the data that you’re not even allowed to talk about. But in the smaller organizations, it may be a lot easier to actually have these conversations. And when we’re thinking about how technology or innovation is produced, it’s fast, has a lot of feedback, often involves face-to-face interaction, being close to universities, which are very open in that sense, right? We disclose our work publicly. And so-

BRIAN KENNY: Yeah, perfect complete antithesis.

MARIA ROCHE: Exactly. So it’s like polar opposites. And so how do you do that? And then maybe the best way to do it is actually go out of the organization.

BRIAN KENNY: Yeah. SALSA, how much autonomy did you have to make decisions to move things forward?

VICTOR ‘SALSA’ LOPEZ: Quite a bit. So normally as any good large organization has, almost every role in the DOD has what’s called an Air Force instruction attached to it. And we have inspections that go through of, “Are you doing everything that your job requires?” So the scheduling people we talked about with their big old whiteboard will have a checklist of things that they’re required to do, and they will have an inspection to ensure that all of those things are being done. I mean, you can in trouble if you don’t do them, right? And you’ll need to show how you’re improving. There was none of that for this job. There was no Air Force instruction. We were the first cadre. Our mission was to go accelerate AI, which is so broad, and massive. And so sourcing the right talent because it takes a special kind of person to go into that environment with no instructions, no real formal goal, so there was a lot of, “How do we make the best value in a short amount of time?” And balancing that with, “How do we start building these long-term, ideally relationships that are going to continue providing dividends back to the organization well after we move on?” And so lots of autonomy to keep it short, and lots of risk here.

BRIAN KENNY: Maria, the case talks about rewiring the workplace. This is another concept that I think broadly applies beyond just the DOD example. Can you talk a little bit about why that matters?

MARIA ROCHE: So this was really important to us to think about it because as I was just mentioning, when you have to change things, you’re doing something different from the source organization, you also have different goals, you have a different mission, you have a different vision in mind. And so the activities you do are going to be different from what the source organization does. So you have to think about a different way of getting these activities together, choosing the activities you do, but also how they align with each other.  And a way that you can think about it is really thinking about four components: people, process, product, and place, and kind of thinking about these different parts of how you want to structure them, and how you want to change that from how it used to be. So thinking about the people, right? SALSA already mentioned this, who do you hire? Are these different people from who would work or flourish in the more hierarchical setup? Right? And so thinking about that place, where do you go? Do you move away from the Air force? Do you locate closer university? Do you locate in Silicon Valley? Where do you go? Because you want to be close to that new information. If you’re trying to learn all about AI, you want to go where that’s happening, but you also want to be close because you want to be able to bring the knowledge back. And so it’s a very fine line and a trade-off thinking about how you do that. And I have other research that really touches upon that because technology adoption amongst startups, for example, sometimes it’s only 20 meters that matter in terms of learning from each other.

BRIAN KENNY: We’ve done several cases now. We’re doing more and more on AI, and how organizations are trying to plan for it. And the kinds of things that you just described are what many large firms are trying to figure out how to do, particularly on the talent side, because we know everybody’s going to need a new set of skills going forward. Not all the jobs that exist today are going to exist five years from now, or maybe even two years from now. So I think a lot of firms are trying to think about how to do this, and do it well, and do it in a way that I think is respectful of the employees that you already have. “How can we retrain people, get them to think differently about what they do?” So very interesting. SALSA back to you for a second. You’re very good-natured about this whole thing. It sounds like it was fun. I’m going to guess that it wasn’t always fun that there’s some challenges that you’ve encountered along the way.

VICTOR ‘SALSA’ LOPEZ: Yes.

BRIAN KENNY: Maybe you can describe a little bit about the road bumps.

VICTOR ‘SALSA’ LOPEZ: I think really in this role, I’m not selling anything at the AI Accelerator. The goal was to do research, but we did. I, at least, felt as though I was a bit of a salesman sometimes. I needed to explain the good and the bad that was going to come with adopting this new technology, and the technical debt that was associated with it. Let’s go back to that pilot scheduling problem. They required investment money, not just for the research, but then to actually put it into production, to actually go and say, “Hey, I need money,” which means I had to go back to the larger organization and sell this idea, but I’m not selling anything. I don’t make any money. And so that, I think for me, was particularly hard of finding that fine line of trying to explain what needed to be done, how much it was going to cost, why it was going to be better. And it wasn’t always tangible.

BRIAN KENNY: So it sounds like there’s a big educational component though. The burden is on you and your colleagues to figure out how to kind of educate the rest of the organization, or at least the people who are helping to pay for things-

VICTOR ‘SALSA’ LOPEZ: Absolutely.

BRIAN KENNY: … about why this matters.

VICTOR ‘SALSA’ LOPEZ: There’s a great cartoon that we always went back to. Maria knows, she’s already giggling. It’s a caveman, and they have this cart with square wheels, and there’s a scientist in the back with paperclips saying, “I have a solution for you. Look.” And he’s pointing at these round wheels, right? And the inertia, if you will, of the other caveman on the square wheel wagon is, “I don’t have time for that right now. I’m too busy.” That is how we-

BRIAN KENNY: Pushing the square wheels.

VICTOR ‘SALSA’ LOPEZ: Pushing the square wheels, right? But they’ve never seen a round wheel. They have no idea, right? And to the organization’s credit, there are going to be things in here that likely will not transition. There are things that are going to fail. Not everything is going to be a resounding success. So we need to be able to be very honest about where our pitfalls lie, where we think some of this technical debt is going to be, and be willing to accept those trade-offs that our senior leaders give us of, “This is something that we can incorporate today. This one, we’re going to have to wait.”

BRIAN KENNY: And you can learn a lot from failure too-

VICTOR ‘SALSA’ LOPEZ: Yes.

BRIAN KENNY: … as long as there’s a tolerance within the organization to recognize that, right? Maria, do you think it’s harder to do this in a government organization than it would be in a private firm?

MARIA ROCHE: I wouldn’t say it’s harder to keep it going, I think it may be harder to get it going. But once you’ve started the initiative, I think the government is very supportive, and does have deep pockets, and is very patient. But there is this issue, and this goes back to Ken Arrow, thinking about an underinvestment, especially in more tough technology when there’s high market uncertainty, also high-tech uncertainty, that what we see is that there’s an underinvestment, especially from the private companies, and this is where government can really help. But government can also really help, even if there’s market certainty. Technology maybe is kind of certain, but still requires a lot of fixed cost investment that they can help as a coordinating mechanism. So if you think about SEMATECH or the Rad Lab, there again, it was more coordinating the effort because there are many people who want to work on it, many people who want to help. There’s so many smart brains and minds out there that can make it happen, but how do you bring them together so that they’re not doing all of these tiny isolated initiatives? And that’s where the government can be really great.

BRIAN KENNY: This has been a fabulous conversation. I knew it would be, I expected as much, and there’s a lot at stake, right? I mean, we’re talking about the country’s national defense. I’m going to ask you each one more question, but, SALSA, I’ll start with you. Can you just tell us what the most important lesson is that you can take away from your experience at the Innovation Accelerator, and at AFWERX? What have you taken away from that?

VICTOR ‘SALSA’ LOPEZ: Having these very small focus teams that can, to Maria’s point, bring all of this together, and putting us in the right location, both physically, so that we can learn that 20 meters separation, and then connect back to the top of the organization. So don’t bury your innovation at the bottom. You won’t make it through the bureaucracy through the top. And so getting directly from your senior leadership, your C-suite, the intent so that your innovators can go and make that change. Getting the buy-in is really important. So don’t bury us. Give us the movement and the ability to understand, and put us in the spot where we can really connect with the people that are going to make it happen.

BRIAN KENNY: That’s great.

MARIA ROCHE: So I really want to stress that the Air Force innovates. That sometimes gets lost. And I think it’s really important to bring that to the forefront. So I hope that’s something students and instructors take away because this is also hopefully others will take the case, and teach this as well in their MBA programs.

BRIAN KENNY: Maria, I’ll give the last word to you. If there’s one thing you want people to remember about this case, what would it be?

MARIA ROCHE: Often we think that the dominant paradigm to do innovation is internally, that you have to do it all by yourself, but there are actually other ways to organize it. But it comes to the consideration of really like, “Do you need speed? Is openness important?” Versus maybe secrecy is more important, then you want to keep it inside. And so really thinking through these trade-offs, and that there’s not just one way of organizing innovation is what I really hope students will take away. And then when you’re setting up these vehicles for innovation, really thinking about people, process, product, and place, and how they reinforce the goal of the organization. Sometimes people decide, “We want these people,” or, “We want this kind of product,” but then it doesn’t fit together. And then that could have all been in vain. So really thinking about how everything aligns is incredibly important.

BRIAN KENNY: The four Ps.

BRIAN KENNY: Maria, SALSA, thank you for joining me.

MARIA ROCHE: Thank you so much.

VICTOR ‘SALSA’ LOPEZ: Thank you so much.

BRIAN KENNY: If you enjoy Cold Call, you might like our other podcasts, After Hours, Climate Rising, Deep Purpose, Idea Cast, Managing the Future of Work, Skydeck, and Women at Work. Find them on Apple, Spotify, or wherever you listen, and if you could take a minute to rate and review us, we’d be grateful. If you have any suggestions or just want to say hello, we want to hear from you. Email us at coldcall@hbs.edu. Thanks again for joining us. I’m your host, Brian Kenny, and you’ve been listening to Cold Call, an official podcast of Harvard Business School and part of the HBR Podcast Network.