It can be difficult to find collaboration between large companies and startups. They are the odd couple in the business world. However, major challenges can require bold solutions as companies work to define the best way to improve the process. Successful companies develop innovation models and systems that suit their circumstances and reflect their corporate strategies. You will draft a mandate for innovation programs and ensure clear communication of the goals, priorities and parameters of these efforts.
This process includes defining the innovation goals, the resources required, creating a work profile for preferred partners, and ensuring the issues on which the company’s R&D efforts are clearly defined. In the past few years, some suggestions have emerged that we are at the beginning of a new cycle that could advance R&D for the next two to three generations, or that we could move from the end of a cycle, a “deployment phase” – which The main aim was to create applications based on existing information and communication technologies – up to the beginning of an “installation phase” in which new technology infrastructures are set up.
For this transition to be successful, companies need to understand how to work effectively with startups to understand the challenges and opportunities that deep technologies can offer. Startups are uniquely positioned to explore areas like biotechnology, artificial intelligence (AI) and quantum computing because they are nimble, agile and creative. However, translating that value into a pragmatic corporate giant requires a happy marriage that understands and appreciates each other’s worth. To illustrate this point, consider the three core attributes that define deep tech in a business context: impact, time and scope, and investment.
Innovations based on deep technologies can generate tremendous economic value, but their ultimate impact extends well beyond the financial realm to a wide range of areas such as human wellbeing, sustainability and infrastructure. Deep tech also takes time to move from basic research to applied solution for actual use cases. The time can vary significantly depending on the technology, but it is almost always longer than the time it takes to develop an innovation based on a widely used technology (think of a new mobile app). After all, the funding needs of deep tech companies vary significantly depending on the technology. Several factors make this investment difficult, including market risk and technology risk. Deep tech investors have few or no KPIs that they can use to evaluate traction and market potential. In addition, depending on how specialized the knowledge required may be, ensuring the required expertise and continued skill adoption can be a major barrier.
Before starting partnerships with startups, companies need to consider how they want to interact with startups, where the power to make decisions lies, whether to act and react as quickly as startups expect and require, and what types of KPIs to use to assess progress. Adjusting the “hard” side of the organization – governance, processes, and KPIs – is not enough, however. Company and startup values, cultures and goals are different. The companies in charge of working with startups may need to immerse themselves in the corporate culture to better understand what startups are trying and the challenges they are facing. This way, the company representatives can see the startups as valuable partners to advocate for across the larger organization.
Deep technologies have the potential to make dramatic improvements over technologies currently in use. However, it will take massive investment and effort to bring these technologies from the laboratory to the market. Almost $ 60 billion was invested in the fastest growing sectors of deep tech in 2018. Of that, $ 18.6 billion was biotechnology, $ 14.5 billion AI, $ 11.2 billion photonics and electronics, $ 8.0 billion robotics, and $ 5.5 billion $ 839 million for blockchain and $ 123 million for quantum computers on advanced materials science. Around 4,000 deep tech startups in the US made up about half of that total investment, but other countries are quickly catching up. Between 2015 and 2008, the average annual growth rate of private investments in deep tech was 10% in the US, 47% in the UK, 73% in Germany, 81% in China and 103% in South Korea. Globally, private investments had a CAGR of 22% over the same period.
Organizations can take six steps to take a leadership role in shaping deep tech ecosystems:
1) Cooperate to Compete: Think beyond the company’s immediate goals. Commit to a long-term vision for the development of the entire ecosystem;
2) Identify features that add value: Define what the company can offer to nurture the ecosystem and bring deep technology to market – not just money, but access to customers, data, networks, mentors and technical experts.
3) Do not pre-select the winners: Deep tech sectors are developing rapidly. Continuous monitoring of the ecosystem to identify successful startups, applications and business models as they emerge.
4) Blur the lines with partners: Make it easier for deep tech partners to navigate your business system. Define a clear role for them in your innovation strategy, ensure that executives are sponsored and incorporate the core businesses.
5) Rationalization of Decision Making and Governance: Success requires a faster partnership with fast-paced startups. Embrace agile ways of working;
6) Find what you’re not looking for: Develop breakthrough solutions by combining expertise from previously unrelated areas or industries. Look for breakthrough opportunities that offer both economic and social value.
Working together can seem difficult, especially if you plan to outsource some of your software development. The opposite is the case. Cross-border collaboration itself is a great opportunity to experiment with cross-functional team management. Why? Because you can expand your internal employees with the missing specialist knowledge – software engineers. By taking on different roles on your part (e.g. product management and sales), you can create a more complete list of requirements for the project, ensure a more even distribution of the workload, prioritize different tasks, facilitate knowledge sharing, and avoid bottlenecks due to lengthy approvals Process and accelerate the transition for all new team members.
The scientists and entrepreneurs working in deep tech aren’t deterred by big problems – or the time and effort it takes to solve them. In fact, these problems are part of the attraction for many. Curbing climate change, feeding eight billion starving mouths, and sustaining an aging population are career-worthy challenges – and large markets that are attracting a lot of attention from startups, investors, and companies alike.