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‘Deep Tech’ has become one of the most powerful use cases for A.I. in business. Here are 3 keys to making it work

Pfizer BioNTech vaccine laboratory
Technicians working at a BioNTech COVID vaccine plant in Marburg, Germany. The rapid development of the vaccine was an example of 'Deep Tech' in action, the authors write.
Boris Roessler/picture alliance via Getty Images

In early 2020, when scientists rushed to develop a vaccine to take on the SARS-CoV-2 coronavirus that causes COVID-19, it seemed like a really long shot. The fastest a vaccine had ever previously been developed was for mumps, back in the 1960s—an effort that took 48 months. Still, just nine months later, in December 2020, the American pharmaceutical giant Pfizer and a German deep-tech startup, BioNTech, had developed the first COVID-19 vaccine, validating the use of the new technology of mRNA-based vaccines. 

The first studies on DNA vaccines began 25 years ago, and the science of RNA vaccines too has been evolving for over 15 years. One outcome was mRNA technology, which required the convergence of advances in synthetic biology, nanotechnology, and artificial intelligence, and has transformed the science—and the business—of vaccines. Pfizer generated nearly $37 billion in sales from the COVID-19 vaccine last year, making it one of the most lucrative products in the company’s history. 

Like Pfizer and Moderna in the pharmaceuticals sector, several corporations in other industries—such as Tesla in automobiles, Bayer in agrochemicals, BASF in specialty chemicals, Deere in agriculture machinery, and Goodyear in rubber—are relying on deep technologies. Deep Tech, as we call it, is the problem-driven approach to tackling big, hairy, audacious, and wicked challenges by combining new physical technologies, such as advanced material sciences, with sophisticated digital technologies, such as A.I. and soon, quantum computing. 

Deep Tech is rising to the fore because of business’s pressing need to develop new products faster than before; to develop sustainable products and processes; and to become more future-proof. Deep Tech can generate enormous value and will provide companies with new sources of advantage. In fact, Deep Tech will disrupt incumbents in almost every industry. That’s because the products and processes that will result because of these technologies will be transformational, creating new industries or fundamentally altering existing ones. 

The early prototypes of Deep Tech-based products are already available. For instance, the use of drones, 3-D printers, and syn-bio kits is proliferating, while No Code / Low Code tools are making A.I. more accessible. They’re opening up more avenues by which companies can combine emerging technologies and catalyze more innovations. Unsurprisingly, incubators and accelerators have sprung up worldwide to facilitate their development. Not only are more Deep Tech start-ups being set up nowadays, but they’re launching successful innovations faster than before. 

It’s risky for CEOs of incumbent companies to count on a wait-and-watch strategy. They need to figure out ways to tap into Deep Tech’s potential right away before their organizations are disrupted by them—just as digital technologies and start-ups disrupted business not so long ago. Unlike digital disruption, though, the physical-cum-digital nature of Deep Tech provides a golden opportunity for incumbents to shape these technologies’ evolution and to harness them for their benefit. 

Established giants can help Deep Tech start-ups scale their products, which can be especially complex and costly for physical products, by leveraging their expertise in engineering and manufacturing scale-up and by providing market access. And because the incumbents are already at the center of global networks, they can also help navigate government regulations and influence their suppliers and distributors to transition to infrastructure that will support the new processes and products. Doing so will unlock enormous value, as the Pfizer-BioNTech case exemplifies. 

Most incumbents will find that Deep Tech poses two stiff challenges at first. One, it isn’t easy to spot or assess the business opportunities that the new technologies will create. Two, it’s equally tough to develop and deploy Deep Tech-based solutions and applications, which usually requires participating in and catalyzing collective actions with ecosystems. To manage the twin challenges of Deep Tech, CEOs should keep in mind three starting points. 


Despite its sophistication, conventional technology forecasting produces linear predictions and siloed thinking; it doesn’t account for how technologies change and converge. As a result, most forecasts underestimate the speed at which technologies evolve and when business will be able to use them. That’s why companies should use “backcasting,” the method outlined by University of Waterloo’s John Robinson in the late 1980s. 

Rather than tracking the development of many technologies, business would do better to start by focusing on the world’s biggest needs and pressing problems, to identify the long-standing frictions and tradeoffs that have prevented it from tackling them until now. Then, they should define a desirable future in which those issues have been resolved, and work back to identify the technologies, and combinations thereof, that will make solutions possible and commercially feasible. Backcasting helps companies come to grips with both short-term and long run technological changes, making it ideal to manage Deep Tech. 

The Anglo-American think tank Rethink X, for instance, has used a technology disruption framework, predicated on backcasting, to highlight the implications of creating a sustainable world. The analysis suggests that the technological changes under way in the energy, transportation, and food sectors, driven by a combination of just eight emerging technologies, could eliminate over 90% of net greenhouse gas emissions in 15 years’ time. The same technologies will also make the cost of carbon withdrawal affordable, so more breakthrough technologies may not be needed in the medium term. 

Gauging change 

When companies evaluate the business opportunities that deep technologies will open up, they should take into account the scope of the changes they will bring about. It will be determined by the complexity of a technology and the business’s ability to scale solutions based on it. As Arnulf Grubler, the head of the Austria-based International Institute for Applied Systems Analysis, and his co-authors argued six years ago, new technologies can bring about four levels of change. They can: 

1. Improve an existing product. For example, sustainable biodegradable plastic can replace conventional plastic packaging. 

2. Improve an existing system. Nanomaterial-infused paints and an A.I.-enabled smart home system can, for instance, dramatically change homes. 

3. Transform a system. Developing the ecosystem for hydrogen-powered automobiles, from hydrogen production to refueling stations, could transform urban mobility. 

4. Transform a system-of-systems. Creating a purification technology that transforms current water supply and management systems will also alter the working of water-consuming sectors such as agriculture, alcohol, beverages, paper, and sugar. 

Figuring out which of the four levels of change is likely to result will help companies better assess market sizes as well as growth trajectories. When BCG recently estimated the market size of Deep Tech solutions in nine sustainability-related sectors, for example, it found that while technology improvements in existing value chains would generate additional revenues of over $123 billion per annum, those that resulted in systemic changes would generate 20 times more. Or as much as $2.7 trillion a year. 

Cultivating ecosystems 

Few companies already have in-house all the technologies and capabilities they need to deploy Deep Tech. They must gain the support of technology-related ecosystems, which extend from academics and university departments to investors and governments, to develop those competencies. The types of linkages that will result will depend on the business opportunity as well as the ecosystem’s maturity. 

Several kinds of collaborations are likely to form. Some incumbents will, obviously, join hands with start-ups to develop new products or processes, as Bayer did in 2017, setting up a joint venture with Ginkgo Bioworks to synthesize microbes that will allow plants to produce their own fertilizers. Others will orchestrate systemic changes, which is what Hyundai Motor Group is trying to do in the field of mobility by working with several Deep Tech startups. Still others may focus on nurturing deep technologies to maturity themselves, akin to the efforts of Sweden’s SSAB (formerly Swedish Steel), Vattenfal, and Finland’s LKAB to scale a sustainable steel-making process in which fossil-free electricity and green hydrogen replace coking coal. 


A deep technology was impossible yesterday, is barely feasible today, and may soon become so pervasive and impactful that it will be difficult to remember life without it, points out Michigan State University’s Joshua Siegel. The future will likely belong to companies that don’t just track Deep Tech, but invest in its development and drive its adoption by engaging with ecosystems, forcing rivals to play the losing strategy of catch up. 

Read other Fortune columns by François Candelon. 

François Candelon is a managing director and senior partner at BCG and global director of the BCG Henderson Institute.
Maxime Courtaux is a project leader at BCG and ambassador at the BCG Henderson Institute.
Antoine Gourevitch is a managing director and senior partner at BCG.
John Paschkewitz is a partner and associate director at BCG.
Vinit Patel is a project leader at BCG and ambassador at the BCG Henderson Institute.

Some companies featured in this column are past or current clients of BCG. 

The opinions expressed in commentary pieces are solely the views of their authors and do not necessarily reflect the opinions and beliefs of Fortune.

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