We already have an idea of how digitalization, and above all new technologies like machine learning (ML), big data analytics or IoT, will change companies' business models — and are already changing them on a wide scale.
It's natural for people – even young digital natives – to feel some growing uncertainty from the rapid technological adoption across industries. In Singapore, one of the most hyper-connected cities in Asia, 80 percent of Singapore’s workforce are positive about the opportunities digital technologies will present while about 57 percent feel that they need to develop their skills to keep up with these developments.
How many of today’s jobs will exist in the future? Which human activities can be taken over by machines and will there be new types of jobs in the future that we can't even imagine today? Now's the time to examine more closely how different facets of the workplace will look, and the role humans will have.
Future of work or work of the future?
Thinking about the work of tomorrow, it doesn't help to base the discussion on structures that exist today. After the refrigerator was invented in the 1930s, many people who worked in businesses that sold ice feared for their jobs. Indeed, refrigerators made this business superfluous for the most part; but in its place, many new jobs were created. For example, companies that produced refrigerators needed people to build them, and now that food could be preserved, whole new businesses were created which were targeted at that market. We should not let ourselves be guided in our thinking by the perception of work as we know it today. Instead, we should think about how the workplace could look like in the future. And to do that, we need to ask ourselves an entirely different question, namely: What is changing in the workplace, both from an organizational and qualitative standpoint?
Many of the tasks carried out by people in manufacturing have remained similar over time in terms of the workflows. Only parts of the processes are being performed by machines, or at least supported by them. But in the age of digitalization, people do much more than fill the gaps between the machines. The work done by humans and machines is built around solving customer problems.
People define problems and pre-structure them, and machines or algorithms develop solutions that people evaluate in the end. For example, radiologists are now assisted by machine-learning-driven tools and have claimed that ML-driven advice has significantly improved their ability to interpret X-rays.
Redefining the relationship between human and machine
The progress at the interface of human and machine is happening at a very fast pace.
For facilities staff and security officers working for Certis, this is already happening. Through a cloud-enabled mobile app, staff can easily track their tasks, communicate with supervisors and send alerts of emerging incidents, greatly increasing Certis’ operational efficiency.
In the future, technology may also become a much more natural part of our workplace activated by several input methods such as speaking, seeing or touching. At the US space agency NASA, for example, Amazon Alexa organizes the ordering of conference rooms - anyone who needs a room asks Alexa, and the booking takes place automatically. The effective use of technology can create better synergy between employees and improve productivity. Of course, responsibilities and organizations will change as a result of these developments. Resources for administrative tasks can be moved into roles that have a direct benefit for the customer. We will probably need more "developers," "creatives," "relationship experts," and fewer "administrators". By speaking more to humans' need to create and shape, work might ultimately become more fulfilling and enjoyable.
Expanding the digital world
This new understanding of the relationship between man and machine has another important effect: It will significantly expand the number of people who can participate in digital value creation: people without access to technology and people who are digitally illiterate, older people, and people without access to smartphones or computers.
A good example of the latter is rice farmers who work with the International Rice Research Institute, an organization based near Manila, the Philippines. With machine learning, farmers can access recommendations on how much fertilizer is needed and when the best time is to plant the crops and through a telephone just by selecting their dialect in a menu and describing which piece of land they tend. So, with the help of digital technologies, farmers can see richer harvests and improve the value of their produce.
Until now, we only have a tiny insight into the possibilities for the world of work. But in the foreseeable future, the quality of work for us humans will most probably increase, and that technology can allow us to perform many activities that we still cannot imagine today.