In the age of the fourth industrial revolution, a diverse development agenda starting from attaining sustainable development goals to empowering youth to meeting milestones of economic aspiration largely depends on harnessing the potential of technological innovation. We need to create utilities out of technological ideas in terms of product and process features and trade them at a profit in the globally connected market economy.
Examples of such technological potential include artificial intelligent machines for reducing wastage and material as well as energy need in manufacturing, and agriculture, and improving road safety. To harness such a potential, the challenge is far more than acquiring science and technology capability, providing research and development (R&D) finance to learn, absorb and advance technologies, fostering creativity in generating ideas, and channeling risk capital finance to startups for taking ideas to market.
It has been found that a series of rational decisions should be taken in the midst of uncertainty in nurturing the faint potential of technological ideas to profit from in a competitive market. Managing such uncertainty poses a daunting challenge in profiting from innovative technological ideas. Here are a few sources of uncertainties.
TECHNOLOGY POTENTIAL: Invariably, innovation opportunities are distilled from the emergence of new technologies. At the early stage, the underlying potential remains unclear. How far technology potential will remain amenable to growth often poses risks to investment. For example, artificial intelligence (AI) technology has the potential of adding intelligence to machines, making them safer, and more accurate. The addition of AI to automobiles has the potential to reduce accidents and increase the utilisation factor. To harness this potential, more than $80 billion has been invested.
To take a technology lead, Intel, in a single transaction, invested $15.2 billion to acquire Mobileye. The growth of the performance of autonomous driving technology over 2014-2017 gave a hope that vision-based self-driving cars would be a common sight on the street by 2020. But it has been learned that before reaching the required level of performance, measured in terms of disengagement frequency, progression has saturated. Apparently, it has reached the limit of the underlying potential of machine learning science. Such reality raises the question of the future of huge investment made so far for the R&D in producing patents. The possibility of expiring patents before the beginning of commercialisation of robotic cars poses serious risks to return on investment. Dealing with such uncertainty has been one of the key challenges to benefit from technological innovation potentials.
CUSTOMER PREFERENCES: Technological innovation targets to offer solutions that customers have not experienced yet. Despite the value proposition, until and unless customers prefer the offered solutions, innovators dot not succeed in generating revenue. In this competitive market, innovators need to risk investment, and time to develop and roll out innovative solutions, and wait for customers' acceptance.
Often, the challenge is to meet the preferences of millions of customers to generate adequate revenue to reach profit. For example, Japan has been experiencing the nurse shortage to look after the growing elderly population. Honda saw robotics potential to offer caregiving solutions. Upon investing over two decades in robotics R&D, Honda came up with a well-engineered humanoid - ASIMO. But to its surprise, Honda found that the elderly population did not like humanoid nurses in offering needed care. Similarly, by underestimating customer preferences to multi-touch user interfaces, Nokia was late to adopt similar options in its flagship products. The rest is history.
RESPONSE OF COMPETITION: The third source of uncertainty facing the innovation journey is the response of competitors. Once an innovation starts showing possibility of unlocking profitable business opportunities, competition starts showing up in the form of replication, imitation, innovation, and also substitution. For example, over more than a decade, Tesla has been investing in unlocking the green automobile potential by advancing electric vehicle (EVs) innovations. Upon witnessing profitable business around EV, automobile behemoths of Germany and Japan have taken aggressive initiatives to roll out EV versions of their popular models. Often such competition shows up from unknown, weak sources causing powerful disruption firms. For example, relatively unknown Sony from Japan disrupted the consumer electronics industry in the 1960s, and imaging industry in the 1980s, by making American icons like RCA and Kodak, among others, bankrupt.
POLICY AND REGULATION: Policy and regulation often pose uncertainty in the success of innovation. For example, a Canadian automaker has come up with a three-wheeler compact car. This electric battery-powered compact car requiring far less material and producing no emission appears to be a sustainable, better alternative to cars for commuting. But the regulatory requirement of side airbags has stalled its rollout, despite the fact the company already received 20,000 preorders.
On the other hand, due to absence of conducive regulation, often innovation faces a barrier to exploiting the full potential. For example, in the 1960s, perceived radiation hazard was a barrier to the wide-scale adoption of the microwave oven. The regulatory guidelines limiting the radiation level addressed this concern. Similarly, liability issues faced by AI machines will require appropriate regulatory guidelines to make the next generation human-free operation of intelligent machines like autonomous vehicles acceptable. Uncertain regulation poses a risk to profit-making opportunities for innovative ideas.
INFRASTRUCTURE READINESS: In the absence of readiness of infrastructure, often high-value innovation faces a serious barrier to diffusion. Progression of competing technologies often creates a policy dilemma in mobilising investment in making infrastructure ready. For example, at the dawn of the 21st century, infrastructure issues related to broadband wireless started surfacing for unlocking the innovation potential of the Internet over handheld as well as portable devices, like smartphones, tablets, and laptop computers. The future of 3G/4G cellular service and WiMax as a preferred broadband wireless service delivery platform was not clear. Such uncertainty led to the deployment of the WiMax network in some parts of the world, only to witness wasteful investment. Similarly, EVs are facing charging infrastructure barrier in most of the countries. On the other hand, uncertainty pertaining to emergence of EVs as a strong substitute to gasoline cars, and also the future of hydrogen vehicle is posing a dilemma to investment decision.
Along with development of science and technology capacity, fostering creativity, entrepreneurship, and startups, allocating resources for R&D, and offering protection to intellectual properties, progress should also be made in developing capacities for managing uncertainty facing the exploitation of technology innovations. Such capacity should be developed at different levels, so that individuals, families, firms, national governments, and global institutions can predict unfolding future more accurately, consequentially succeeds in reducing wasteful investment, and mobilising resources for unlocking most promising technology potentials.
M Rokonuzzaman, Ph.D, is academic and researcher at Technology, Innovation and Policy.
