One of the key drivers of economic growth nowadays is innovation, and it involves substantial investment in research and development (R&D). While general tech companies have made a global impact in improving the quality of life, deep tech startups are redefining the concept of innovation and are dubbed the ‘next wave of global disruptors’.
Deep tech startups are based on advanced scientific advances and high-tech engineering innovations. They have attracted unprecedented traction across all sectors and their impact is being felt everywhere.
From blockchain to advanced Artificial Intelligence (AI) to advances in biotech and medicine (picture cancer-detecting devices and fake drug detectors), this tech has the potential to solve global pressing issues and change lives, for the better.
However, building a startup that thrives on deep tech requires a different playbook due to the nascent and complex nature of these technologies. In this post, we’ll discuss the steps and scientific processes involved in establishing and commercializing deep tech startups.
Akin to the ideation stage in general tech startups, the discovery stage is what lays the foundation and basis for deep tech.
Discovery is all about identifying a need that cannot be solved by the existing technology. For example, a cancer research scientist might discover that a certain type of cancer cannot be treated with chemotherapy and conceptualize new ideas about tech that could solve the problem.
The discovery phase is an important part of the design thinking process and it aims to generate, develop, actualize, and communicate ideas. As fun as this stage may sound, it’s not without challenges.
For instance, the idea might be rejected on the basis of novelty. At times, cognitive bias might kick in, causing other parties to reject the idea even without further consultation. To overcome this, it’s great to stay clear of the goals and objectives of the tech in mind and take into account every variable that might affect the introduction of such technologies.
But the discovery process isn’t exclusive to deep tech startups. Many processes that require prior planning, such as the SQL server blocking, also starts with the discovery phase. In this context, discovery entails gaining an in-depth understanding of your data systems to build a migration plan. Likewise, discovery in deep tech startups entails identifying the key pain points and gathering market intelligence that will help to support your idea.
2. Advocacy & Screening
Advocacy and screening help to weigh the idea’s potential benefits and challenges. These two processes take place simultaneously and help to squash ideas that lack potential, which is easier than having them rejected by stakeholders solely on the basis of their novelty.
This phase is very important when assessing the potential of deep tech startups for two reasons:
Deep tech startups require a significant amount of capital to develop and scale. A recent Hello Tomorrow survey published by BCG revealed that developing the first prototype in biotech on average costs around $1.3 million. While deep tech has far-reaching potential, many startups seek funding in the early research phase—long before the prototype is unveiled to customers, leaving investors with no KPIs with which they can evaluate the product’s market potential.
Additionally, deep tech lacks third-party standardization, which again makes it hard for investors to assess the risks or potential returns, since there are no comparable products in the market.
However, screening helps to ease the investor’s burden in many ways. Researchers in the Innovation: Management, Policy, and Practice study identified refinement as the core advantage of advocacy & screening. If the idea has potential, advocacy and screening can help to refine and enhance it, making it more attractive and understandable to investors.
(ii) Growth Capacity
Sometimes, researchers are stymied when approached by potential investors because they’re unclear about the growth potential of their projects. Advocacy and screening help to map out the project’s future prospects and all the needs that will be addressed.
3. Research and Development (R&D)
The R&D phase is what distinguishes deep tech startups from general tech companies.
This stage encompasses experimentation and testing and lots of money is spent on design and engineering. The amount of time dedicated to R&D varies from company to company though it’s significantly longer than the time needed to develop an innovation based on existing technology.
According to data from the deep tech startups surveyed by Hello Tomorrow, it takes 4 years to develop a technology in biotech. Some advanced technologies can take longer, to the tune of 50 years. For instance, it took decades to develop the underlying technology behind AI.
During research and development, multiple experiments are done to determine the product’s feasibility. At times, the development phase can lead to new ideas as more information is gathered, and many elements tested.
The development phase has changed dramatically over the years due to the advancement of technology which has led to the introduction of robust design and prototyping tools. For example, 3D printing and computer-aided design tools have revolutionized prototyping, making it an easy and straightforward process.
At the end of the R&D phase, comes commercialization, which is the process of bringing high-tech innovations to the market. Commercialization is not a straightforward process and can be broken down into several phases, which include:
- Initial introduction
- Mass production
As you move through each phase, you’ll receive customer feedback and may need to refine or improve the product to meet the customer needs.
However, the commercialization process is marred by many challenges. Besides funding and lack of third-party standardization, commercialization of deep tech faces many challenges some of which may hamper its widespread adoption
For instance, since the tech is new to the market, commercialization is often hampered by lack of matching business infrastructure and human talents. It can be difficult to educate the partners and even the public when you don’t have the right resources.
Moreover, humans by nature resist change, and it can be hard for people to embrace what they don’t understand. This explains why training is critical to the success of the commercialization phase and it can also be stymied by lack of skilled personnel.
In addition to marketing challenges, industrial and cultural barriers may also thwart commercialization of deep tech. If the tech results in environmental pollution or violates certain religious or cultural beliefs, it may be antagonized by the public.