The five technologies that matter the most at this moment in time are Artificial Intelligence, Blockchain, Robotics, Synthetic Biology and 3D printing. Why? They interact with each other, creating previously unthinkable conditions of technological, biological, material, social and psychological change. What is each technology capable of today? What might it achieve in 10 years? What are the immediate implications for C-suite executives?
What is technology capable of today?
Because of their maturity and business applications, each of these five technologies have a unique contribution to the post-pandemic business world. AI is enabling analytics on epidemiologic patterns, drug discovery, advanced tracking of the supply chain and more. Blockchain is empowering the next generation of peer-to-peer economic exchange in a myriad of fields, allowing this activity to occur without a middleman, thus encouraging the kind of distributed activity we need to foster in a post-centralized model where humans need to spread out to avoid contagion. Synthetic biology is enabling a much more efficient way to produce vaccines in the future and potentially could speed up clinical trials. 3D printing is enabling distributed production and consumption of manufactured items without going through a vulnerable, physical supply chain. Together, they enable an increasingly distributed type of business activity that brings new actors into the market, creates new classes of products, and that call for a different type of C-suite leadership mindset.
What might technology achieve in 10 years?
Using these technologies, items as commonplace and well-known as a wall, a piece of cloth and a human being may become nearly unrecognizable from their predecessors over the past 1000 years within the span of this decade. The impact will not only be new construction industry verticals, innovative textile industry goods or superhuman abilities for sale. Rather, changes will intertwine and surprise us.
However, each technology is embedded with a complex set of forces of disruption, namely sci-tech, business models, policy and regulation, and social dynamics. Nothing about their impact, in business or in society overall is set in stone. It is imperative for the C-suite to contain as many surprises as possible.
The success of these technologies in helping address the pandemic will depend on how well we support the scientists who are attempting these feats. In order to facilitate the process, regulators will need to have a fundamental grasp of the impending changes in each core technology and would need to develop a thesis on how they think the technologies would and should evolve and interact. Politicians will need to provide programmatic R&D support across a wide array of sci-tech domains, not all of which will see immediate results. Businesses will have to pivot both to react to, and to make the most of, such opportunities and challenges. Consumers may have to endure periods of drought as these technologies mature. In short, there are no guarantees with technologies of the future. Their success will, ultimately, depend on how well they are able to embed in our society, and how they latch on to the existing patchwork of solutions that we already use.
For example, a first observation might be that AI only becomes productive in a business context when it can be used to solve problems that consumers or enterprise clients will pay for. Earlier generations of AI have been abandoned because of the lack of results beyond initial domain specific breakthroughs (beating humans in chess), but also because of an ability to scale in a useful way without too much tinkering. The reason AI currently is being deployed with some limited amount of success across forward thinking businesses worldwide is somewhat less spectacular. When various analytical techniques are combined, a business can currently achieve a new level of clarity about what their business does by tracking and tracing its own data streams. If methods keep improving and integrate new types of data and even better integration algorithms, incremental improvement might amount to step change progress and even leaps and bounds.
But with experimentation also comes increased risk. Interpretability is also key to a business application, so unless the approach taken by the machine can be understood and explained at least in some simplified way to laypersons, it will not be trusted to carry out business. The C-suite better combine launching experiments in superficially achieving analytical superiority through AI with pondering its long-term implications, its unintended consequences, and its security and privacy aspects. This is especially important as AI-type methods become deeply embedded in industrial technology through robotic interfaces, 3D printing-enabled digital factories or even in the industrial deployment of synthetic biology, giving slow, wet-lab biology the efficiency of scalable engineering methods.
The polymathic challenge for worker efficiency
To be future-proof, we cannot limit ourselves to only picking a few technologies to watch. Rather, it is essential to become a deep expert in one domain and to be well versed in dozens of other domains so that you can communicate across and have the absorption capacity for all kinds of novelty. This goes far beyond the currently recommended approach of becoming a T-shaped expert (expert in one and shallow awareness in others). It also has to do with developing the teeth to be curious but with a bite, so you can still be critical to new perspectives coming from fields where you have not spent a decade preparing the ground.
Consider how 3D printing makes use of AI-enabled machine learning which enables further digitalized supply chains which, together with the increased use of robots throughout that supply chain, contribute to automating the manufacturing industry, which has ramifications on every other industry. When blockchain is used in manufacturing, it makes the supply chain more secure so you can verify product origin, integrity and whereabouts, and enabling smart contracts that change the business dynamics, too. Synthetic biology also re-shapes manufacturing because the products we know might now take on biological properties we had never conceived of, literally growing products instead of, or in addition to, making or printing them, bringing a plethora of new products to market.
These technologies together foster entirely new ways of being ourselves, of being designers (in a design-thinking sense that we are all aspiring designers of worlds), citizens, consumers, financial actors, parents, lovers. In fact, across all or most of our multi-faceted roles and identities.
Reskilling as the C-suite action imperative
Today, the C-suite can already deploy Artificial Intelligence, Blockchain, Robotics, Synthetic Biology and 3D printing for post-pandemic positioning, and are increasingly doing so. They’d better, because the race is on. But done the wrong way, it sets you back.
One last thing to keep in mind is that the next generation of technologies are not mainly about management control but about worker augmentation. Just to take one example, what blockchain does is to level the playing field among economic actors by providing radical transparency of existing markets (to the extent market actors allow it to penetrate), as well as create new markets that embody its peer-to-peer aspects already from scratch.
Financial services such as peer-to-peer lending are only scratching the surface. As blockchain and its promise of decentralized services permeate healthcare, industrial production, or indeed any business where reputation formerly was based on a centralized, coordinating set of actors or institutions, we need to reconsider what the market, the workforce and its business leadership will entail. We will be dealing with an array of workers, machines and stakeholders whose actions may be imminently trackable, perhaps even trustable in most ways, but perhaps less immediately understandable. You will not have met all your employees. You may not even know their identity. The impact will be slower than many think because the complexity means that implementing such a brave new world will take some time, but once it kicks in, the C-suite will not operate as it once did. The C-suite leadership instinct should already move from the mindset of control to the more complex, but more realistic, mindset of coordination.
As such, the breakthroughs that these technologies enable, or indeed the process improvement and visibility they create on the shop floor, can only be sustained over time if they are embraced by the entire workforce. According to the World Bank, the global labor pool consists of more than 3 billion workers. Given the speed with which technology is introduced, global industry will need to continuously re-skill and upskill billions of workers. That’s why reskilling, not developing the technologies themselves, is about to become the 21st century’s greatest leadership challenge.
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