Digitalization, automation and AI pose a great threat to today’s job market that requires constantly changing skills. However, some of the skills are not missing due to the evolution of technology, but rather due to a loss of attractiveness. This is especially the case for positions with an unusually high number of vacancies or such that remain vacant for a long time.

 

According to the Swiss Skills Shortage Index, “a skills shortage exists if there are more vacancies than job seekers in an occupation.” Last year, the Adecco Group compared in its Swiss Job Market Index job advertisements with the number of job seekers registered by the Vacancies and Job Market Statistics Information System (AVAM), which yielded the 2019 Swiss Skills Shortage Ranking.

As in previous years, in 2019 engineering occupations such as structural and electronics engineers are most wanted by Swiss employers. They are followed by technical occupations, fiduciary and IT professions. The ranking further indicates that compared to 2016, when the measurement was conducted for the first time, the skills shortage in 2019 is 22% higher across Switzerland. [1]

There are many reasons for the skills shortage. The rapidly changing skills requirements caused by technological innovation are believed to have the most profound impact on the risks of skills mismatch and shortage. Similarly, Hay’s Global Skills Index 2019/20 reported the highest talent mismatch since the index’ launch in 2012 and they, too, believe that technological development is one of the main contributing factors [2].

On the part of businesses, many companies facing the threat of talent shortages, which might damage their commercial success, prepare themselves for new technologies by upskilling their existing workforce, investing in training, encouraging lifelong learning and raising the retirement age.

There is no doubt that continuous upskilling throughout a career will become the new normal, but is this really the key to overcoming skills shortage? If it were, how come that the situation looks as if things are going in the opposite direction?

Another report published by a Swiss online job portal and Zurich University of Applied Sciences (ZHAW) provides further insight into the Swiss job market. The report compared more than 100,000 job advertisements with the number of clicks on Swiss job portals and, thus, reveals people’s interests in specific jobs in a more direct fashion.

In the German-speaking part of Switzerland, professions in administration, HR, consulting, sales and customer services, marketing, communication, and executive boards received more interest (clicks) than the job advertisements posted. However, jobs in areas like production, telecommunications, construction or nursing received less interest (clicks) compared to the job advertisements posted. [3] This suggests that economic incentives as well as social recognition are becoming increasingly important for people when it comes to choosing a profession.

Last year, there were over 6000 professional care vacancies in Switzerland. This number has doubled compared to five years ago.[4] Reporting on the healthcare workforce supply and demand in Switzerland shows that care workers graduating in the near future will only cover 56% of the demand until 2025.[5]

In the case described above, the problem doesn’t have to do with up-or reskilling. It is rather about the ways in which more people – especially younger ones – can be encouraged to pursue a career in jobs that are considered less attractive. What is even worse, evidence shows that due to bad working conditions (e.g. little income, long working hours, too much stress) a large share of young people has switched their working field either right after their apprenticeship or after a mere few years of professional experience. This includes professions in childcare, hospitality, catering services and handcrafts.

Today everyone is talking about automation, digitalization, AI, upskilling and reskilling. We must remember that there are still many jobs that are unlikely to be automated but essential to our daily lives. And these jobs are losing in popularity. It is important for governments and education systems to take action on increasing awareness and to promote such professions. As written in the OECD Employment Outlook 2019, the “future of work is in our hands and will largely depend on the policy decisions countries make.”

For almost a decade, JANZZ.technology has been observing and working with many labor markets worldwide. Our latest product JANZZdashboard! creates transparent and easy to understand gap analyses of the labor market. This will give governments a clear idea of which skills are available and which ones should be expanded or redeveloped. To learn more about our solutions please write now to sales@janzz.technology

 

 

 

[1] Spring Professional. 2019. Swiss skills shortage index 2019. URL: https://www.swissinfo.ch/resource/blob/45398900/860c466e7be6e615ba922c24c9edf5ee/adecco-study-data.pdf [21.01.2020]

[2] Rachel Muller-Heyndyk. 2019. New technology causing skills gaps and stagnant wages. URL : https://hrmagazine.co.uk/article-details/new-technology-causing-skills-gaps-and-stagnant-wages [21.01.2020]

[3] Robert Mayer. 2019. Die meisten Stelleninserate, die geringste Nachfrage. URL : https://www.tagesanzeiger.ch/wirtschaft/in-diesen-berufen-herrscht-ein-mangel-an-fachkraeften/story/18953945 [21.01.2020]

[4] Albert Steck. 2019. Offene Stellen auf Höchststand. URL: http://jobs.nzz.ch/news/6/arbeitswelt/artikel/421/offene-stellen-auf-hochststand [21.01.2020]

[5] Veronica DeVore. 2016. When caring for patients gets competitive. URL : https://www.swissinfo.ch/eng/showing-off-skills_when-caring-for-patients-gets-competitive/42524090 [21.01.2020]

 

 

 

 

 

 

Artificial intelligence (AI) is unquestionably a powerful tool. Its economic value is increasing tremendously and transforming numerous industries such as manufacturing, fintech, healthcare and automobile. Workers in finance and marketing have much success using AI technologies, whereas HR practitioners find it rather hard to integrate these into their daily practices.

Prasanna Tambe, Peter Cappelli and Valery Yakubovich state in their research: “there are systemic and structural differences for HR that do make it harder, when you are building an AI-based system.” [1]. Due to the fact that the quality and explanatory power of big data and AI are limited they are still considered unconventional in the fields of HR and employment. To have a better understanding of this matter, we need to consider the AI problems in terms of data science in human resource management (HRM).

There are three main challenges in HR practices when it comes to data science. The first issue is the lack of consistency in HR process measurement throughout the employee life cycle. For example, when determining which candidate to hire or choosing whom to promote, it is crucial to consistently record and analyze which criteria and skills were the decisive factors in the previous hiring process.

The second problem with HR practices is the limitation of data sets produced in HRM. Unlike some fields such as marketing and finance, where a lot of data are generated and easily gathered, data collection in HRM faces big challenges in terms of quantity and quality. Moreover, data in HRM is oftentimes unstructured (on paper, in excel or PDF) and consequently, difficult for a computer to process.

The last difficulty concerns ethical issues related to data processing. The results of HR decisions can have a significant impact on someone’s career. Therefore, it is imperative to think about how fairness and transparency can be achieved. Furthermore, it is also crucial to know how employees react to the results that are solely based on data-driven algorithms. As Morgan Hampton from Tesla declared, “recruitment should be automated as much as possible, hiring should remain human.”

Taking these three issues into consideration when searching for AI solutions, HR managers should focus on the following aspects in order to utilize AI more effectively. Firstly, HR managers need to create the right HR process that is ready both for the digital age and AI technology.

Currently, AI technologies are separately, for example in recruiting and talent acquisition, payroll management and self-service transactions. However, they lack a mechanism to generate data that can assist the whole AI process in HR practices.

HR managers often merely keep the applications that they are interested in and don’t retain those that are screened out. This leads to a one-dimensional analysis and conclusion [1]. All these criteria should be gathered in data collection and eventually, be evaluated to facilitate the development of big data models and AI processes.

Furthermore, it is also imperative to generate data in a sustainable way. For instance, there are AI applications that can predict which workers are about to quit their jobs, and some even track data points from employees’ social media or emails [2]. If employees were aware of such a system, they would probably change their behavior and deliberately produce misleading data.

Last year, the story about the Amazon AI recruiting tool being biased against women was proof that machine learning can mimic human attitudes. Gender, nonetheless, is not the only aspect that is reason for discrimination. Others such as age, nationality or ethnicity could also have a negative impact, keeping companies from inclusive and diverse hiring. HR managers should carefully collect data samples that are representative and look for explainable AI solutions. The complex neural networks in deep learning is far from self-explanatory.

Up until today, the standard data criteria that HR managers should respect throughout the HR practice cycle does not exist yet. This means that HR managers have to team up with their company’s internal IT department or with external AI vendors to determine what data to trace and how to measure those data, in order to establish the best practices for AI within their companies.

At JANZZ.technology we believe that collecting and structuring data is fundamental for creating smart data. Our parsing tool extracts the right entities from paper, Excel or PDF, ensuring a fair end-to-end data processing from the very beginning. Do you want to know more about our parser and how we can assist you in your AI transformation journey? Please write now to sales@janzz.technology

 

 

[1]Prasanna Tambe, Peter Cappellli and Valery Yakubovich. 2019. Artificial intelligence in human resources management: Challenges and a path forward. URL:https://www.researchgate.net/profile/Peter_Cappelli/publication/328798021_Artificial_Intelligence_in_Human_Resources_Management_Challenges_and_a_Path_Forward/links/5c5edc7f299bf1d14cb7dc5f/Artificial-Intelligence-in-Human-Resources-Management-Challenges-and-a-Path-Forward.pdf [2019.10.20]

[2] Samantha Mclaren. 2019. Here’s how IBM predicts 95% of its turnover using data. URL:https://business.linkedin.com/talent-solutions/blog/artificial-intelligence/2019/IBM-predicts-95-percent-of-turnover-using-AI-and-data[2019.10.20]

Over the past decade, thanks to the availability of large datasets and more advanced computing power, machine learning (ML), especially deep learning systems, have experienced a significant improvement. However, the dramatic success of ML has forced us to tolerate the process of Artificial Intelligence (AI) applications. Due to their increasingly more autonomous systems, current machines are unable to inform their users about their actions.

Nowadays, most AI technologies are made by private companies that make sure to keep their data processing a secret. Furthermore, many companies employ complex neural networks in AI technologies that cannot provide an explanation on how they come up with certain results.

If this kind of system, for example, wrongly affects customers’ traveling, this might not be of great consequence. However, what if it falsely impacts autonomous vehicles, medical diagnoses, policy-making or someone’s job? In this case it would be hard to blindly agree with a system’s decision-making process.

At the beginning of this year, the Organization for Economic Cooperation and Development (OECD) put forward its principles on AI with the purpose of promoting innovation and trustworthiness. One of the five complementary value-based principles for the responsible stewardship of a trustworthy AI is that “there should be transparency and responsible disclosure around AI systems to ensure that people understand AI-based outcomes and can challenge them.” [1]

Explainable AI (XAI) has recently emerged in the field of ML as a means to address the issue of “black box” decisions in AI systems. As mentioned above, most of the current algorithms used for ML cannot be understood by humans in terms of how and why a decision is made. It is hence hard to diagnose these decisions for errors and biases. This is especially the case for most of the popular algorithms in deep learning neural network approaches. [2]

Consequently, numerous regulatory parties, including the OECD, have urged companies for more XAI. The General Data Protection Regulation, which took effect in Europe, provided the people in the European Union with a “right to a human review” of any algorithmic decision. In the United States, insurance laws force companies to elaborate on their decisions such as why they reject the coverage of a certain group of people or charge only a few with a higher premium. [3]

There are two main problems associated with XAI. Firstly, it is challenging to correctly define the concept of XAI. Furthermore, users should be aware of what the limitations of their knowledge are. If companies had no choice but to provide detailed explanations for everything, intellectual property as a unique selling proposition (USP) would disappear. [4]

The second problematic factor is assessing the trade-off between performance and explainability. Do we need to standardize certain tasks and regulate industries to force them to search for transparently integrated AI solutions? Even if that means putting a very high burden on the potential of those industries.

At JANZZ.technology, we try our best to explain to our users how we match candidates and positions. Our unique matching software excludes secondary parameters such as gender, age, or nationality and only compares skills, education/training, specializations, experiences, etc. It only uses aspects that truly matter in order to find the perfect candidates.

Instead of giving one matching score, our unique matching system breaks down all criteria such as functions, skills, languages and availability. This allows users to have a better understanding of the results and sets the foundation for reskilling and upskilling the workforce to be analyzed. Do you want to know more about the ways in which JANZZ.technology applies explainable AI solutions? Please write now to  sales@janzz.technology

 

 

[1] OECD. 2019. OECD Principles on AI. URL :https://www.oecd.org/going-digital/ai/principles/ [2019.9.17].

[2] Ron Schmelzer. 2019. Understanding Explainable AI. URL: https://www.forbes.com/sites/cognitiveworld/2019/07/23/understanding-explainable-ai/#6b4882fa7c9e[2019.9.17].

[3] Jeremy Kahn. 2018. Artificial Intelligence Has Some Explaining to Do. URL: https://www.bloomberg.com/news/articles/2018-12-12/artificial-intelligence-has-some-explaining-to-do[2019.9.17].

[4] Rudina Seseri. 2018. The problem with ‘explainable AI’. URL: https://techcrunch.com/2018/06/14/the-problem-with-explainable-ai/[2019.9.17].

 

 

 

 

According to the latest Nahtstellenbarometers – Education Decisions after Compulsory Schooling, published by Innovation SBFI and State Secretariat for Education, Research and Innovation (SERI), the most desired professions for vocational training in Switzerland are commercial employees, followed by healthcare assistants and information technologists. This means that none of the professions in handicraft are among the top 5. Compared to boys, there are 15% less girls aspiring to follow the vocational training, and not one single girl reports to be interested in choosing an apprenticeship as electrician, as a car mechanic or as a polytechnical technician. [1]

There is currently a deficit of 42,778 craftspeople in Switzerland. This figure was published by a job search website that also pointed out that craft jobs form one of the most advertised job category in years: currently, there is a total number of 198,097 vacancies advertised. Companies in construction, and building and dwelling services are struggling to hire skilled workers. Stefan Danev, Managing Director of an electrical and safety engineering company in Winterthur, ZH, says that “finding personnel to fill our vacancies in the long term is not easy under the current market situation”. [2]

 Growing interest in the academic route

What causes this shortage of craftspeople in Switzerland? The increasing popularity of the academic route is mentioned as a contributing factor for this development. Between the ages of 12 and 14, Swiss adolescents decide whether to attend a pre-university baccalaureate school or search for an apprenticeship at a company (this is known as the dual-track system).

Thanks to its long tradition, the dual-track system was well received by parents, by adolescents and by society at large. It has largely contributed to a very low youth unemployment rate and high levels of workers’ competency and of quality in all kinds of skilled trades in Switzerland.

However, a growing number of parents hold the perception that the vocational system has a lower quality and that students who enter vocational schools are less capable or have lower aspirations compared to students with four-year university degrees. This is especially the case among parents with migration background. International companies, too, add pressure by not recognizing graduates from vocational training as equal to graduates with a bachelor’s degree. This, in turn, forces many young people to hesitate about pursuing vocational training.

Recently, there has been an increase in adolescents choosing the baccalaureate track, especially in the French and Italian parts of Switzerland. This poses a direct challenge for some of the professions in skilled trades. SERI noted in a statement that “across Switzerland, the baccalaureate has gained more interest and the desire for a general education is stronger than last year” [3].

Misconceptions in skilled trades

When looking at the media, one could think that the only prosperous professions in the future are computer-science-related, and that most skilled-trade jobs will be replaced by automation and robots. But under certain circumstances, machines do not yet have enough dexterity and fine motor skills to compete with human hands. For example, to grind bearings at an accuracy within a 100^th of millimeter requires years of practice and only experienced skilled craftspeople can do this. Moreover, in addition to experience, it also requires intuition that machines will never have.

One tends to hold the misconception of skilled trades as providing little chance of climbing up the career ladder, and, hence, of offering a small salary. But the reality is that there are many top executives in big companies, as well as well-known government figures in Switzerland who have worked their way up from apprenticeship. Highly qualified craftspeople are particularly in demand today and their salary is likely to increase in the near future.

Moreover, the job provides the opportunity to become an entrepreneur. Unfortunately, there are many people in the skilled trades business who intend to pass on their very successful and lucrative business but cannot find a willing successor. Apart from the fact that few young people are trained in such business, it is also reported in various surveys that Generation Y/Millennials (born between 1980-1995) are seeking more security and flexibility in work and prefer having a good, stable salary in an international company, instead of taking on the responsibility of entrepreneurship.

Are young people getting too lazy?

In addition to Switzerland, many other countries like Germany, Belgium, Austria and the United States are facing similar shortages of craftspeople. It is an undeniable fact that the nature of many skilled trades jobs is hard, and that there is a general trend among parents who were in those professions to push their children towards more comfortable jobs. For example, in an apprenticeship at a bakery, one has to start the job at 2am, a tough job that many are unwilling to take up nowadays.

One hardly hears young people saying that their dream job is to become a boilermaker, a metal worker or a welder, because few of them have the opportunity to be introduced to such professions at an early age. With no understanding of or even misconceptions about such jobs, more and more young people are likely to be discouraged from starting an apprenticeship in such professions.

Christoph Thomann, president of the Central Board of Vocational Education and Training Switzerland, believes that the skilled trades are outshined by an increased focus on computers and robots among young people [1]. However, digitalization should not only be regarded as a barrier from choosing vocational training. In fact, vocational training should embrace digitalization and promote an image of the digitalized craftsperson, a modern and less traditionally masculine image that attracts more young people, even young girls, to the field.

A collaborative effort

As in the other economic sectors, digitalization has also made its way into skilled trades and is having a profound impact by increasing the productivity and reducing business costs. For example, by using data analysis, roofers can now measure a house with a 3D scanner to order the exact number of roof tiles needed.

Building information modeling (BIM) helps to realize the integration of building information, which ranges from the design, construction and operation of the building to the end of the whole life cycle of the project. A lot of different information is integrated into a three-dimensional model information database for the stakeholders. There are of course many more examples, including virtual reality, drones, 3D mapping and 3D printing.

Despite great efforts in raising awareness of the importance of the professions in skilled trades communities, reality is disappointing. Matthias Engel from the Swiss Building Association appeals to the federal government in Bern by saying: “It is important that politicians do not send the wrong signals. It is important that the government not only promotes secondary and university education, but also vocational training”.

Indeed, a greater effort to actively promote vocational training, diminishing the gap between young men and women and encouraging more young women to take apprenticeships in certain professions ought to be made by the government. Furthermore, it needs to be considered to bring in young people from other countries than Switzerland, if necessary.

What can AI and big data do for contractors, headhunting firms specializing in skilled trades jobs and government agencies planning to promote vocational training?  How can you use digital platforms to hire the people with the skills you need? Write now to sales@janzz.technology and let JANZZ.technology help you with our intelligent data.

 

[1] gfs.bern. 2019. Nahtstellenbarometer 2019. URL: https://cockpit.gfsbern.ch/de/cockpit/nahtstellenbarometer-2019/ [2019.07.25]

[2] Ulrich Rotzinger and Julia Fritsche. 2019. In der Schweiz fehlen 42’778 Handwerker. URL: https://www.blick.ch/news/wirtschaft/in-der-schweiz-fehlen-42778-handwerker-schreiner-sanitaere-und-elektroinstallateure-verzweifelt-gesucht-id15399544.html?fbclid=IwAR0TU2tUpeSmljN23gLwK5S09DOpvURnFdNqBNoR6nRSfLo_Z3ChojKrVYE [2019.07.25]

[3] Isobel Leybold-Johnson. 2019. What careers did Switzerland’s students choose this year? URL: https://www.swissinfo.ch/eng/continuing-education_what-careers-did-switzerland-s-students-choose-this-year-/45035674 [2019.07.25]