It is safe to assume that everyone is aware of the importance and relevance of innovation and innovation scouting today. Therefore, the question went from “how important is innovation today?” to “how important is it to find a fitting startup?”.
On one hand, it is clear that companies need constant innovation and technologies, but on the other hand, it has been observed that companies need to collaborate with startups. It has become a matter of survival: if companies find relevant startups, they stay relevant on the market. If they do not, companies risk becoming the new NOKIA and lagging behind to then, ultimately, disappear from the market. So the question becomes, how do you manage to innovate by investing or partnering up with a startup when it has been calculated that one startup emerges every three seconds? Here are a few things to keep in mind.
Making decisions about startups is fraught with uncertainty. Moreover, positions are often prone to personal agendas, which results in the classic, traditional long cycle decision making, making an opportunity disappear. Therefore, having your company cooperate is crucial.
What has been observed is that companies lack the discipline approach to the startup sourcing side of discovery. In other words, a lot of attention is paid to the sourcing or contractual side of the partnership, instead of testing the depth of the relationship and aligning expectations. “A key item for successfully implementing an innovation strategy is to reach alignment of the stakeholders”, as Sander Van Der Blonk explained.
As early on as possible, have the right stakeholder team with the right background and authorization. Additionally, make sure stakeholders are aligned around the project’s scope and help the team to further perfect it. Not sure how to do that? Scoutely is a great resource. Now it is time for startup scouting.
There are great sources for innovation scouting such as databases. However, they mostly offer financial data. What happens once the information is not enough? Innovation managers need to do all the work themselves: find the right criteria, result filtering, vetting, due diligence, etc. This can lead to SAD Syndrome. On top of that, competitors find the same exact data only by entering the same keyword, even though two companies have unique strategies. How does Artificial Intelligence then add an extra layer to this process? AI provides information, insights about the actual activity of startups and filters out the results that are known to not be a match from the very beginning. Moreover, AI scouts more than just a name, website and financials. It scouts investors, employees, partners and customers, as well as content on their website or social media. This type of data is what creates a perfect match between corporate needs and startup offerings.
According to the webinar’s poll results, when asked how important it is for attendee’s companies to find a fitting startup, more than half answered “it’s strategic! We have specific projects planned”. It is of no surprise, since companies are slowly adapting their innovation strategies and adding them at the top of their priorities.
All of these ideas together, will bring an organization closer to the best startup match and, therefore, to the next best practice to stay relevant in the marketplace. Technologies change fast, therefore innovation strategies need to move at the same pace. Is your company in need of help? Is it at risk of becoming the new NOKIA or KODAK? Get in touch with us today or book a call with us.
Full transcript here
Hello everyone. Thank you for taking some time out of your day to attend the Novable and Scoutely webinar. I am Karim Hosny, customer success manager for Novable and I am joined today by Sander Van der Blonk CEO of Scoutely, and Olaf Maltha, Chief Product Officer of Scoutely as well on one side, and the third speaker will be Laurent Kinet, CEO of Novable. They will have the pleasure of talking to you today about innovation, and corporate venturing: the AI tipping point. The webinar has a very simple structure. There will be a small introduction by me, and then the speakers will proceed to tell you, how they see startup scouting and innovation. And we will conclude with a small Q&A session. So I would ask you to use the side tab that the app Livestorm gives to submit your questions at any time, and we will go through them at the end of the webinar. With no further ado, let’s get started.
I think it’s great to start with a question. So the question is not: how important is innovation today? I think this is pretty obvious and evident, but rather, when you are trying to innovate for your organization: how important is it to find a fitting startup?
Now to make this webinar, a bit more interactive, we will be publishing some polls that I invite you to answer, takes a few seconds. Again, to do this you can just use the Livestorm interactive side menu. I hope you can see it, and that you can answer it. The reason why we asked this question is simple. The trend is undeniable. And we see that the amount of corporate venturing deals that are backed by capital is increasing, and it’s not just increasing, it is peaking right now, as you can see, the difference between 2020 and 2021 is critical. This is probably also linked to the COVID crisis, and the need for digitalization. So on the one side, there’s more need to innovate and to bring new technology, but on the other side, we also see that it is really important to collaborate with startups, if you want to stay relevant on the market. If you do not, then you run the risk of becoming a new Nokia and lagging behind and ultimately disappearing from the market. This is backed up by research from notable institutions. But on the other side, there’s also the dilemma of having so many new startups a year. One, it has been calculated that there may be as many as one startup created every three seconds, perhaps more. So how do you combine the two, how do you manage to innovate by investing or partnering up with a startup, when there are so so so many that are created every day? This is the challenge that we believe you should be looking into.
So one, you should be ensuring that your innovation and investment strategies are aligned, which then is your sound, solid base to identify which startups match your needs to innovate. And from there, how do you establish a relation, great relationship with the startups. So, without further ado, I will let the guest speakers tell you what they know about these subjects, and the possible answers so sensitize you to how you can overcome these obstacles, these modern day challenges. So I will introduce now. Sander Van Der Blonk and Olaf Maltha from Scoutely, who will begin will take over from here. Thank you.
Sander Van Der Blonk
Thank you, Karim. Hi everyone. It’s great seeing you, although at this distance. Let me go straight in. So for the next five minutes, I’d like to talk about everyday realities for companies that want to collaborate with startups and, by that, I mean, working together with not owning a sale, and all of that, my father co founder will then elaborate on one crucial aspect in startup cooperation, namely, identifying signals and getting their buy in and how our platform helps you overcome some of the, of the hurdles in doing so.
As Karim said, we see companies, strive for kind of a healthy mix between building internal capability and sourcing external expertise, products, technology or talent from amongst others, early and later stage startup so, in some, startups have bottom line impacts. Now, we research– here at Scoutely– we research these days, practicing, we research the good, bad and even next practice and we know that working with startups is no easy fit to make it work but hey, I mean, even if you were to collaborate internally. There are also a lot of obstacles to overcome.
So, if you have a seminar, we would like to walk you through some of these obstacles and aspects we hear often, and how to go about this. First off, one of the aspects that companies feedback to us is well, we’re not sure when we found the best startups, and this is related to the fear of missing out but it’s also related to what we call the hit fit ratio, I’ll give you an example, a two year VC sees about 2000 styles for one investments and we reckon that’s about the same for collaboration, although it’s a bit dependent upon the engagement mechanism. And it is what it is to, uh to rephrase it a little bit, you have to kiss many frogs to find your prince so you better prepare for that. Related to this, is another aspect and that is not so much the challenge of finding status but it’s the challenge of finding the willing. Why are we saying this? Well, terms like showrooming or innovation see its limits for a reason. And a lot of startups have become cautious and picky, to our large enterprises. Plus, the real good startups, they have many options to choose from, so this is also something you have to prepare for. Well, this is the perennial issue plaguing open innovation, and we have it also working with startups. How do you get your organization behind you? How do you assure that people are really committed to working with external partners? And related also to this is that making decisions about startups is fraught with uncertainty. Positions are often prone to personal agendas and that results into kind of classic, traditional long cycle decision making and that, probably, the opportunity is gone, there’s, there’s a bit of research into how long does an opportunity last, and based on what we know. Only three to four weeks, mind you, that’s the speed of sort of decision making, required today. So, to conclude, what we see here, bringing startups in, triggers the immune system as bringing in all companies for any type of collaboration.
So how do you get your organization behind you? What we also noticed, is still a lot of companies lack the discipline approach to startup sourcing side of discovery, and the relationship side which follows. So the real engagements. And what’s more, we noticed that a lot of attention, a lot of time is spent on just the sourcing side, and maybe the contractual side, unlike testing the depth of a relationship and aligning the expectations. So if you read about Phil’s collaborations, then in 90% of these times has to do with this root cause is that you have not spent time on aligning expectation and properly get to know each other and each other’s needs. We have a simple piece of advice for you. If you’re serious about working with startups, if you feel there is a business opportunity to meet, then you better organize this as a business process, especially as there is now anecdotal evidence and I’m quoting McKinsey here.
Research done last year, and updated, the beginning of this year if I’m not mistaken, saying that companies that are into this game are working together with startups, and not just by acquiring a stake, majority stake or controlling stake, or maybe taking a startup over so simply device I’ve been really honoring with a startup, is that they if we see a shift taking place from having these exploratory programs. Many to many often outsource to an accelerator for example, or working together with startups in a many to manufacturing in your own lab or startup studio venture Studio One name you like to give there, a shift from exploratory programs to one more targeted. One on One programs, closer to the business. And you see then startups as a supplier or as a commercial auto, as a co innovator and the reason for it is, is really bottom line impacts, very fussy that targeted collaborations can deliver impacts, unlike the more exploratory programs. So, to do so, you need a process, what could the process be like, well, you can look at it from prior varying angles, this is our view, and it’s pretty simple, straightforward. So we believe you have to think through what I was searching what am I looking for, who would like to solve what kind of challenges do I have, who should be involved, then you embark on the startup discovery process using maybe Novable for that. And then we believe is an important step here is that you start up. Very simple sweet pilots they can run a few hours a few days, a few months if you’d like. That can be a proof of concept, a proof of value or building a capability prototype together, or doing a research experiment, and the outcome of which you take with you to make a calculated guess and make a decision on which startup collaboration, you really relied to continue scaling up to grow impact, because only things that scale, deliver impact, well, a more granular view of this is that in this process you will have ample decision points, saying, we go on or we don’t go on, Or we keep the startup for at a later stage. And the name of the game really is to decide with less risk. This this greenish area is where we, we help the process, making it more simple and more efficient. Or to put it differently, we help on how you deal with the Fuzzy Front End of collaboration. Now, let’s focus on this aspect of startup discovery, and I would like to invite Olaf to take it over. Elaborate on one important aspect here called stakeholder management. Olaf, over to you.
Thank you Sander and good day to everyone. So we identify these four different stages within startup discovery thing, things start off normally with some kind of inquiry from within the company or maybe from an innovation team, that that kind of started an initial scope, maybe a very rough one with a more general idea of what you’d be looking for. But it’s a very important stage, the planning and scoping stage where you, you really kick things off, I will be diving a little bit deeper into the stakeholder management part where we believe that it’s very important early on to bring in the right stakeholders, not just stakeholders but the right stakeholders, and make sure that they’re aligned around your scope that they help you perfecting your scope. And then the stage afterwards and we’ll be talking about this is when we believe that if you have a proper scope set up, and you have the right stakeholder team, you’re ready to start scouting and around we’ll talk about the challenges when you get to that stage and and how Novable, for example, can help with that through AI. And then there’s the last stage that we won’t be touching much upon today because it really stands a bit on itself and really want to focus on, on really getting started is the decision making phase where you vet and qualify startups, and really get to that decision point. So that makes it one of the key conditions to success within the startup discovery we see it as getting the buy in from the right stakeholders. I think we’ve all been part of projects in our lifetime that maybe after months or maybe even years of work, a stakeholder or that we didn’t know about, or maybe it was a stakeholder we actually did know about and it was already part of our project. At the end of a project maybe put the heels in the sand and put up a roadblock, it could be a logical roadblock, maybe a technology reason or a legal reason, but very often also, we hear about emotional reasons, reasons that could have been taken away if people were more aligned up front. And so things like the fear of stakeholders, maybe conflicting priorities, you know, it could be that that person sees a direct impact on their own work or on the work of their department. Maybe it pushes them to a sidetrack, maybe they don’t believe that the quality from external development can be as good as internal. There’s all sorts of emotional reasons that that can also come up that you really want to catch. And obviously, you also want to catch all the kind of contractual or the logical reasons up front as well. And so it’s crucial to bring in stakeholders. I think most innovation teams are most people working on innovation they know at least a few key stakeholders. Often they know a decision maker, they know one or two topic experts but the larger the organization, the more difficult it is to really identify the right stakeholders, if you know them directly within our platform for example we facilitate very easily to invite them, it’s very simple, you assign them a role, and you have a base, base team set up. But then how do you find stakeholders that you should know. So the ones that you don’t know about, but that could have a big impact on your actual search. And so this is where you need to spread the word throughout your organization somehow. Within Scoutely, we created kind of a preview link to your scope where people within your organization can see that preview and you’re gonna get an idea of what you’re working on. Just can be sent to managers of different teams or a managing director of a sister company, you can put it in a newsletter, and it’s an easy way to spread the word around your organization to have people be aware or become aware that you’re working on the scope, and that it might be of interest to them, and they can use that preview also to ask for access to your scope or to your stakeholder team and participate. And so it’s a great way to cast kind of like a wide network throughout your organization and find those stakeholders that you really should know about. Ideally, you can also get stakeholders obviously to find you. You know, if you have already an internal platform where you’re running your innovation. Maybe there is an overview where people can look at. For me at least, within Scoutely, we created this kind of Trello type board where you see this quick preview of the different scopes that a company is working on, and people that are already participating on the platform can easily, also through this way, find additional projects that they should be part or that they would like to be part of. So in the end when you’re targeting is a strong stakeholder team. You want to make sure that you have the right stakeholders, they have to have the right background the right authorization.
You want to make sure that they see the urgency for the scope, and you want to make sure that they feel accountable towards the project and so within Scoutely, we visualize this through a stakeholder map, where we map out these two parameters against each other, and you get a kind of an idea of how strongest my stakeholder team are there any outliers that I need to talk to or that I need to review, like it could be that somebody says, Well, actually I think this is not a very urgent project, so that’s a great indicator for you right, really early on in the process to start talking to that person and find out. Is maybe not the right person? Or maybe there’s something in our scope that they don’t agree on or that they find that we need to change. And so, with the use of the stakeholder map. We try to give you a good, good sense of how strong is your team. Do we have really the right team to start working. Now, Not only do you need to bring them in, you also need to give them a voice, and you want to create buy in from your stakeholders. So they will actually actively work with you, they will put in the time that is needed, and they will participate and make sure that the goals that you set out are actually reached and so within Scoutely, we facilitate this through a proposal mechanism where every stakeholder is asked as soon as they join, they’re asked to give their, their view on all the different elements of your scope. So you really start working together on perfecting your scope, and making it as concrete as possible, you can have some conversations on all the different elements of your scope. And so you make a very kind of like transparent process where you include everyone, and kind of create accountability also from your entire team, because they are part of the creation of the scope, they have had their voice, they automatically create to create some buy in from your entire stakeholder team, you make sure that you get out of the, the starting blocks properly and solidly. So once you believe that you have that straight and strong stakeholder team. You have the buy in on a at least, pretty, pretty decent scope, you know, we understand that we see that very often in the field as well that scopes, they start off and get modified over time because of new insights right you start working, maybe with a startup or you discover a new technology that you also want to include the scope can obviously change, but when you feel that it’s at least solid enough to be relatively specific on what you’re going to be looking for. We believe that you should start scouting, and that’s where Laurent will highlight some of the challenges there and how that can be solved.
Alright right thank you very much. To Olaf and Sander for sharing their insights on how to align your organization and get everybody on board before you start to establish a relationship with startups. Just like to let you know that there is a question for you that came in, and on, considering what you just shared with everybody. I would like to publish the second poll again for the attendees if you have a couple of seconds to answer it, we would like to know how important it is for you to establish working collaborations, for your innovation strategies, and otherwise I will not take more of your time, Laurent, please take it away.
Thank you Karim. Hi everyone and congratulations Sander and Olaf for insightful presentations. It’s great to see how stakeholders alignments and strategy formulation are so important to ensure successful innovation tracks. On my side I will cover what happens just after that, you know when you finally nailed what you’re looking for and need to find the most relevant startups to engage with. And as Karim said at the beginning, there are so many startups popping up everywhere, evolving and dying at a rapid pace. And it becomes so difficult to find needles in a haystack, not only because there’s too much of them out there, but also because the match between the need of the company and the real activity of the startup is the most important success factor. We could say that the intrinsic quality of a startup has little to do with its attractiveness to the company interested in. It’s the matching that matters. And this is why startup databases cannot fill the gap entirely. If we look back to a short history of startup scouting we see that the first step taken by companies towards startup venturing is serendipity, they rely on opportunistic encounters, you know, an employee meets a friend in a bar, knowing someone who knows someone etc., or just waiting for startups to contact them spontaneously. And when companies start structuring this activity they use network based activities like attending or organizing style of events, or inviting startups to hackathons or hiring consultants to do the job. While you still don’t have any technology at this stage. Except, email, maybe, so it becomes interesting when startup databases are being used. There are super useful to access a wide range of young companies, and you will find tons of information, like location, headcount, financials, turnover, funding rounds, actually a lot of financial data. And most of companies we meet at Novable are at one of those three first steps of this latter. Now, very few, actually, are taking advantage of intelligence, Even if we see a growing trend. The problem is that when you’re stuck with databases, you can only rely on data, all the rest, you have to do yourself, you know, ideatio, criteria. Results filtering, vetting, due diligence, and it’s of course, a massive workload. As I said, databases are super useful. I don’t want to criticize them too much because they lead the market and they are a key step in the value chain, because without data, we couldn’t add this intelligence layer. However, when it comes to innovation scouting, there are a few limitations. First, it’s a one size fits all solution. If you’re your competitor will enter the same query as you, you will get the same, he will get the same results as you. But of course, your strategy is unique. It’s like your DNA, so you need more granularity when working with strategy and innovation, second limitation already mentioned, they focus on financial data. But when you work in a company with an innovation related position you’re not like investment funds: you need information, insights about the actual activity of startups, you know, technology, methodology products, USP, models, and that you won’t find it extensively there. Third, last but not least, your search will retrieve long list of results, and you will have to browse them record by record to check if it fits your criteria or not. And even for high potential young analysts, it will take hours, fans and 1000s of hours.
This is what we have called the SAD syndrome, you know, professionals, spending 1000s of hours analyzing bad candidates that they will eventually disqualify itself, of course, and it also refers to spots, analyze, drop, and repeat. Another limitation with current ways of scouting startups, is that they disregard the fact that they are more than a name of websites, and a couple of numbers, startups are in a web of interactions with a lot of stakeholders, you know, funders, of course, but also employees investors, partners, customers, suppliers, and they also produce a lot of content on their website, on social media in the news in patterns, you have, you know, data is scattered all around the internet, and this data is truly a goldmine to create perfect matches between corporate needs and startup offerings. This is precisely where we have a qualitative leap in the startup scouting activity because we switch from a search to a context, thanks to AI. With a search, you must take the step, it’s a push effort. You lean forward a keyword and type a search or you take a flight or you hire a consultant firm, but with a context, we take the most out of the contextual data we gather about all startups worldwide, And this is the basis of the matching with Natural Language Processing, Machine Learning, Network Science, it becomes a full curation process where we can delegate to the machine, most of those tedious tasks while leaving you, the final cut. As an analogy, all things considered, of course, 20 years ago, the search engine industry experienced a similar leap when Google removed that database from the map, just by using website content and links between websites, instead of directories and taxonomies. So, the result of all this is that we can switch from generic lists, with little information to rank lists with information, details, and I say ranked because ranking is key here, because it’s what really brings value to scouting, and startups are sorted by the their semantic proximity or distance between the organization’s strategy and the actual activity and content produced by the startup itself. And this is where AI really brings value. In a nutshell, on the left, you’ll get access to data, and on the right, it’s the intelligence layer on top of this data that can turn a simple search into a contextual delivery. Now, how can you do that, it’s not the purpose here to make a demo. But this shows the fundamental difference between a keyword search. And the brief taking by the computer. We want the machine to properly understand the innovation strategy, just like a consultant, would do. So it’s a kind of artificial consultant, let’s say. So, in Novable, you first tell the computer which use case you’re working on. And this will adapt the algorithms accordingly. For instance, when you monitor your landscape, you will want to get broader results. And on the other hand, when you’re looking for your needle in a haystack, you would want only spot on startups in your list. And when you, then you can submit your briefing just like you would brief a consultant, as I said, in a meeting room with long texts, so that he can give you details, express nuances and develop the key elements of your search. And this briefing will be automatically and immediately vectorized, and through that process Novable identifies and suggest topics based on your briefing from the most popular topics and expressions used by similar companies in your target community. So this way, your search will use common terms and words used by this target community and therefore will increase the quality of the outputs. And next step in this brief-taking process is clustering very interesting parts, so they can, the computer identifies clusters of topics, and lets you weigh each of them to make your briefing the most accurate possible because the human brain is limited. So, the way you word your briefing might not reflect the relative weight of different topics so you have the opportunity to fix that here. And when you submit your campaign. The platform the AI retrieves the most relevant startups in real time, and you get ranked 100 lists, and we call those ranked lists, golden baskets. So you have your personalized ranking, where startups are ranked based on this semantic proximity score between your briefing and startups context, contextual information stopped. And then you can tag and comment each results so that future deliveries will be improved. It’s a pure machine learning at work, and you will also notice that instead of focusing too much on financials, the platform first displays activity data, you know, website description topics, Twitter, news feeds, and so on founders patents and other contextual data, likely to facilitate the decision making, whether it’s a good fit or not. And so the who’s making use of this.
Basically any innovation related role within an organization might run some status scouting activities, and innovation professionals are of course at the forefront of this mostly to ensure a constant feed of innovative ideas, inspiration, or potential threats, Or just to feed the innovation culture and internal R&D. So let’s say you were, you’re an innovation manager and your company’s strategy focuses on a couple of key topics or domains related to the future of your current business, then you would run multiple campaigns in parallel on each of those topics that will automatically scan the startup landscape worldwide spots initiatives, falling into your radar and calculates the proximity or the distance of these initiatives from your strategy. Another group using that kind of technology, are couple of development teams who have used lean also needs startup scouting for CVC, or M&A, often with additional services to go one step further in the process, including analysis and context with stakeholders. And because technology spares you some time, extends your reach and delivers super, super results, super fast, but you can also need some extra expertise from an extended corporate venturing team, who would manage the full process and deliver you with qualified candidates, after having checked the potential viability, financial health, and pre vetted real match with your needs. In an ideal scouting stack, and depending on your case, you would certainly need a smart combination of AI and human, human support. Next to that, procurement and purchasing teams, more and more rely on innovation scouting to find new strategic partners to solve new kinds of problems raised by business units, And we see rising interest from this function, towards AI powered scouting these days. And of course investment funds, venture capital is also interested in that kind of technology and not especially to identify or spots the next investment target, but also to keep a grasp on their investmentsenvironments you know their standard environments their competitive landscape, and other insights, so that they can help them during board meetings. And next to that we also have some unexpected case. As we know it’s the users who drive adoption and usage. And we encountered a couple of unexpected use cases, like for instance, people startups or SMEs, using Novable to monitor their competition which is great way to automate that kind of watch. We also have other companies using the technology to find twins, look alike searching perfect clones of a given startup just to wider the choices. We also have some innovation managers using Novable to check if an idea they had was already implemented by a startup somewhere in the world. And there is always a startup doing the same thing, you idea. And finally, we even have some cases where the scouting technology is used to retrieve qualified leads for sales or even marketing activities. So to conclude, I, I believe that the most interesting parts of using AI, which corporate venturing are first, the fact that we can delegate to the machine, most of those tedious tasks are usually done by humans, and the machine is never tired, never sick, never out, works 24/7 at constant speed, and second, the fact that the scouting reach is by default, with AI, unlimited. Both with uncertainty which, with much more startups than before. And also, particularly with much more contextual information for each of them, looking at a time. Okay, I think I’m done. Thank You for attention and happy to take your questions
Yes, thank you Laurent for also sharing your insights on how to do, how startup Scouting is going to change or is changing already the, the landscape and AI is boosting. These activities this innovation activities. Before we start with the Q&A, I would like to ask if you have a question, by all means, go ahead and use the side menu to ask them if you can just give me. If you want to address this specifically to a person, we’d have three questions that have come in. But before I start asking them, while we get ready for those. I’m going to publish a third poll, which is going to ask you, how do you currently find startups, this is a very simple poll. We hope you don’t mind answering this one as well and thank you for your, your contribution. So far, so to go to the questions. The first one, I was asked while Olaf was presenting, and it, but I’m not sure that I don’t know who this question is for.
But before I start asking them, while we get ready for those, I’m going to publish a third poll, which is going to ask you: how do you currently find startups? This is a very simple poll. We hope you don’t mind answering this one as well and thank you for your contribution so far. So we should go to the questions. The first one was asked while Olaf was presenting. And it’s, but I’m not sure that I don’t know who, who, this question is for so if it’s for Sander or Olaf but the question is, how do you normally lead this discovery process, and I believe it’s about when you were talking about the process of…
Sander Van Der Blonk
Of rushing green, I think, okay, thanks for asking the question is who do you see normally lead the discovery process. Well, the obvious answer is it depends. It depends the, the governance in a company, some companies are very centralized other companies are very decentralized, their company is very centralized. Most probably they also like to centralize processes related to finding fascinating qualifying startups, either organize it for inbound flow solicited or outbound flows using scouting companies so have a single point of contact. And we see, depending on the kind of company we see some companies really organized for this in a central way. And there’s one person leading a team or one or two Scouters or VC developers. And this person has a role from this development lead to start off leader to open innovation leader, up to strategist, even though we’re a very decentralized company, We see this discovery process taking place on many, many instances can be at a geographic national basis, it can be international. You can organize as with outposts. People in outposts have this process. So, what we learned over the years, it is more important to, to look into this question of centralization decentralization. Plus if you organize for this. It has the, it has to do with the mandate. The person has to lead this process, is this person only responsible for spotting startups, or is he really responsible for driving business outcomes. It’s a matter of how you slice and dice the discovery process before you get, hopefully that answers the question.
Maybe there will be a, a follow up question to that, we’ll see. I have another question. This one is for Laurent from Sebastian. He’s asking: you mentioned a startup scouting stack. Would this be technology only or something else?
Well, that’s a great question. Like Sander, I’d say, mostly depends on your personal situation and company structure and goals. But basically, as I explained, I believe that even if new technologies like AI can bring very valuable benefits and speed up the full process with unprecedented reach and quality. It might not be enough to fulfill all needs when it comes to startup innovation scouting. We have met at Novable quite a lot of companies who are curious about our technology, but are definitely more interested in the output itself. I mean, AI is a process, a means to an end, a tool, a capability, and quite a lot of innovation professionals are even more interested in what AI can deliver so they ask us to bring them results to use our technology ourselves on their behalf. And in a way be an extension of their of their team. So I would say that a good scouting stack is made of the right tools, and of the right expertise through a smart combination of both. This is how we think we can, we can bring value faster and better than with technology only or always expertise only. I hope I answered your question.
That was clear to me. I have two more questions, so thank you everybody for your patience. The first one is for Olaf, from Kevin. He thanks you for the presentation and he asks: how do I get my organization on board with my innovation strategy?
Okay. Good question as well. I’m also going to say it depends. As Laurent and Sander also kind of said, obviously depends on how big your organization is and what stage you’re at, but I guess in general what we see is that people try to go too fast, too big, at once and kind of cause conflict within the organization or frustration within the organization. So I would actually say to start light, and just start on a scope and start working with the people in your organization that, that are willing that are willing to work on innovation that are willing to work on the scope that, that understand what you’re what you’re really doing. And I think I personally, you know, you’re kind of dealing with change management I guess if you’re really new to this to this type of process into this type of startup discovery, I think, Personally I believe a lot in in transparency, and I think that’s also the key in in change management is. It’s also kind of the sort of the base of Scoutely to be transparent and to create that transparency and and get people involved and have them be able to voice and that way kind of pull them in instead of like putting things in their face and confronting them and, and only asking things from them but just try to draw them into the conversation and into the decision making, make them part of it, and take away any doubts that they might have, point them to maybe also very concretely like what are you trying to achieve. It’s very important. That’s why we say focus upfront on the scope and have the right people on board and together make clear, what are you really trying to achieve and what is the impact for the company also there potentially. So I think. Yeah, start, start light, be transparent and be concrete I will say,
Sounds like very good advice to me, Olaf, thank you. We still have two questions. The first one is from Nicholas and he asks: for Novable, so for Laurent, how are we applying AI to our search process?
Yeah, well I think it’s not the purpose here to dig into technical details and architecture, basically, the Novable technology is based on two main foundations or two main pillars. The first is data, of course, so we have a stack of code and algorithms, scraping the data, analyzing it through a gating system and vectorizing all the content and data we can gather about two startups. So we have volumes to define if this startup, this is an innovative startups, it still exists, and all that kind of things. And then it’s vectorized with the contextual elements. So data in one part, and on the other. On the other hand we have a bunch of different goals, mixed tax, mostly with natural language processing for the matching but also machine learning for improving the matching campaigns themselves. And we use different techniques and algorithms to improve that matching and continuous improvement basis so technically I think I, it’s not the place to dig deeper on that but if you want more information about the AI as such, we can provide that to you.
Great. And last one, almost, almost finished here. Lionel asks a very philosophical question, I think anybody who wants to join this discussion, should. He asks: if there is no startup in a domain is it maybe too early? Is it possible to predict the potential creation of a startup in a specific scientific/technical domain based on papers, patents, market attractiveness, mindset of university? I do not know if this is a joint effort from both Novable and Scoutely.
I can quickly answer that, I guess, what we can do already is, of course, drawing some insights and statistics and conclusions and predictive insights on what will happen next in the very near future based on the current actual rolling activity of startups and applied there are a couple of predictions based on the actual data. But for now, at least at Novable we don’t use scientific publications and academic reserves to do that kind of thing. Even if we will soon as patterns and an academic articles but not to predict what will come next to identify the rights, the rights initiatives and the right startups, but I guess it is it will be possible, at least, to to run some tests on that, but it’s not what we are doing right now.
And I do not know if this is going to be a welcomed question but personally, from what I just read from Lionel’s question which I think is very interesting. How does Scoutely see this potential so right now there’s a focus in the process of establishing the relationship but what about helping in creating when there is no such activity yet? Just throwing it out there but
Sander Van Der Blonk
Yeah well just just a short comment I really liked the question. What we learned is that there are two things first. More often than not, enterprises do not know what they search for what they look for. So they like also this notion of serendipity and also the process that long explained I think this is a great way to, to become acquainted or to become familiar with startup that you probably never thought about. And the second thing is is that a lot of startups and up in a playing a different game compared to how they start, so you never know how to frame the space that they’re in. So I’m not sure whether you should define spaces, or text spaces or application spaces. The real name of the game here is to come to a kind of a scope or a kind of an idea of what you’re seeking. And then let’s as Laurent said let the machine do the work because you never know what you will encounter.
And maybe I will try to close this last philosophical concept by saying, indeed, I think it’s the challenge also to let the creativity flow and innovation needs creativity so it might be a met backfire, to try to categorize things that don’t exist just yet, as they might end up shaping corporations as well as startups. I think this is all we have, I thank very much all of our speakers, and all of our, all of the attending all the participants, thank you for joining us and for your time. You will be receiving a follow up email, just after the webinar.