The future is already here — it’s just not very evenly distributed – William Gibson.
The initial years of this decade were an inflection point in business history. The determinants of business success changed irrevocably due to two major technological shifts:
(a) the coming-of-age of technologies like Big Data, Cloud Computing, Social Media and Mobile Computing
(b) the emergence of newer technologies like Blockchain, Deep Learning, modern Computer Vision and advanced Robotics.
Additionally, there are even bigger disruptive technologies, like Artificial General Intelligence and Quantum Computing, waiting to be further explored and developed. These exponential technologies, on their own as well as in conjunction with each other, will cause massive disruptions that will impact every business, individual and society. No entity will be left untouched.
The specific nature and structure of these disruptions may not always be fully clear at this time. However, there is a general consensus that their impact will be greater in intensity and broader in scope than what we have witnessed at any singular point in human history. Furthermore, the challenge will not only be in terms of addressing massive changes at multiple levels, but also in dealing with an increasingly accelerating pace of change. As a result, the drivers of business success today (and in the coming years) are going to be much different than before. New business ecosystems, process innovations, modernized governance structures, new management ideas, and non-technology transformations will be important. But the single-biggest driver will be the increasing reliance on exponential technologies. This means that traditional approaches to innovation, such as Incremental and Non-R&D Innovation, are not adequate anymore. There is a compelling need to augment these approaches with R&D-driven Innovation.
Exponential technologies, in general, are still evolving. The supplier market is in formative stages; the associated technologies are at early stages of their maturity cycles; there is a general lack of specific governance frameworks; and the global talent pool is very limited. High initial costs, development schedule over-runs, functional and performance defects, interoperability and extensibility issues, and high maintenance costs are quite common. Many solutions based on these technologies exist only in their prototype forms (contrary to claims made by vendors), and not as full-scale production systems. Yet, the impact of these technologies is already felt in many industries.
One major impact of exponential technologies is the widening of the gap between innovators and the rest. For instance, 5G communication is expected to be a key driver of Smart Cities, Intelligent Grids, Next-Generation Healthcare, Digitization of Services, the Internet of Things, etc. Adopters of 5G communication will be able to develop and deploy applications that run exclusively on 5G networks. This new technology, when leveraged with Deep Learning, Blockchain or Edge Computing, will enable the adopters to solve new customer problems, or existing customer problems more effectively and efficiently than before. This is likely to significantly boost their revenues and/or business profitability, and will create a business gap between them and the rest. Similarly, companies that have already started developing critical solutions leveraging Artificial Intelligence will steal a march on their late-adopting peers. It is generally observed that late adopters and laggards are seldom able to shrink those gaps with early adopters that were powered by advanced technologies.
Another impact is the emergence of new forms of talent (labor), the evolution of some of today’s forms, and the phasing-out of the low-end ones.
In the 2017 book ‘What to do when machines do everything’, the authors predicted that 12% of US jobs will be automated over the next 10 years, displacing around 19 million workers. They also predicted that 21 million new jobs will be created, thus keeping the unemployment rates around the same as of today.
While there are different views on the exact job statistics, there is broad consensus on one point – the displacement of existing jobs will lead to newer ones. Additionally, there will be new jobs created due to the emergence of new technologies, new use cases, new compliance, and regulatory frameworks, new business processes, etc. Many of these newly created jobs will be different in form and structure from the ones of today.
Furthermore, consumer behavioral and preferential patterns are changing at a fast pace due to factors such as increasing interest in e-experience and 360-degree experience, and human engagement with augmented/virtual reality systems, automated/AI agents and robots. The ever-increasing digital footprint of consumers is leading to new types of business models. The large-scale availability of personal data, coupled with the technological capability to capture and process that data in real-time, is turning out to be a huge disruptor. New concepts like the ‘subscription economy’ and the ‘sharing economy’ are evolving that are changing user preferences. Consumers have better options than before with the launch of hyper-personalized products and services. The digital transformation of businesses are directly and indirectly transforming individual user tastes and consumption patterns. Additionally, changes in the economic status of users (for instance, a sizeable population of major developing countries are moving from below poverty levels to the middle class) are changing user tastes and preferences. These trends will only gain greater momentum as we progress into the future.
Companies that aim to compete and lead in this new age of exponential technologies will have to reimagine themselves as R&D-driven Innovators – no matter which industries they belong to, and which customer groups they serve. More than anything else, this requires a fundamental shift in the mindsets of company leaders, and in the manner, companies have traditionally positioned themselves internally as well as in the marketplace. Over-reliance on old technologies and innovation strategies is like fighting tomorrow’s war with yesterday’s ammunition. This era is different, its needs are different, and it needs new types of tools and techniques.
Numerous breakthroughs have been generated through R&D-led innovation. At times, these innovations may even take place inadvertently or as side benefits of other innovations, or merely by experimenting with technologies used in other industries. For instance, BMW’s iDrive system was based on innovations from the video game industry. X-rays, and their use in medical science, were discovered while testing the passage of cathode rays through glass. Formula 1 Pit Stops led to the creation of McDonald’s Drive Thru restaurants. 3M developed their pathbreaking surgical drapes for preventing infections based on the work of Hollywood’s makeup artists. The list goes on and on.
The primary task in R&D-based innovation is to build a composite 3-to-5 year R&D and Innovation Strategy based on the company’s vision, business goals, resources, constraints, dependencies, and external and internal dynamics. This strategy must be developed as a core business strategy (and not as a sub-strategy). In accordance with the same, an actionable R&D-based Innovation Roadmap must be developed that would include high-level goals, planned investments, and key evaluation parameters. R&D failures, partly or fully, are inevitable, and initial setbacks must not lead to decommissioning of the R&D program. Instead, there must be a robust mechanism to capture the lessons learnt and apply them in future projects. At the same time, the company’s innovation culture must be regularly revisited to upgrade the organizational ‘psychological safety threshold’.
Some companies, in their bid to initiate R&D efforts with a bang, follow the M&A route. This is a risky proposition, particularly for those with negligible technology experience. A case in point is Mattel’s 1998 failed acquisition of The Learning Company. Mattel, a traditional toy company, decided to enter high-tech toys and computer gaming through this acquisition. While there were various reasons for the failure, one of them was Mattel’s inability to understand the dynamics of a company where R&D played a crucial role. R&D-led innovation has to be gradual, and companies must develop the strategic expertise to understand not only the value but also the limitations of technologies that they leverage for innovation.
Companies that have understood and accomplished the above aspect have been extremely successful in history, such as Alphabet/Google, Amazon, Apple, Cisco, IBM, Intel, Microsoft, Oracle, SAP, Salesforce, and others. Furthermore, R&D programs should include a mechanism to constantly generate and evaluate critical ideas—irrespective of where they originate, or where they are currently utilized. In the 1960s and 1970s, top Japanese manufacturing companies reportedly spent over 25% of their cumulative R&D investments on identifying and adopting new technologies from the West. This has often been cited as a key reason for Japan’s growth in the 1980s and 1990s.
Overall, companies need to really understand that the best approach to generating long-term Innovation Capital in today’s age is R&D. Deep innovation capabilities cannot be built through mere tinkering or by accidents – it needs focused and sustained efforts in terms of idea generation, experimentation and evaluation, and removal of false positives as well as recovering from false negatives during this journey.
The R&D journey is not an easy one. There are many variables to provision for, and many challenges to overcome. Here are some of the important ones.
Companies operating outside technology and R&D-driven industries have generally relied on non-R&D innovation for business success. Their innovation strategies have largely been driven through incremental improvements; adoption of matured processes, governance frameworks and late-stage technologies; and re-aligning and tinkering with existing ideas, knowledge and technologies. While these strategies will always be relevant, they will not be adequate to fulfil the demands of innovation in this new age of exponential technologies. Adopting the mindset to innovate through R&D necessitates companies to first change their existing mindset.
Leadership matters, and in cases of highly complex functions like R&D, it matters even more. There is a distinct lack of senior leadership talent that understands the nuts-and-bolts of setting up, managing and governing R&D functions. This is one of the biggest impediments in achieving success in corporate R&D initiatives.
Successful R&D functions have generally been fully autonomous, and are often placed directly under the CTO or company leadership. R&D teams need a high degree of decision-making authority (in terms of determining their internal procedures, work schedules and employee evaluation criteria) because of the creative, complex and highly unstructured nature of their work. Forcing R&D teams to follow the same procedures, guidelines and standards as the rest of the organization is often counter-productive.
The decision on the best possible apparatus for creating an R&D structure depends on multiple factors, such as the company’s strategic goals, operating/business models, investment capacity, technology strategy, risk appetite, and planned products and services. The ‘Build’ and ‘Buy’ models work best when technology is extremely strategic to the company’s core value propositions, and tightly coupled with its products and services. The ‘Partner’ model is most optimal when two or more companies/entities are working to solve a common industry challenge, or when companies may not have the financial power to independently invest in R&D, or when companies intend to reduce their overall R&D risk by sharing that with others. The ‘Outsource’ model works well when technology may not be closely linked to the company’s products and services, or if the company does not possess the requisite technical leadership, financial strength or strategic intent to conduct in-house R&D.
Any form of research-based development is a long-time approach. In most cases, technologies need to be first developed or improved, and subsequently put to use to address specific business cases, often through iterative prototyping. This not only takes time, but also involves high chances of failure. As a result, R&D-based innovation is generally non-linear, such as long periods of experimentation with limited results, and short periods of massive successes. Companies need to develop the culture and the mental fortitude to deal with such high uncertainty situations.
The value addition from R&D usually takes place in a staggered manner over a long period of time. One of the most complex aspects is the measurement of the ‘lag effect’, i.e. the productivity lag from investment to innovation, and the lag from the diffusion of that innovation to commercialization. Furthermore, the spill-over effects of R&D productivity to the rest of the company has its own measurement challenges. This temporal and gradual distribution of R&D productivity with spill-over impact is complex to understand and analyze for financial accounting and business planning.
R&D functions are seldom able to deliver on their own, particularly while dealing with evolving technologies or unstructured workloads. This is due to two main factors. Firstly, top talent is scarce, and not limited to individual companies. Hence, it is important to tap into external talent. Secondly, it is unrealistic to assume that a company can, within reasonable timeframes, develop every component of its R&D-led products or services on its own. Hence, there is a strategic need to leverage the external innovation ecosystem to accelerate the cycle time for development, reduce overall costs, and/or enhance product or service quality. This external ecosystem may include peer companies, academic institutions, start-ups, incubators, accelerators, government institutions, contractors, and even customers.
Modern technological landscape is shaped by technological convergence where multiple technologies interact, at different levels and in varying proportions, to produce non-linear or even extreme outcomes. This not only involves high uncertainty and unpredictability, but the complexity also keeps increasing as newer technologies come into play and existing technologies mature. Hence, companies need a robust ‘Technology Adoption Model’ to integrate the traditional and evolving technologies in their R&D-led innovation use cases.
One of the critical questions for companies and their R&D functions is to determine which problems to solve (and using what approach), and which ones to avoid, or park for the future. Success in R&D-led innovation largely depends on the ability to address this question effectively. It is not easy, and even large, experienced organizations have sometimes failed to accomplish this. R&D projects often witness schedule or cost overruns, or may even completely fail to achieve their intended objectives because of inefficient project prioritization, or simply because they may have picked up the wrong challenge(s) in the first case.
R&D-driven Innovation, despite known and unknown challenges, is gradually emerging as the most critical determinant of business success. It has the potential to shorten the competitive gap between a small company and a large Fortune 500 corporation. It can enable companies to disrupt their respective industries through radical innovation; provide ammunition to prepare for future battles; and at times, even enables companies to chart entirely new courses.
Most adopters of exponential technologies are generally on or around the starting line today. Hence, this is the optimal time for the fence-sitters, the late adopters and non-technology companies to get serious about R&D-led innovation. Closed innovation paradigms must be replaced by open ones, and efforts must be focused on building Innovation Capital over time. The new-age innovation marathon has already begun, and just like everything else in life, the ones that adapt better to the changing dynamics will eventually march ahead.
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