Kangaroo Court: Partnering with Deep Technology Startups

Association of Certified E-Discovery Specialists (ACEDS)
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Association of Certified E-Discovery Specialists (ACEDS)

Finding collaboration between big companies and startups can be tricky. They are the odd couples of the business world. However, big challenges can require bold solutions as organizations work to define the best path for improving process. Successful companies develop innovation models and systems that are suited to their circumstances and reflect their corporate strategies. They design a mandate for innovation programs, ensuring the clear communication of the goals, focus, and parameters of these efforts.

This process includes defining the innovation objectives, required resources, establishing a working profile for preferred partners, and ensuring the topics on which the company’s R&D efforts are clearly defined. Over the past few years some suggestions have emerged that we are at the beginning of a new cycle that could carry R&D for the next two to three generations or that we may be shifting from the end of one cycle, a “deployment” phase – which has been mostly about building applications that are based on existing information and communications technologies – to the beginning of an “installation” phase in which new technology infrastructures are constructed.

For this transition to be successful, organizations need to understand how to effectively partner with startups to understand the challenges and opportunities deep technologies can provide. Startups are uniquely positioned to explore areas like biotechnology, artificial intelligence (AI), and quantum computing because they are designed to be nimble, agile, and creative. But translating that value to a pragmatic corporate giant requires a happy marriage that understands and appreciates one another’s value. To illustrate this point, look at the three core attributes that define deep tech in a business context: impact, time and scale, and investment.

Innovations based on deep technology can generate enormous economic value, but their ultimate impact extends far beyond the financial realm to a broad array of areas, such as human well-being, sustainability, and infrastructure. Deep tech also takes time to move from basic science to applied solution for actual use cases. The amount of time can vary substantially by technology, though it is almost always longer than the time needed to develop an innovation based on a widely available technology (think of a new mobile app). Finally, the funding needs of deep tech companies vary significantly with the technology at hand. Several factors complicate this investment, including market risk and technology risk. Deep tech investors have few if any KPIs with which they can evaluate traction and market potential. Additionally, securing the required expertise and continued skills adoption can be a huge barrier depending on how specialized the required knowledge can be.

Before partnerships with startups begin, companies need to think through how they plan to interact with startups, where decision power resides, whether they can act and react as promptly as startups expect and require, and what types of KPIs would be applied to assess progress. However, adapting the “hard” side of the organization – governance, processes, and KPIs – is not enough. Corporate and startup values, cultures, and goals are different. The corporate individuals assigned to work with startups may need their own immersion in entrepreneurial cultures so that they can better understand what startups are trying to do and the challenges that they face. In this way, the corporate representatives will be able to see the startups as valuable partners to be championed throughout the larger organization.

Deep technologies have the potential to deliver dramatic improvements over technologies currently in use. But massive investment and considerable effort will be necessary to bring these technologies from lab to market. Nearly $60 billion was invested in deep tech’s fastest-growing sectors in 2018. Of this figure, $18.6B went to biotechnology, $14.5B was invested into AI, $11.2B towards photonics and electronics, $8.0B to robotics, $5.5B to advanced materials science, $839M to blockchain, and $123M to quantum computing. About 4,000 deep tech startups in the US accounted for roughly half of this total investment, but other countries are catching up fast. Between 2015-2018, the compound annual growth rate of private investment in deep tech was 10% in the United States, 47% in the United Kingdom, 73% in Germany, 81% in China, 103% in South Korea. Worldwide, private investment for the same period had a CAGR of 22%.

Companies can take six steps to take a lead role in shaping deep tech ecosystems:

1) Cooperate in order to compete: think beyond the company’s immediate goals; commit to a long-term vision for the development of the ecosystem as whole;
2) Identify capabilities that add value: define what the company can offer to nurture the ecosystem and bring deep technologies to market – not only money but also access to customers, data, networks, mentors, and technical experts;
3) Don’t pick winners in advance: Deep tech sectors are evolving rapidly. Continuously monitor the ecosystem to identify successful startups, applications, and business models as they emerge;
4) Blur the boundaries with partners: Make it easy for deep tech partners to navigate your corporate system. Define a clear role for them in your innovation strategy, ensure senior-executive sponsorship, and engage the core businesses;
5) Streamline decision-making and governance: Success requires partnering more nimbly with fast-moving startups. Embrace agile ways of working;
6) Find what you are not looking for: Develop breakthrough solutions by combining expertise from previously unconnected fields or industries. Be alert for game-changing opportunities that deliver both economic and social value.

Collaboration can seem like a tough act, especially if you plan to outsource some of your software development. Yet the opposite is true. Cross-border collaboration itself presents a great opportunity for experimenting with cross-functional team management. Why? Because you can augment your in-house people with the missing expertise – software engineers. By engaging different roles from your end (for example, product management and sales), you can create a more complete list of requirements for the project, ensure more even workload distribution, prioritize difference tasks, facilitate knowledge sharing, prevent bottlenecks due to lengthy approval process, and speed-up the transition for any new team members.

The scientists and entrepreneurs working in deep tech are not put off by big problems – or the time and effort it takes to solve them. Indeed, for many, these problems are part of the attraction. Mitigating climate change, feeding eight billion hungry mouths, and keeping an aging population healthy are challenges that seem worth dedicating a career to – and big markets that attract a lot of attention from startups, investors, and corporations alike.

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