According to a study conducted by the German Ministry of Labor and Social Policy, the population pyramid no longer exists; rather, it is morphing into a mushroom shape. By 2030, the German workforce will comprise 38 million people—5 million fewer than the current 43 million, and 3 million fewer than in 2010.
A Boston Consulting Group (BCG) study, presented in the TED Talk The workforce crisis of 2030 — and how to start solving it now, shows a likely scenario in which German economic growth will create a labor demand for 46 million people, leaving a gap of a breathtaking 8 million workers.
The sheer size of this gap is sobering, but the assumptions behind the study’s 38 million projection are also worth noting (all figures are compared to 2010):
- The working population is defined as people between 15 and 74 years old. This assumes that people will work much longer than they do today, with 3 million people over age 55 working. We will have 4.8 million fewer people between 25 and 54 years old.
- 0.5 million more women will work.
- 0.9 million more labor will be created by part-time workers who increase the percentage of work they give.
- 3 million more people will have a university degree.
- 0.2 million fewer people with a dual education (typical for Germany) will bring skilled labor to the labor market.
- 3.4 million fewer people who have completed vocational training will be in the workforce.
- 0.2 million net immigration into Germany. Before the refugee crisis, around 0.9 million people left Germany each year, so a net immigration of .2 million will require 1.1 million people to immigrate into Germany.
You might think that we could simply increase net immigration. As BCG’s analysis shows, the aging population crisis will hit many major economies in 2030, including China, so there will be fierce competition to prevent “brain drain” and attract skilled immigrants. And there is no guarantee that Germany will win this talent war.
With this is mind, we might take a new look at automation brought by digitization. The basic formula is that automation is the result of digitization and connectivity. For example, machine data from a wind turbine is broadcast via the Internet to service technicians, enabling them to more quickly and thoroughly understand what to do if there’s a problem. This reduces time-consuming inspections and allows technicians to identify issues quickly and efficiently so they can fix things right the first time. That, in turn, helps optimize maintenance intervals, reduces downtime, and enables service technicians to service more wind turbines.
Predictive analytics based on huge amounts of data from many wind turbines takes this a step further: An algorithm can detect breakdowns before they occur, trigger a work order, and order the required spare parts in advance, making the work of the service technician even more efficient.
Here’s the formula:
Digitization + Connectivity + Artificial Intelligence (AI)/Machine Learning (ML) = Hyper-automation
Admittedly, this can be a scary scenario in a world with sufficient or excess labor supply—but it is comforting in the labor market projections described above.
With some studies predicting that half of all jobs will be killed by robots or AI bots, you might worry that automation will lead to mass unemployment, even with a reduced workforce. That’s why it’s helpful to look at a study conducted by McKinsey that analyzes the U.S. labor market in terms of automation potential of existing technologies. The research organizes jobs into seven basic work categories:
- Management tasks: 9%
- Application of expert knowledge: 18%
- Interaction with stakeholders, i.e. customers: 20%
- Unpredictable physical labor: 25%
- Data collection: 64%
- Data processing: 69%
- Predictable physical labor: 78%
For each category, McKinsey analyzed the percentage of existing technology that can automate these tasks (shown above). The study also looked at each job and the level at which each category is required to fulfill this job. At the end, it shows the overall automation potential of each job.
If you are a CEO, for example, your job comprises a very high percentage of management, interaction with stakeholders, and application of expert knowledge to make informed decisions. These tasks are difficult to automate; thus, CEOs are less likely to be replaced by robots or automation technology. Similarly, if you are a firefighter, much of your work involves physical labor and unpredictable conditions. Each fire is different and requires expert knowledge, so this is another job that is unlikely to be automated.
On the other hand, if you are a factory worker who performs highly repetitive tasks, your job is much more likely to be automated and replaced with a robot.
The authors of the study point out that their analysis looks only at the potential of automation. Additional factors such as costs, availability of skills, and benefits such as improved safety, higher quality, and increased speed are also required in order to make automation decisions.
Rather than completely replacing workers, automation could increase value by taking over tasks that require less skill—think of a doctor who, instead of spending time on the common cold or flu, is able to focus on more complex medical cases. This will likely lead to further disruption in the labor market: the need for upskilling in each job. Those who are unable to keep up will be hit hard.
In short, automation could create a work environment in which professionals and robots work efficiently together and elevate the quality of the work performed — much like a chess player whose skills are supported by a sophisticated chess program.
What is the call to action for companies and participants in the labor market? It all starts with sound workforce planning: Analyze where you are most likely see a skill shortage in the future, and take action. Work to build a reputation as an attractive employer for the talent you anticipate needing, while also embracing digitization to reduce dependency in areas with more limited resources.
This will likely require a radical transformation of your core processes as you collect and process data from within and outside the company and use it in a way that empowers your employees. For this to happen, you must digitize how your organization interfaces with suppliers and customers, eliminating unnecessary steps and enabling real-time reactions to changing market conditions. As shown above in the example of the service technician, companies need to build systems that collect and process data from the Internet of Things. This will allow processes to transform in ways never seen before.
For employers and employees, constant skill development and renewal is essential. Many jobs that do not yet exist will be created. Who predicted, 10 or 15 years ago, that data science or search engine optimization professionals would earn a very good living today? This is the other side of digitization: New businesses, business models, and job opportunities will help counterbalance the negative effects of automation.
For more insight on how digital transformation is affecting the workplace, see An AI Shares My Office.