“A [startup] program gives a very clear collaboration platform to engage with startups. It's a clear gateway that corporations can create to send a signal to the startup ecosystem, that they are ready to collaborate.”
In this episode, we welcome Paolo Lombardi, author of the book ‘Startup Program Design’ and Director of Artificial Intelligence Innovation at Taylor & Francis. Together with Paolo, an expert in AI and startup engagement, we delve into the essentials of effectively partnering with startups, exploring various engagement models such as venture funds, hackathons, accelerators, and incubators.
Paolo shares his unique journey, starting with coding at age seven, and how his early experiences shaped his career in technology and AI. He emphasizes the importance of choosing the right startup engagement program, avoiding common pitfalls, and aligning corporate goals with startup innovation. Paolo also offers insights on fostering productive relationships between corporate managers and startup founders to mitigate cultural clashes and maximize collaboration success.
This episode is packed with practical advice for corporates and startups looking to navigate the complex landscape of innovation partnerships and drive impactful results.
Below you will find the full transcript for the episode.
The impact of AI and technological evolution
Chris: Effectively partnering with startups is a key strategy that can make a real difference for companies. Corporate venture funds, venture clienting, hackathons, accelerators, incubators; there are many ways to engage with startups, and it's hard to choose the right one. The four most important reasons for choosing a program and the biggest mistakes to avoid when designing such a program: That's what we'll discuss in this episode. Let's go. Paolo, you wrote your first line of code at the age of seven. Let me ask you the question: How does a seven-year-old boy get to write code? And can you recall what you wrote?
Paolo: Well, I can absolutely recall what I wrote. And it was quite basic, in two senses. So I got given my first computer. It was a VIC-20 from Commodore when I was seven. And my first instinct was to play games. But my dad wouldn't allow me unless I wrote some code. So, the program was something like 10 PRINT “What is your name?” and 20 INPUT A$ or $A - can't remember the string now if the dollar was before or after - and then it would say PRINT “Hello”, “Your name”. But it was like the beginning of what became a bit of a story because I was allowed to play games only if I was coding. So at one point, my dad found this trick of giving me a book of games so I could actually copy the basics into the computer and then run the program and it would be a very basic game, very quaint and you know, like there was an asterisk moving around and you have to shoot at it. But it was a way for a seven-year-old to get introduced to programming. I think it was quite smart of my parents.
Chris: Yeah, it was very wise. So the motivation for you to play games, of course, is there, but you were only allowed to do it when writing code. That's very nice. You know, I can relate to this. My very first programming language was QBasic, where you can select 16 colors and then you can basically think of yourself how you can create a game from that. So that was a fun time.
Paolo: Yeah. And I was doing programming challenges with my friends because a friend got a Commodore 16 that absurdly enough was an upgrade even though it probably had less memory, I don't know, and so we were doing coding challenges. Can you do this color or write this thing…yeah, those were the days.
Chris: And how did that lead you to what you're doing today? I mean, coding at a young age, I mean, again, it's one of the best things probably you could do for understanding informatics and even the language to some extent. But today is all about leading innovation in different forms. So what lessons did you transfer from your early days to what you're doing today?
Paolo: That's a great question. I think, well, it's been an evolution. Technology is clearly an amplifier of human capabilities. So that was clear from the beginning for me. I started my career in technology; actually in optics as an electronic engineer. So it was a calling to go back to coding and AI later, which is what I do now. And I understood that the ability of optical communications, so fiber optics to improve communications, was already there. And I was playing only the fine tuning of it. I believe that it was exactly that calling to make technology a lever to improve someone's ability that brought me to AI in the end. I think the idea of technologies and amplifiers has always been there. The wheel made people go faster on chariots and, I mean, computers made us better at computing, at just calculus and creating big projects with CAD, etc. Now AI is going to be a new challenge in many senses. It's going to improve our ability to imagine things. Generative AI is proving it. And there is a saying that if you can imagine something, you can produce it. Sometimes the biggest step is really imagining a different world. So I'm very excited by the evolution of AI and generative AI for that aspect. Like every other technology, it also takes the ability to jeopardize something that we do. I mean, steam engines and electricity changed how people use their strength. And there was much more space for human strength before machines came around to do things that we were doing or horses were doing for us. And the same will be true for artificial intelligence. We just have to learn how to govern that, which is another thing that comes from coding, I think. As a coder, you have full control of the machine. Sometimes the machine doesn't respond to your controls or does something a bit different than what you expect. And yet, if you're careful enough, you're always the one in control. That, at a societal level, is a bit of a difficult lesson to learn, but we will need to learn it. We have done it with nuclear power and nuclear bombing. That is a hot issue and will always be. But I'm confident that we can do the same with AI.
Chris: So there are many models on these larger technological changes and even disruptions. One of the models is called the Kondratiev waves. Because you mentioned the steam engine, it's believed or it's said that the very first of those larger cycles, called Kondratiev cycles, was indeed the steam engine. The second one was, I guess, railway and steel, then electrification, chemicals, then petrochemicals, automobiles, and then already IT, ICT, if you will, and the cloud and the like. And there is an ongoing debate if AI is now starting the sixth Kondratiev wave, leading to a new world, if you will. There are other voices that say it's going to be nanobiotechnologies and things like that. Nobody knows, but it's what you say. The important part of this is that the time between two Kondratiev cycles is getting shorter and shorter. So where you would have about 50 years in the first cycle of the steam engine, you actually just have 25-30 years for the fifth one, for the IT one, before the next one starts. So it's an interesting discussion about the compression of time. Now, where you are today, you interact and help big corporates in this innovation jungle, if you will. I'm not saying theater, but in this jungle, in this very complex world to navigate, specifically at the intersection of startup collaborations and startup engagements. Now, in the corporate world, when does setting up a startup partnership program make sense? And based on your experience, what difference can partnering with a startup actually make to a corporate?
“Partnering makes sense when the technology that you're looking for is soon to be a commodity.”
Paolo: Two very good questions. I'll start from the second. So it makes sense to collaborate with startups in the innovation cycle for a corporation. It can be related to innovating the core, or it can be related to innovating in adjacent markets, or the transformative horizon. You know, the model of horizons 1, 2, or 3. So it actually makes sense in all three horizons. In the first horizon, when you're innovating in the core, you always have the decision between build, buy or partner. And partnering makes sense when the technology that you're looking for is soon to be a commodity. Maybe it's not a commodity today. Very few people or very few companies have it, especially some startups have it, but you don't - but it will be a commodity. It will not be a part of your core value proposition because also your competitors will get that technology. In those cases, I believe it totally makes sense to partner with a startup. So that you acquire fast that technology. You start building layers that create your competitive advantage on top of something that will be a commodity at some point. I think generative AI today is in that stage. Yes, there will be a number of players, and Cloud was kind of the same. It doesn't really make sense to build your own Cloud; it's just to buy a Cloud from a producer. Similarly for AI. So that is one scenario. Another scenario is when you are exploring a market where you don't have a presence yet, but you are interested in. Organizations are machines with standardized processes that work well exactly because those processes have been tested and standardized and then can be repeated at scale. Entering new markets sometimes involves changing those processes or changing the value proposition or changing the entry strategy or the marketing strategy, that's when you want to collaborate with a startup that already has a foothold in that new market. It gives you time as a corporation to understand that market, restructure your processes, and understand what kind of changes you need to make in order to enter that market. And the partnering could also be, you know, the rent-to-buy model for houses. It could be a partner-to-buy model, right? So when you want to partner with a startup, because eventually, you want to buy it and enter that market through that branch that you bought. So that also makes a lot of sense. And another, third case I would say is when the startup develops a need for your core market. Let’s say you are a Cloud company. When developers of Cloud technologies saw a possibility in the adoption of Cloud, they created programs for startups to help them use the Cloud in order to grow their business so that the demand for Cloud technology would grow. And it worked. So that's another possibility. So when you have a platform technology or any other kind of platform that would grow, if that gets used, then you want to engage with startups and increase the demands for your core business. And basically everyone gets a win; the startup goes faster thanks to the technology that you're offering and at the same time, your core business grows thanks to an increased demand. So yeah, I think I'll stop here; there might be other examples but these examples also map pretty well on why you may want a program. A program gives a very clear collaboration platform to engage with startups. So it's a clear gateway that corporations can create to send a signal to the startup ecosystem/ the startup community, that the corporation is ready to collaborate, that has created a process for it, which is really important. And it has a budget for collaboration. So it creates all the steps that would spare time for a startup to engage with the corporate, which otherwise is like a massive investment for a startup. And a program that creates that avenue lowers the barrier to entry for startups. And it also helps the corporates, if the program has been designed correctly, to speed up internally all the processes that get to realizing the goal. And that is also why I believe there are very different programs for very different goals, depending on what the corporation wants to achieve.
Startup engagement strategies
Chris: And it's interesting, many of our customers are building those external engagement programs, also with the help of a digital platform. But none of them looks like the other. And I guess you’re also cautioning against this, right? In your book, you’re cautioning, for example, against a copy-paste approach in setting up those startup programs, startup engagement programs. So the obvious question would be, why shouldn't I just look at the most successful approaches out there and do exactly what they did?
“If you copy-paste exactly one of the famous models, that might bring you towards those same goals as those who created the model, but that wouldn't advance your own goals or your own needs.”
Paolo: Another great question. I think the answer is partially what I said before. Organizations are very different. They are a sum of people, processes, industry, competitors, as well as goals, objectives, and strategy. So someone's strategy might be different from yours. Someone's people might be different from yours in terms of what background they have and skills they can offer to startups and vice versa, what they can take from startups. So that boils down to different needs. So if you copy-paste exactly one of the famous models, that might bring you towards those same goals as those who created the model, but that wouldn't advance your own goals or your own needs. An example is with corporate accelerators. That is one of the most evident things that happened in the corporate world. I think accelerators are quite successful in the investment world because of an alignment of incentives of what happens in there. So startups join an investor-led accelerator to grow faster. The investors have money to do it and a network to allow them to allow startups to raise their next fund, financial rounds. Because accelerators are typically created by entrepreneurs or investors with a large network of fellow entrepreneurs and investors ready to put money in, and they can activate their network for you. So it is kind of a growth trajectory for the startup. What corporations want from corporate accelerators is rarely that a startup raises the next round. I mean, that could be a fun thing for investors, but it's never the first goal, or very rarely so. So corporate accelerators had the same model as investor-led accelerators, but the goal was completely different. It was to create a long-term partnership with startups. And then the means to achieve that goal were wrong, because they were the same means as investors, but the goal was completely misaligned. And that is what brought a lot of corporate accelerators to fail, at least in the corporate sense of failure. I think history will still say that a lot succeeded in other respects. For example, in growing startups, and startup ecosystems, but not in gaining tangible revenues or cost savings for companies. So what we see in corporate accelerators is that the model has been twisted by, or repaired, as I say also in the book, by different players who changed some parts of the model in a way that actually works for corporations. For example, the goal is not to raise the next fund but to close a deal with the corporation. The content of the corporate accelerator should be changed accordingly. It's not about mentoring for raising funds or changing your business model, but mentoring for how you sell to this corporation or corporations participating in that specific program, and how you change your sales process. A lot of startups start with a business model that implies a sales model, that is maybe mature for selling to corporates. And within a couple of months or three months, they can be made to realize, I didn't want to say taught, but that's actually teaching in a sense, they can be taught how to change their sales model so that corporations can actually buy from them. And it doesn't take three years, it takes literally sometimes even two weeks or four weeks for a startup to change its model. But the realization of it can happen within a program. And so that is one of the reasons why I think you don't want a copy paste model that is made by others. And there are other situations where sometimes your people are not ready. So you want a lot of interaction from your people with the startups that some other companies don't have to do because they already have a good kind of startup procurement process that they put in place. Whereas you want to create that startup procurement process. And so you need to educate your own people to do it. And I could mention probably another two or three, but I think I'll stop here for the moment. If you want, we'll drive the conversation in that direction again. But yeah, I gave a couple of examples. There's really a tailor-made process that is required because of the complexity of a corporation. You need to see where that complexity lies and make the program solve that complexity for you. That is one of the reasons why you want a program; you want to create that sandbox, that avenue that solves the blockers in creating a startup engagement and collaboration for you, right?
Chris: That makes a lot of sense, and I guess it goes down the path of goal alignment, but also choosing the right tool for the right goals, obviously. There are many of these “tools or startup programs”. You mentioned, for example, accelerators. There are also incubators, corporate venture funds, and many more. And surely each program is unique in the way they might satisfy goals, they work and they should be designed for a specific purpose or a specific goal. But are there also some common features among them that allow for effective design management or also improvement over time?
Paolo: Absolutely. There's a design space available in startup programs. And the design space comes from these common features: duration, for example, or the structure that starts with our recruitment phase, there's always an activation phase where the interactions happen. And then there's a follow-up phase that many people don't design for, which is one of, I think, the major mistakes that first-time startup program designers make. They don't design the follow-up stage, where a lot of the value comes from, especially for larger organizations, corporations, and governments. So there's first of all this structure that is common to hackathons - that is the shortest possible program probably –, to incubators, or corporate venture funds, that have a very long follow-on, even like 10 years after the investment or the first relationships are curated. There's a big commitment in follow-on because there's an investment. The other common thing is the content. A lot of content in the activation phases is similar. You have mentors in hackathons, you have mentors in incubators, you have mentors in accelerators. And in a sense, you have mentors also in corporate venture funds because it's people who sit on either the advisory board or even the board of directors (BOD) and they help direct the companies by coaching or mentoring the founders within that scope of the BOD. The other things that are in common, well content again, is you could have workshops, you could have lectures, etc. So all these ingredients can be shaped. Duration can be short. A hackathon is really a very short accelerator if you think about it, or vice versa, an accelerator is a very long hackathon. There's always a selection stage, sometimes it's between the recruitment stage and the activation stage only, and sometimes it's at the end, what I call the graduation. So the first is the selection stage and the second is the graduation stage. You have another selection over there where you graduate the startups and you promote them. Sometimes you have an intermediate selection stage where at the beginning you have a phase one of the program and then an intermediate gate and it's a stage-gate model really. And then you have another stage and then another gate. Quite common with innovation processes that were replicated in startup programs. All of these are features that you can tweak and change, depending on your goal. The offering can change, and that is a marketing problem. If you want to attract the right startups for you, you need to create features inside the program that resonate with that market. If you want Series B companies, for instance, in your program, and you ask founders to relocate to your city for 12 months, you will never get Series B companies because founders there are fully booked. They're really busy and they need to stay near their market wherever that is. They will never commit 12 months to relocate to your city. So if you design the offering in a way that basically closes doors to the startup market or the startup community that you want to attract, that's a very major design mistake that many people do, unfortunately. So all these things can be changed and designed in a way that you have a product-market fit or a program-market-fit that attracts the right startups that you want to attract that match your goals and that you can also cater with some advantages to make them win by participating in the program.
“One of the major mistakes that first-time startup program designers make is that they don't design the follow-up stage, where a lot of the value comes from, especially for larger organizations, corporations, and governments.”
Chris: So it's probably very hard to generalize. But in what sort of situations and goals should you lean towards which type of engagement program? And I realize it's not black or white, and it's probably multifaceted, but still, there might be some common pathways when, for example, choose an incubator versus an accelerator versus a corporate fund, maybe. Can you sort of walk us through the typical scenarios?
Dan: Right at the beginning. What do I mean by right at the beginning? We're trying to measure innovation with what we typically use for the core business. And it's absolutely impossible for, and again, probably this is no news for everybody that worked in startups. It's impossible to report on the same KPIs day one as the core business is doing. If you say, hey, we are a car manufacturing company. We've been building cars for 100 years. I don't wanna name the name of the company, but we know who's been building cars for 100 years, right? We've been building cars for 100 years. We know absolutely every single dollar that goes in, what's gonna happen with it, and how much we get back. If you start a business, even within that company or outside that company today, you won't be able to report on the same things. Other things might be more relevant for you in terms of accounting for your progress. And venture capital knows that very well. Investors know exactly what to look for and identify whether or not they are making progress without using financial KPIs. Obviously, as your business grows, you will start to need more financial accounting. But this is going to be a gradual process in which you dilute the innovation indicators and add more financial indicators up to a point where your business is huge and you can only manage it and you will only manage it through financial indicators. But early on, financial indicators won't do anything. And to give a more practical example here, Think about starting up a company now. If I would come to you and say, hey Chris, tell me what is your ROI, or tell me what is your return on net assets, or tell me what is, I don't know, any other financial indicator to be honest with you, customer lifetime value, and all these kind of things. You'll be like, dude, I just started my company last week, so I don't know. However, if I come to you and say, Chris, tell me how many customers have you spoken to in the past week since you started the business and have confirmed that they have this particular issue? This would be an indicator for which you're going to have very relevant data and very important data for me to make a decision about your business. If I want to invest in it, if I trust you're on the right track and so on and so forth. So you see, just the way we phrase the conversation and the indicators we tend to use is going to shape my perspective of your business. If I use financial indicators, I'm going to be super disappointed in your business. Look, you don't know what your customer's lifetime value is, and you don't have any ROI, what the hell, right? This is not a good business. But if I ask you how many customers have confirmed a problem in the past week since you started and you say, look, I send out a newsletter to 1,000 people, and out of those 1,000, 900 came back saying that they want to buy the product. All of a sudden, I believe that your business is definitely on the right track and you should continue investing or I should come in as an investor into your business.
Chris: Yeah, so it's the right indicators at the right time. They can be a key performance indicator. There can be some other indicators. So that's a good point. That's actually a good point. So let's think about innovation KPIs from the perspective of different levels of innovation maturity. as you mentioned before. Can we go through them step by step and simply define what level of innovation maturity, what kind of makes sense to measure, and at what stage for a comprehensive overview?
Paolo: Yes, it is challenging because, as we said before, it's kind of a tailored process where you want to create the program that works in your situation. But what we identified in our research that is published in the book is that there are four major long-term reasons. There are even short-term reasons why you might want a program. But the four major long-term reasons do map quite well on some characteristics. One reason is you care about the growth of the startups, and that is investors, typical investors because investors want an exit. So the faster the startup grows and the better it is, that really aligns with financial return. In that case, accelerators make a lot of sense, because you push a lot of resources in a very short amount of time, you attract startups that want to grow fast, and at that point you have the founders, in the program who are the main decision-makers, who can change the strategy, the approach of the startup, the product, have the decision power to make it. So you condense everything for them in a very short amount of time, be it three months, six months, two months, whatever, and so the accelerator model makes a lot of sense. If instead, your goal is the procurement of a solution, a technology that you don't have, or access to a market that you don't have, you have another kind of need. There, you need to create a match with your goals and with your internal processes. You don't forcibly need founders there. So that is a new breed of programs that came out about 10 years ago now, or eight, six years ago. They're called in different ways, in Germany; there's venture clienting that has been created there. And in other regions of the world, they're called collaboration programs or collaboration platforms. But essentially, they don't require sometimes the founders even to attend. They're not as intense as accelerators. They have very different content. It's more about how you create the collaboration. The output is a pilot. It's doing something together rather than pitching to investors or raising your next fund. It's actually about testing the performance of the technology within the context of the corporate or the government that runs the program. So that's another breed of program. And then you have a third situation where the corporation or government, so a large organization, aims to create an ecosystem around something that I call a platform in a different sense. So it could be a technology platform. I gave the example of Cloud technology before. Corporations who created that technology want that technology to be adopted and they want to create an ecosystem of startups that use that technology. It happened when Microsoft and Nokia tried to catch up with Apple in their App Store, where they created the same ability for startups to code for Windows, and then they wanted to do Windows for mobile, and then they wanted startups to create apps for that. It was called App Campus and ran, I think, in the early 2010s. There are a lot of programs like that. Also governments do it. Startup Chile is one example of an ecosystem program where you have a platform that is the business ecosystem of Chile and you want startups to adopt it and actually do business in Chile or have a base there for international operations and so on. So you have this platform that is somehow ready to be adopted and then you just bring startups in. There, what you want to do is to incentivize the usage of that platform, right? So it's not about raising funds, it's not about a pilot, but it's about creating a lot of ties and locking in somehow the startup, but in a good way. It should be a win-win, right? Showing the value of that platform so that the startup commits to that platform. And there, incubators work pretty well because you want the duration that allows for those ties to happen. And then the fourth kind of program is when you care about the founders more than the company. So even if the company doesn't go well, but the founder develops a transformative maturation or framework in their mindset so that they then carry that on and maybe in their next company they succeed. There you have pretty much a wider choice but in terms of models. But that's another very big thing. And think of all the educational programs for students, entrepreneurs, or for early-stage startups that even governments put money in. That sometimes happens also with corporations. Corporate social responsibility is a driver. You have this transformational goal that can be achieved by even hackathons, but accelerators that are really intensive and again require the founders to attend. So you have that component where a lot of content can be delivered in a short time that works really well for a transformational impact on people.
Chris: So you definitely need to be aware of your audience or the startup audience that actually want to engage in; we were talking about the time problem founders have and being part of this. And there is help, of course. I got to know that the Startup Program Strategy Canvas, if I'm taking the right name, but at least some sort of strategy canvas, is a tool that is front and center of your specific methodology, for example. Could you break the canvas down for me just a bit and how you would use it in a practical way?
Paolo: Yes, in the book we had at the beginning like five canvases and we were making canvases like a lot of people do; too much actually these days in our opinion. That's why we have only one, but the most significant thing that sometimes people get wrong is the strategy around the program. You can do a lot of tactical design, you can decide to have a workshop on design thinking instead of having one on, I don't know, programming or coding or whatever. That's really tactical design. What people get wrong is the fit between three major points. One is your organization, so who you are, and what you can offer to startups. That's really key. If you are a manufacturer, what you can offer is very different from when you are a communication or marketing company. The other thing is your objectives and goals. I've covered them with examples before. If your goal is creating a collaboration with a startup, don't do something that investors would do. You would attract the wrong startups for what you want to achieve. So you need to clarify your objectives really, really well. And hopefully also your KPIs. And then the third circle in our canvas is what startups you can actually reach. So what is the startup community around you that could participate in the program? If you establish that that community is a local community, you can put in the program features like attendance or local attendance that could work. But if you understand that the startup community that will solve your goals and would profit from what you can offer is not local, then all of your program needs to be remote. This is a simple example, but there are some other decisions in your design that depend on what kind of startup community you want to attract. What is your target market for startups? We're missing one from the Ikigai, the Japanese four circles of who you are, what you love, what you can be paid for and what the world wants from you, right? And this is the reason to exist in Japanese culture. And it's the reason to exist for a program as well. So if you sort out those three circles, and in fact, the fourth could have been sustainability. So what you can be paid for is sustainability. But in fact, in a corporate environment, that kind of coincides with your objectives. So if you deliver on your objectives, you make the program sustainable. So we collapse two circles into one. So we have only three.
“What people get wrong is the fit between three major points: your organization, your objectives and goals, and what startups you can actually reach.”
Chris: Yeah, that makes a lot of sense. And you're probably not only specifically pointing towards environmental sustainability, but really long-term sustainability, which comes with success.
Paolo: Exactly.
Metrics, KPIs, and measuring success
Chris: Talk to me about metrics. When designing such a startup program, how can - and again, it probably depends on the type of program, but still - how could you use some early metrics or maybe even KPIs to show that you're on the right track with this, that they are aligned with your innovation goals or the purpose of the program and actually report impact? I mean, sure, if you're, for example, a corporate venture fund and your goal is, I don't know, 10 or 15x, well, of course, a KPI would be that 10 or 15x for sure. But there might also be leading indicators where it makes sense to check in and say, well, are we on the right track with this prior to the ultimate outcome? So what would you do in terms of metrics and KPIs for the programs?
Paolo: I'll start with one definition of intelligence. One of my favorite definitions of intelligence is that you're intelligent if you're context-aware, and you change your decision-making process and your decisions based on the context where you are. And that applies to KPIs too. KPIs are the key to open your context. So for me, a KPI keeps everyone aligned - stakeholders, participants, the project team, and the program team - on what can be achieved and should be achieved. You want to choose your KPIs based on what you want to achieve. It shouldn't be the opposite. You shouldn't compare your corporate accelerators. That is one of the major mistakes that I believe have been made historically with accelerators. Comparing your corporate government accelerators with the same metrics as an investor-led accelerator. You're not trying to achieve the same goal, so you shouldn't use the same KPIs in terms of how much fund is being raised at the end of it. And that applies to all programs. If you have clarified the objectives, typically the KPIs follow quite easily. I'll give you an example. So you actually said it correctly. So if you have growth as an objective, like in investor-led programs, then early traction can be a leading indicator of that 10x or 15x that you will eventually get. And another indicator is your ability to raise funds. So the volume of funds you raise within the first 6, 12, or 24 months after the end of the program is an early indicator of that 10x, 15x exit that will happen at some point. If you're trying to achieve a core innovation in your core business, whether that comes from, you know, assumed to be commoditized technology or an improvement. You're not interested in that. What you want to see is an early performance improvement. That's why a pilot is usually a good tool, because you want to measure your baseline of what you can achieve today with your current process or your current technology, and you want to see a percent improvement in the pilot on that performance that is key to open up the revenue stream, for example, or get you a competitive advantage to gain more traction then later in the market. So performance is a good KPI for solution-sourcing kind of programs. For ecosystem-building programs, your final goal is retention. So you want a startup to adopt your platform and stay with it, whether that's Cloud computing, or a local business ecosystem, or a co-working space. So you want people to adopt that. So usage is typically a good sign. So if a startup burns through those credits that you gave them at the beginning of the program very quickly, that's a really good sign. So you want to optimize that. You want them to understand the value that they're getting from the platform. Interact with the platform a lot. So measure the interactions. I summarize within usage a lot of different possible KPIs that could be an instantiation of that. With the transformational impact, what you want to see is that people actually apply what they're learning. So they're good learners. For example, in a course, you want them to run a lot of interviews or a lot of build, learn, measure cycles so that they actually get that as a way of thinking, a way of working, reframing the world in that way of experimental attitude towards business building not just as a scientific methodology and so on. So the number of cycles of build, learn, measure is a very good early indicator of success for the transformative impact kind of programs. It really does depend. But the one thing that is really difficult in corporations is to realign all your stakeholders that that is the right KPI for you. And that needs to be done possibly even before opening the first call for startups. If you don't have that big alignment with your internal stakeholders, then people don't understand what the program is doing, and obviously, they will start doubting the value, right? If you identify the right KPI that works in your context, so again, like you're intelligent, you're smart, you're context-aware, and you work out the KPI that aligns with your internal stakeholders, and you try to deliver on that KPI, that is going to deliver value for real. Sometimes, it takes time to discover that KPI or group of KPIs. So what you want to do is, I think you suggested in the question, to evolve your KPIs in time. So the first few programs you want to learn; so you have KPIs for you to learn. For example, can we attract startups of this kind with this kind of offering? So a KPI for you in the first round of the program could be the number of applicants. You just want to see if your marketing offering, your marketing campaign, your brand are appealing to that startup community. It could be the number of applicants in general, and it could be the number of applicants in target. So the kind of startups that you want actually to attract. But that can be a KPI that is good for the first round, but it becomes a vanity KPI if you keep it for the second and third rounds as well. In the second and third rounds, you want to evolve that KPI into something more concrete. And it's a learning process, depending on what kind of program team you have, the experience of that program team; that also has an impact on the KPIs. In the beginning, the program team might be learning as well. So, you want to put objectives they can achieve that can be leading indicators of success later. When the program team is more experienced and they don't have to focus anymore on marketing or attracting startups, they can focus on, the pilot that needs to work. So now in round two, you want to show me that performance increases and you want to show me that you can select the right startups, and sometimes it's not only the program team, but also the internal business unit that needs to collaborate with startups. So putting maybe a performance objective on round one is very aggressive, but keeping that in the background for round one could be a smart thing. But in round two, you probably want to put them forward and say to the engineering team that needs to collaborate now, you know guys, now you need to commit resources and make the pilot work and show that performance that we need for the business to achieve our goals. So it is smart to fine-tune the KPIs as you go, especially if it's the first time you adopt a program.
“A KPI is something that keeps everyone aligned - stakeholders, participants, the project team, and the program team - on what can be achieved and should be achieved.”
Cultural alignment and collaboration
Chris: It's a very comparable thing that we see for other corporate innovation vehicles, so not startup engagements, for example, but even internal innovation efforts, corporate innovation efforts. When a new corporate innovation unit starts, oftentimes, you start measuring around how many people we can engage internally? How many ideas can we collect? Things like that. And after the first year, into the second year, questions start around business impact, for sure. What does it do to the business? What is actually contributing to the business? So your KPIs change over time. And there is a point in time where you need to be confident about the results you can deliver and that there are people you engage, which could be an early KPI, but then later, obviously, it is aligning with the goals and then delivering on that. So that's the quantitative side of it. Another side is culture clash. So of course founders of startups and managers and seasoned executives in corporates, they tend to be different. So there could be a clash in language, speed, vision, being driven by a vision, even goals, obviously. So based on your work, what do you think are some of the mechanics of fostering a productive relationship between the people of startups and the corporates? And how could you help align and lower the consequences of such a culture clash?
Paolo: That's such a big problem in organizations because a lot of people who work in large organizations stay there and they're not in startups or they're not entrepreneurs themselves. They are in big organizations because they like predictability, and they like organizational structure. And we're all different. Some people foster when they can focus on what they can do and do best rather than worry about all the little fringe problems that, for example, a startup founder has to solve, right? Like refocusing costs, and brain power, as we all know. So people who work in large organizations typically are the kind of people who strive in an environment where they can focus on what they can leverage the most in terms of their skills. Moving those people out of their comfort zone is typically more difficult than for other kinds of personalities. So it's not good or bad, it's just the way it is, right? And the ability of a program to change that is usually correlated with showing results and showing that startups can help. That is the most powerful thing. And the leading kind of trailing indicator towards that is the time spent in contact with startups. So what I try to do when I have a cultural objective, is to optimize the quality and the quantity of time spent between corporate managers and startup entrepreneurs. One thing that worked out really well in the past for programs I was involved in was creating mixed teams, from a hackathon to an accelerator, where you have a corporate manager who is actually part of the startup team for a while. They try to solve the same problems, they act as advisors, and sometimes they even get a stake in the game. For example, a part of their MBO, management bonus, is tied to the success of a pilot. Or they even get a share because they become an advisor in the company. So they get a zero point something, which should also be tied to some kind of incentive in terms of career inside the corporation that is more concrete with employees typically. So the ability to spend time and actually live the life of a startup founder is something that worked quite well. Changes the reference point, the framework of people, puts them a little bit out of their comfort zone, not too much, because they are not pushed out to become entrepreneurs, but they live like entrepreneurs for a while. And that is a nice way of doing it.
“What I try to do when I have a cultural objective, is to optimize the quality and the quantity of time spent between corporate managers and startup entrepreneurs.”
Debunking myths about corporate-startup collaborations
Chris: Yeah, that sounds like very practical advice. And if we would debunk some of the myths in that vast landscape, what would be the one misconception that you wish to dispel, maybe for both seasoned entrepreneurs, but also younger ones, when it comes to collaborating with corporates? Certainly, there are expectations and ideas when going and when starting a collaboration with corporates. But what are some of the myths that we should debunk today?
Paolo: Well, I'll start with one that was told to one of my colleagues recently, and he resents it a little bit. The corporates are dinosaurs. And I mean, it's true that corporations are delivery machines. So they deliver around their core business. And they have a responsibility because they have clients, right? And they have shareholders if they are public companies. So they need to deliver on that business model, on those processes. Is that a sin? No, it's not a sin. You know, we buy food every day that is produced by corporations. That's actually really good for our everyday life and our health. And we take drugs that are delivered by corporations and the drug needs to be always effective, right? So again, that's really good. It's a backward culture. A lot of times, there are incredible employees with a great vision who even like collaborating with startups, they follow the technology landscape, and they would like to do things. It just takes a lot of time and a lot of internal, not politics, but, you know, games to realign a machine that is delivering towards A to also deliver towards B. I mean, ambidexterity, you know, like exploration, exploitation. These are all models that in the past have tried to describe this situation, which is just a reality. So it's not that corporates are dinosaurs. They just need time. Programs can help because if they're designed well, the program team has already done the homework of realigning part of those processes in order to be startup-friendly and then participating in a program, especially an established program that has already shown some traction and results after the first one or two rounds, is a good thing for startups because that collaboration has already been streamlined for them. There are many other myths, I think. For example, corporates are organized. Sometimes they are, especially in delivering the value proposition. Sometimes people really are in the silo. Meaning, that if you're a good employee, you try to do your job well. And if you try to do your job well, a lot of energy goes into that thing, your job, right? So getting out of that job takes some time, and then it's disorganization. So getting out of that organized tunnel that is your day-to-day activity can be very disorganized. So in that sense, pulling people out, and changing a process can be very disorganized. And that's another myth. Startups need to be a bit patient. Sometimes even pulling the jacket a little bit can help. And providing from the startups, providing that organization. So be present at deployments, arrive prepared, doing some homework for what the corporate managers should have done in theory, but come on, they are also busy with their day-to-day job and their MBO and objectives. So if the startup helps the corporate do its job, that typically is a win-win for everyone.
Chris: It is. And also, know your numbers. That's something we've seen a lot in those discussions between corporates and startups. Because the last sentence you said is spot on, right? It's of course helping the corporates solve a very important part of the puzzle. But when it comes to these interactions, yes, those people in the corporates as the founders of the startups are super busy. So if you're in those early stages, you want to understand if you gain some traction, if you get some whatever results from the metrics and KPIs you're measuring, you should better know your numbers and not be in a meeting like, oh yeah, sure, sales, well, let's pull up some of the recent dashboards and take three minutes to find those numbers. It's really about knowing your numbers, which helps a lot in those situations.
Paolo: Absolutely. And especially when corporates need to prove the value of the collaboration, the data is fundamental, of course.
Chris: Paolo, this has been a fantastic conversation. It's very, very insightful. I feel there is much more we could go through in greater detail. But I think it's a good time to mention that if you want to learn more about this in greater detail, we will make sure that we link the book you've written in the show notes - it's a very good one. And it details much of the conversation we had, which we unfortunately could only go through on a very superficial level. But still, to me, this has been very, very insightful. But before the conversation ends, Paolo, we do have a new tradition on this podcast. And the tradition is that the previous guest leaves a question for my next guest without knowing who my next guest is and what we're going to talk about. So there's a question for you and it's an interesting one. Take your time.
Paolo: Let's go. You have told me it would come, but obviously I don't know the question, so let's see.
Chris: No, I just received it this morning from my previous guest. So the question that's being left for you, Paolo, is: “What is the vision of your future self?”
Paolo: That is a very interesting question. Had I thought that, I would probably be my future self as well. I think there's always a challenge in imagination. I think I could ask generative AI to try to design for me a few future self-scenarios based on where I stand now, and then choose among them. That would be an interesting exercise I haven't done yet; designing my vision of the future self with AI. It could be a challenge for everyone. I'm also challenging the audience to do it. I believe that different people take satisfaction in life from different sources. My personal one, given my personality type as well - I'm pretty much a workaholic. Also because my satisfaction mostly comes from the impact that I have. And one of the reasons for writing a book is really try to reach out to a large audience with some of the teachings that don't only come from my experience but also from the 100 plus people we interviewed for this book. So the vision for the future self is trying to find the levers that I can pull with my humble skills and from my humble perspective and the point where I stand to improve that impact in a possible way to impact as many people in a positive way, as many people as possible in the planet as well, which becomes more and more evident than needs help. And we're navigating our future all together for probably the first time in history that we, through globalization and globalization of pollution, we are now on the same boat, clearly lost in the universe, and we need to collaborate. So enhancing these collaboration opportunities and the culture of global collaboration, and it goes with developing entrepreneurial ecosystems and developing AI as well, that I hope I can contribute to in the future.
Chris: It does. And let's see where we are in 10 years. Very, very interesting, though. So that's it for this episode. Paolo, thanks again so much for being my guest. It was a pleasure getting the insights. I learned a lot today and I hope everybody did as well. So thanks for being my guest.
Paolo: Oh, thank you for having me here and for the very interesting questions. Some of them were not easy. Thank you very much.
Chris: And to our listeners, if you haven't subscribed to this podcast yet, please go ahead and do so. My promise to you is that we will continue to do our best to deliver exceptional value at no cost to you. So if you're not already a subscriber, please subscribe now. Thank you for listening. Take care and bye-bye.
Related Links
Check out Paolo's book we talk about in this episode: https://www.startupprogramdesign.com
About the authors
Dr. Christian Mühlroth is the host of the Innovation Rockstars podcast and CEO of ITONICS. Paolo Lombardi is author and Director of Artificial Intelligence Innovation at Taylor&Francis.
The Innovation Rockstars podcast is a production of ITONICS, provider of the world’s leading Operating System for Innovation. Do you also have an inspiring story to tell about innovation, foresight, strategy, or growth? Then shoot us a note!
