We already have an idea of how digitalization, and above all new technologies like machine learning, big-data analytics or IoT, will change companies’ business models — and are already changing them on a wide scale.
So now’s the time to examine more closely how different facets of the workplace will look and the role humans will have, writes Werner Vogels, chief technology officer of Amazon.com.
In fact, the future is already here – but it’s still not evenly distributed. Science fiction author William Gibson said that nearly 20 years ago. We can observe a gap between the haves and the have-nots: namely between those who are already using future technologies and those who are not. The consequences of this are particularly visible on the labor market many people still don’t know which skills will be required in the future or how to learn them.
Against that background, it’s natural for people – even young digital natives – to feel some growing uncertainty. According to a Gallup poll, 37% of millennials are afraid of losing their jobs in the next 20 years due to AI. At the same time there are many grounds for optimism. Studies by the German ZEW Center for European Economic Research, for example, have found that companies that invest in digitalization create significantly more jobs than companies that don’t.
How many of the jobs that we know today will even exist in the future? Which human activities can be taken over by machines or ML-based systems? Which tasks will be left over for humans to do? And will there be completely new types of the jobs in the future that we can’t even imagine today?
Future of work or work of the future?
All of these questions are legitimate. “But where there is danger, a rescuing element grows as well.” German poet Friedrich Hölderlin knew that already in the 19th century. As for me, I’m a technology optimist: Using technology to drive customer-centric convenience, such as in the cashier-less Amazon Go stores, will create shifts in where jobs are created. 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, for example, have remained similar over time in terms of the workflows. Even the activities of doctors, lawyers or taxi drivers have hardly changed in the last decade, at least in terms of their underlying processes.
Only parts of the processes are being performed by machines, or at least supported by them. Ultimately, the desired product or service is delivered in – hopefully – the desired quality. 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. It’s no longer about producing a car, but about the service “mobility”, about bringing people to a specific location.
“I want to be in a central place in Berlin as quickly as possible” is the requirement that needs to be fulfilled. In the first step we might reach this goal by combining the fastest mobility services through a digital platform; in the next, it might be a task fulfilled by Virtual Reality. These new offerings are organized on platforms or networks, and less so in processes. And artificial intelligence makes it possible to break down tasks in such a way that everyone contributes what he or she can do best.
“People define problems and pre-structure them, and machines or algorithms develop solutions that people evaluate in the end.
Radiologists are now assisted by machine-learning-driven tools that allow them to evaluate digital content in ways that were not possible before. Many radiologists have even claimed that ML-driven advice has significantly improved their ability to interpret X-rays.
I would even go a step further because I believe it’s possible to “rehumanize” work and make our unique abilities as human beings even more important. Until now, access to digital technologies was limited above all by a machine’s abilities: The interfaces to our systems are no longer machine-driven; in the future humans will be the starting point.
For example, anyone who wanted to teach a robot how to walk in the age of automation had to exactly calculate every single angle of slope from the upper to lower thigh, as well as the speed of movement and other parameters, and then formulate them as a command in a programming language.
In the future, we’ll be able to communicate and work with robots more intensively in our “language”. So teaching a robot to walk will be much easier in the future. The robot can be controlled by anyone via voice command, and it could train itself by analyzing how humans do it via a motion scanner, applying the process, and perfecting it.
With the new technological possibilities and greater computing power, work in the future will be more focused on people and less on machines. Machine learning can make human labor more effective. Companies like C-SPAN show how: scores of people would have to scan video material for hours in order to create keywords, for example, according to a person’s name. Today, automated face recognition can do this task in seconds, allowing employees to immediately begin working with the results.
Redefining the relationship between human and machine
The progress at the interface of human and machine is happening at a very fast pace with already a visible impact on how we work. In the future, technology can become a much more natural part of our workplace that can be activated by several input methods — speaking, seeing, touching or even smelling. Take voice-control technologies, a field that is currently undergoing a real disruption.
This area distinguishes itself radically from what we knew until now as the “hands-free” work approach, which ran purely through simple voice commands. Modern voice-control systems can understand, interpret and answer conversations in a professional way, which makes a lot of work processes easier to perform. Examples are giving diagnoses to patients or legal advice.
At the end of 2018, voice (input) will have already significantly changed the way we develop devices and apps. People will be able to connect technologies into their work primarily through voice. One can already get an inkling of what that looks like in detail.
At the US space agency NASA, for example, Amazon Alexa organizes the ordering of conference rooms. A room doesn’t always have to be requested for every single meeting. Rather, anyone who needs a room asks Alexa and the rest happens automatically. Everyone knows the stress caused by telephone conferences: they never start on time because someone hasn’t found the right dial-in number and it takes a while until you’ve typed in the 8-digit number plus a 6-digit conference code. A voice command creates a lot more productivity. The AWS Service Transcribe could start creating a transcript right away during the meeting and send it to all participants afterwards. Other companies, such as the Japanese firm Mitsui or the software provider bmc, use Alexa for Business to achieve a more efficient and better collaboration between their employees, among others.
The software provider fme also uses voice control to offer its customers innovative applications in the field of business intelligence, social business collaboration and enterprise-content-management technologies.
The customers of fme mainly come from life sciences and industrial manufacturing. Employees can search different types of content using voice control, navigate easily through the content, and have the content displayed or read to them. Users can have Alexa explain individual tasks to them in OpenText Documentum, to give another example.
This could be used to make the onboarding of new employees faster and cheaper – their managers would not have to perform the same information ritual again and again. A similar approach can be found at pharmaceutical company AstraZeneca, which uses Alexa in its manufacturing: Team members can ask questions about standard processes to find out what they need to do next.
Of course, responsibilities and organizations will change as a result of these developments. Resources for administrative tasks can be turned into activities that have a direct benefit for the customer. Regarding the character of work in the future, we will probably need more “architects,” “developers,” “creatives,” “relationship experts,” “platform specialists,” and “analysts” and fewer people who need to perform tasks according to certain pre-determined steps, as well as 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: older people, people who at the moment don’t have access to a computer or smartphone, people for whom using the smartphone in a specific situation is too complicated, and people in developing countries who can’t read or write.
A good example of the latter is rice farmers who work with the International Rice Research Institute, an organization based near Manila, the Philippines. The institute’s mission is to fight poverty, hunger and malnutrition by easing the lives and work of rice farmers.
Rice farmers can benefit from knowledge to which they wouldn’t have access were they on their own. The institute has saved 70,000 DNA sequences of different types of rice, from which conclusions can be drawn about the best conditions for growing rice. Every village has a telephone, and by using it the farmers can access this knowledge: they select their dialect in a menu and describe which piece of land they tend.
The service is based on machine learning. It generates recommendations on how much fertilizer is needed and when the best time is to plant the crops. So with the help of digital technologies, farmers can see how their work becomes more valuable: a given amount of effort produces a richer harvest of rice.
Until now we only have a tiny insight into the possibilities for the world of work. But they make clear that 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.
Although there are twice as many robots per capita in German companies than in US firms, German industry still has trouble finding qualified employees rather than having to fight unemployment.
In the future we humans will be able to carry out activities in a way that is closer to our creative human nature than is the case today. I believe that if we want to do justice to the technological possibilities, we should do it like Hölderlin and have faith in the rescue, but at the same time try to minimize the risks by understanding and shaping things.