The future of hiring, will it become 100% automated?

9

October

2021

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One of the most important things managers and recruiters do is hiring the right people for the company. This is one of the reasons why some companies do better than others. However, this is easier said than done. Some companies like Google receive 50000 applications per week, which results in a time-consuming process of picking the applicants that qualify for the job. Most of these companies use artificial intelligence to automate this process. In this article, I will discuss what is being automated, the pros and cons of the automation of recruitment processes, and what we may find in the future.


Like I mentioned in the introduction, the first step of recruitment is more and more often automized by the larger companies. In 2020 alone, recruitment automation had a growth of 547% because of the efficiency of the technology. Artificial intelligence is for example able to filter out all resumes or motivational letters that contain grammatical errors. Apart from that, the algorithm can look for skills that are relevant for the job. As a result, the recruitment process time and cost are reduced by up to 40% and the performance of that company is increased by 20%. This sounds quite appealing, but it is not a solution for every company as it involves high implementing costs. Therefore, for now, you only see this at bigger companies. Another downside is that a filter on grammatical errors will also filter out applicants that do qualify for the job but have some inconsistencies in their CV or motivational letter.


But it does not stop here. Some companies include an AI-powered chatbot in their application process. This robot can collect information such as contact details or a resume, but also ask job-related questions and schedule meetings. 66% of candidates claim that they feel comfortable talking to a chatbot, but another study found out that 82% feel frustrated by overly automated technologies by companies. The recruitment process has become very impersonal in these cases and lacks accuracy.


In the future, it may even be possible to have the whole recruitment process automated by using robots to handle job interviews. This robot could analyze words, speech patterns, and facial expressions of candidates to create an adequate assessment. However, considered the limitations of this technology at the moment, we are far away from a 100% automated hiring process as the technology does not have the emotional intelligence humans have. This is crucial to find the right fit between an applicant and the values of the company.


Personally, I think there will never be a fully automated recruitment process that will be as effective as a personal recruitment process. An interview goes both ways and if you do not get to speak to a real employee, I believe this will increase the occurrence of new employees leaving the company shortly after being hired. And this is something companies do not want to go for.


Have you already been part of an automized recruitment process? And what did you think of that process? Let me know in the comments.

https://valoria.ro/blog/pros-cons-recruitment-process-automation/

https://wperp.com/40125/the-pros-cons-of-recruitment-automation/

https://www.helpnetsecurity.com/2021/09/13/enterprise-automation-adoption/

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Why we should not fear for robots stealing our jobs

8

October

2021

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In my job as a business analyst, I work on the robotization of business processes. We often get the question: are you destroying human jobs by robotization? Although it is true that one of the main objectives of robotization, and digitization in general, is increased efficiency, and robots do substitute some jobs, ultimately they will lead to increased added value for both the demand side as well as the supply side of the job market.

Firstly, robots are likely to replace highly repetitive and logic-based tasks, which does lead to job loss in the lower end of the market (2). For one, this is beneficial to the employer (demand side of the labor market), because of the gained efficiencies and elimination of human error. Secondly, this may be beneficial to employees: as a result of their repetitive tasks being replaced, they are likely to focus on more value-adding tasks, requiring interpretation instead of repetitive logic-based operations, which is likely to improve employee satisfaction. However, of course there will be some jobs that become obsolete as a result of robotization.

Although this downside of robotization is certainly existent, both the history and future predictions tell us that automation also creates job opportunities. These predictions show this will completely offset the job loss as a result of robotization, even resulting in more newly available jobs (2). Besides the positive offset to the quantity of jobs, job value (both monetary – salary, and job satisfaction level) will also increase as a result of this. Because low-level jobs disappear and are replaced by roles with increased expectations and complexity, entry-level job seekers will be more likely to be wanted for those jobs that would have been out of reach in a world without automation. Examples of jobs with increasing demand are data analysts, for which the demand has increased by 650% in the period between 2013 and 2017 (3), marketing specialists, system engineers, and – of course – process automation specialists (2).

To conclude, robotization does not only lead to gains in efficiency and non-monetary benefits such as increased employee satisfaction, but it also creates new jobs. These new jobs are higher valued than the originally substituted jobs, thus robotization does not only provide benefits to the welfare in general (the society) and the employers but also creates long-term value to job-seekers.

References

(1) Brown, T. (2016) Will robots actually take your job? (retrieved from https://www.raconteur.net/will-robots-actually-take-your-job/) (image source)

(1) World Economic Forum (2020) Future of Jobs Report 2020 (retrieved from https://www.weforum.org/reports/the-future-of-jobs-report-2020/)

(2) LinkedIn (2017) Here Are the 20 Fastest-Growing Jobs in the US (retrieved from https://www.linkedin.com/business/talent/blog/talent-strategy/fastest-growing-jobs-in-the-us)

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The fear of being replaced by automation

8

October

2020

No ratings yet. The world is becoming more and more technology driven – IoT, big data analytics, AI and robots. We are living in an age in which automation technologies such as robots play an essential role. Automation has many benefits such as it increases productivity, optimises processes and saves costs (McKinsey & Company, 2017). However, the increase in automation also raises growing concerns of workers about being replaced by these technologies. Studies have shown that 1.5 million UK workers are at a high risk of being replaced by automation (White et al., 2019) and that automation will steal 20% of all UK jobs in 2030 (McKinsey & Company, 2017). Furthermore, research by the economists Frey and Osborne (2017) has shown that about 47 percent of total US employment is at risk of job loss due to automation. The question therefore arises, whether workers have to fear to be replaced by automation?

 

Some economists and researcher believe in the skilled-biased technological change theory. This theory suggests that the demand for low skilled workers decreases relative to the demand for high skilled workers due to technological changes (Bound & Johnson, 1995; Acemoglu & Autor, 2011). According to this approach, automation therefore decreases the demand for low-skilled workers. However, others such as the labour economists David Autor argue that automation does not decrease the overall level of employment, but rather substitute and complement jobs. Jobs that require repetitive tasks are likely to be replaces by automation whereas jobs that perform non routine cognitive tasks are complemented by automation (Autor et al., 2003).

 

What many people do not consider is that automation technologies also create new jobs. Automation changes the types of jobs available in the market by creating new job opportunities. Many new jobs have been created over the past years, due to automation technologies such as software developers, programmers and data analysists. Therefore, I do not believe that workers have to fear that they are replaced by automation. Rather than worrying about being replaced by automation, workers should invest in good education and acquire automation-proof skills such as communication, management and creative skills. Robots can replace humans in many tasks, however, some skills especially inter-personal skills, cannot be replaced by robots.

 

References:

Autor, D. H., & Acemoglu, D. (2011). Skills, tasks and technologies: Implications for employment and earnings. In Handbook of labor economics (Vol. 4, pp. 1043-1171). Elsevier.

Autor, D. H., Levy, F., & Murnane, R. J. (2003). The skill content of recent technological change: An empirical exploration. The Quarterly journal of economics, 118(4), 1279-1333.

Bound, J., & Johnson, G. (1995). What are the causes of rising wage inequality in the United States?. Economic Policy Review, 1(1).

Frey, C. B., & Osborne, M. A. (2017). The future of employment: How susceptible are jobs to computerisation?. Technological forecasting and social change, 114, 254-280.

McKinsey & Company. (2017). Jobs lost, jobs gained: What the future of work will mean for jobs, skills, and wages. Retrieved 25 March 2020, from https://www.mckinsey.com/featured-insights/future-of-work/jobs-lost-jobs-gained-what-the-future-of-work-will-mean-for-jobs-skills-and-wages.

White, S., Lacey, A., & Ardanaz-Badia, A. (2019). The probability of automation in England: 2011 and 2017.

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AI-enabled China’s Social Credit System: in-depth analysis

5

October

2020

5/5 (1)

Automation has transformed every aspect of modern individuals’ lives. Trivial tasks that used to take a person hours to complete, can now be performed within a matter of seconds due to technological advancements. Artificial Intelligence (AI) is one such advancement of technology that is paving the way for the prevalence of automation in every industry. The ability of AI to perform tasks autonomously is primarily possible due to its ability to be able to process large amounts of data and infer patterns and conclusions within this data, thus effectively learning tasks by itself. However, the procedures used by the AI to analyze the data are initially inputted by an administrator in the form of algorithms and statistical models. An algorithm is essentially a set of rules and the process to be followed by the machine/computer to perform a calculation/action. Modern automation stripped to its core, is a collection of algorithms and related statistical models programmed by an administrator. Due to the increased adoption of the internet, algorithms have become integrated into every aspect of our lives.

The financial credit system used in many western countries can be seen as an example of how algorithms govern our lives. The system involves gathering financial data relevant to an individual from multiple sources, followed by an algorithm that analyses the likelihood of an individual defaulting on a loan. The data gathered primarily consists of previous debts taken, payment deductibles not met and other forms of credit taken up by the individual in the past. After the careful analysis of this data, the algorithm calculates a score for the individual, the credit score. This score is then used by banks, insurance companies, and other financial institutions to determine the creditworthiness of the individual when he/she requests their services (Petrasic & Saul, 2017). In China, such a system exists not only to determine a citizen’s financial credit score, but it expands to all aspects of a citizen’s life by judging citizens’ behavior and trustworthiness, known as the Social Credit System, introduced in 2014. The Social Credit System will have a complete database on all Chinese citizens by 2020, which will be collected from a variety of sources. This scale of data collection is possible in China as Baidu, Alibaba and Tencent are the major providers of internet infrastructure in the country; they work closely with the Chinese Communist Party (Kobie, 2019). The majority of the digital footprint left by Chinese citizens is on infrastructure established by these companies thereby making it easy for the Chinese Communist Party to access its citizens’ data. This sharing of data between private companies and the government is not commonly heard of in China’s western counterparts and shows the importance of data protection laws enforced in those countries. The implementation of the Social Credit System has numerous effects on the country and citizens on economic and social levels.

On an economic level, the algorithms that facilitate the Social Credit System help bridge a major institutional gap that is the underdeveloped financial credit system in China. As mentioned earlier, the financial credit system utilizes algorithms to calculate a credit score to determine the creditworthiness of individuals. Such credit checks can make it more difficult or even deny individuals to access credits. Often, these credit checks focus on only certain aspects such as the timely manner in which we pay our debts (Petrasic & Saul, 2017). This is simply not enough to determine the creditworthiness of individuals as there are other factors at play as to why individuals pay their debts over a certain time period as they do. The commercial credit systems such as the Sesame Credit (developed by Ant Financial Services Group) can therefore be seen as more valuable in determining the creditworthiness of individuals. The Sesame credit score is arguably a better predictor of trustworthiness, as the scores take a broad range of important factors into account. This will prove to be very beneficial for the financial institutions as they will have the highest level of guarantee that the credit extended will be in safe hands. At the same time though, the citizen with a low rating will not be eligible for large loans and will be asked to pay a very high interest rate. Thus, effectively positioning the algorithm behind the Social Credit System as the decisive entity on whether a citizen can be eligible for a loan or not. The argumentation behind the decision to allow an algorithm to govern the credit eligibility of the citizens states that, due to the restrictions placed on the citizen with a lower score, it would motivate them to be better citizens thus achieving a better score. However, citizens with a lower social credit score than a certain threshold may be subject to more restrictions. For example, citizens with low social credit scores are restricted access to certain services such as (quality) education or (quality) transportation. On a social level, the Social Credit System may give rise to social segregation, where citizens with low social credits are exempted from social activities as well as leading to reduced interactions between citizens with higher social credits and those with lower social credits. Moreover, on the work floor, people with low social credit scores may fail to get a promotion because of their scores. The combined effect of restricted access to education, social segregation as well as limited career prospects, can lead to the next generation of those citizens, who have low social credits, being given unfair chances to increase their social credits, and, as a result, their quality of life. Questions arise whether algorithms account for bridging the social inequality gap or if it even strengthens it (Ebadi, 2018).

References

Ebadi, B. (2018). Artificial Intelligence Could Magnify Social Inequality. Centre for International Governance Innovation. Retrieved from https://www.cigionline.org/articles/artificial-intelligence-could-magnify-social-inequality

Kobie, N. (2019). The complicated truth about China’s social credit system. Wired. Retrieved from https://www.wired.co.uk/article/china-social-credit-system-explained

Petrasic, K., & Saul, B. (2017). Algorithms and bias: What lenders need to know. White & Case. Retrieved from https://www.whitecase.com/publications/insight/algorithms-and-bias-what-lenders-ne ed-know

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How will automation affect our jobs in the future?

10

September

2019

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(Medium.com ©)                                                                                                                                  Time to read: 4 min

 

As Adam McCulloch describes in his article “Automation and AI: how it will actually affect the workplace”, there are very split opinions about whether automation or Artificial Intelligence in a broader sense will either create or destroy job opportunities.

The latter, more antagonistic side of the argument claims that the use of AI for automating job routines is going to entirely replace the need for human employees. In contrast, the counter-argument to this posits the idea of job opportunities and the shift from routine labour to more meaningful jobs that cannot be replaced by machines at all.

Personally, I believe that we will see both sides materialize to some extent as we continue to develop technologies and machines with the aim of mimicking both, physical and mental human activities. At the risk of stating the obvious, one reason for which I believe that AI and process automation will create, rather than destroy, job opportunities in the near future is that there are more forces fuelling the demand for automation than opposing it.

Industry and government bodies are realizing the gain in productivity that can be achieved by automating routinized tasks and are therefore unlocking large amounts of money to be dedicated to the development of automation technologies. This will most certainly create job opportunities as the supply of engineers and managers with experience in this field is currently drastically behind the demand for such technologies and business models.

Forces opposing the development of automation technologies nevertheless do exist, urging for the development of policies and regulations that shall act to safeguard the human workforce. A good example of one player seeking to oppose automation are labour unions, who act on the fear that humans and machines will compete against each other rather than work together in a symbiotic relationship.

Blue-collar automation requires state-of-the art technology which at this point in time, remains expensive for companies to implement. For this sole reason, I believe that the fear of destruction of blue-collar positions due to automation is not yet justifiable on a global scale, as many countries lack the economic resources and/or incentives to adopt the required technology. Even more developed countries are heavily reliant on a cheap human workforce and keep outsourcing blue-collar work to less developed countries rather than acquiring robots.

White-collar automation or robotic process automation (RPA) refers to the automation of some routine desk-job tasks that are highly standardized within the set of a white-collar worker’s various responsibilities. It is perhaps more easy and less costly to implement than blue-collar automation, as it does not require the development and implementation of physical mechanical robots (e.g.: anyone who has a basic grasp of programming can write programs that automate their excel tasks for example). In this scenario I believe that automation will free up white collar workers’ time and energy to be spent on different, more thought-intensive tasks.

I believe that much of the economic and sociological research of the first and second industrial revolutions do equally apply to what is now often referred to as the third and fourth industrial revolution. John Maynard Keynes for example already thought that the impact of the first industrial revolution on society would be that of a drastically shortened work-week in the long-run. Today we can observe that this theory has in fact not (yet?) materialized.

To conclude, and again at the risk of stating the obvious, it is us humans who are at the source of automation and we seem to be in a period of technological breakthroughs (AI, blockchain, quantum computing, IoT, etc…) which will impact many more people than are currently developing it, and hence deeply understanding it. As more people realize they will be impacted by such technological breakthroughs, a bandwagon effect of decision making involving a highly diverse set of stakeholders will develop to steer the direction of this new industrial revolution. Yes, I believe that the potential for replacing our jobs in the very long-term exists, however, whether that will happen depends on how we and our decision-makers want to spend our time.

 

 

What do you think?

 

 

References        

 

Bessen, J. and Kossuth, J. (2019). Research: Automation Affects High-Skill Workers More Often, but Low-Skill Workers More Deeply. [online] Harvard Business Review. Available at: https://hbr.org/2019/02/research-automation-affects-high-skill-workers-more-often-but-low-skill-workers-more-deeply [Accessed 10 Sep. 2019].

 

Book, A. (2018). Should I Panic About Automation Now Or Later?. [online] Hackernoon.com. Available at: https://hackernoon.com/should-i-panic-about-automation-now-or-later-82a4323f1dc7 [Accessed 10 Sep. 2019].

 

Chui, M., Lund, S. and Gumbel, P. (2019). How will automation affect jobs, skills, and wages?. [online] McKinsey & Company. Available at: https://www.mckinsey.com/featured-insights/future-of-work/how-will-automation-affect-jobs-skills-and-wages [Accessed 10 Sep. 2019].

 

McCulloch, A. (2019). Automation and AI: how it will actually affect the workplace – Personnel Today. [online] Personnel Today. Available at: https://www.personneltoday.com/hr/analysis-ai-automation-impact-on-jobs-hr-employment/ [Accessed 10 Sep. 2019].

 

Sivertsen, R. (2018). The Fourth Industrial Revolution – Where Are You Going With This? – Ross Sivertsen – Systems Sherpa. [online] Ross Sivertsen – Systems Sherpa. Available at: https://ross-sivertsen.com/the-fourth-industrial-revolution-where-are-you-going-with-this/ [Accessed 10 Sep. 2019].

 

 

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Artificial intelligence and business model innovation: inseparable in the future?

13

September

2018

No ratings yet. Nowadays, Artificial Intelligence (AI) is a well-known concept among everyone. But, most of the people do not truly know what it means and what functions and purposes it has. Some people think that AI causes powerful robots that will take over our jobs for example. AI is already there, and we are using it, most of the time, without even knowing we are. At the McDonald’s you can place your order and pick up it up without even talking to one human being. Another example, all the personal advertisements we see on our smartphones are generated using AI.

AI is therefore really interesting for companies to implement in their business models. For example, the robots could be used to make the processes more efficient and, for example, to take over the standardized tasks of the employees, so they could really focus on being more creative and maintaining personal contact with customers. This could be extended by the fact that AI machines do not need any recurring trainings. Employees need to be trained and they can easily leave the company, causing another new employee that needs training.

Before business can implement AI into their business model, they should prepare their business for the arrival of AI. The process of gaining competitive advantage of AI consists of three phases. They need to generate data about the customers or processes they want to support with AI. After that, they should interpret this data, so that means transforming the data into information. Lastly, the company has to implement this information into the desired machines, in order to get the machine making decisions based on the imported information.

To conclude, AI offers very interesting opportunities to companies and their way of working. Companies can set up a team of people who are dedicated to developing the AI solutions within their company, but the most important part of become competitive is the data collection. Companies should see the importance of generating data as the first step in the process of implementing AI into their business model.

 

 

References

Ashwini, A. (2018, January 19). How To Create A Successful Artificial Intelligence Strategy. Retrieved from https://medium.com/swlh/how-to-create-a-successful-artificial-intelligence-strategy-44705c588e62

Barkman, A. (2018, January 23). How AI Impacts Business Model Innovation. Retrieved from https://www.techfunnel.com/information-technology/ai-impacts-business-model-innovation/

Sandehl, A. (2018, May 16). It’s Time To Adopt AI Into Your Business. Retrieved from Modelhttps://www.forbes.com/sites/forbesagencycouncil/2018/05/16/its-time-to-adopt-ai-into-your-business-model/#7fce247adcc3

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Digital Transformaton Project – self-driving carts

14

October

2016

No ratings yet. Chris Anderson argues in his book on the Long Tail that there are retail scientists who are dreaming of smart shopping carts which can detect content in the carts by their RFID tags, and consequently give the customer recommendations of products that fit next to the products in the cart (Anderson, 2009). This means that one could have cheese in his cart, and through recognition, the cart would recommend certain bread that fits with the cheese. After this, Anderson mentions that “even these scientists still can’t transport matter into reach and make acting on those recommendations easy. In the physical world, shoppers move; products don’t.” (Anderson, 2009). This is exactly the point where Chris Anderson could be wrong, and where modern day scientists could be right, by designing a self-driving shopping cart. Even though the products won’t fly into one’s cart literally, the cart brings itself toward the products, leasing little to the imagination of the customer. The self-driving shopping cart is the emerging technology which will be discussed further in this report. The company which is the subject of the report is Albert Heijn. Albert Heijn is a supermarket which was founded in 1895 and currently has over 850 branches spread in the Netherlands. (AH, 2016)

Emerging technology of self-driving cart

The concept of self-driving (shopping) carts is a concept that could potentially change the whole way of shopping. With the Internet of Things growing and the expectation of over 50 billion things being connected in 2020 (Hbr, 2016), it becomes clear that self-driving carts fit right into the future. It is an emerging technology for which Wal-Mart has filed a patent in March of 2016 (Atherton, 2016). The shopping-carts will contain a robotic device under the cart and this will cause it to drive in the way the customer wants. Additionally, the entire system will work in a “sensor-rich world, where central computers track inventory and match customer needs to what’s available in the store for the shopper” as Atherton also mentions. Also, the carts will be able to bring themselves back, after being left by customers in, for example, the garage. This causes the employees to have more time to spend on other duties, instead of assembling carts. The concept of self-driving carts resembles online shopping, in a way that one is working with a digital device. However, with a physical purchase, its advantage being an immediate purchase instead of waiting for shipping, it might even win more ground over online shopping (in the supermarket industry) (Atherton, 2016). As more and more customers are switching to online shopping, retail stores are probably looking for way to nevertheless attract these customers (Mintel, 2016).

Customer Experience

Customer experience is an important aspect of shopping, and it is very likely that it is easier to gain a positive customer experience in a traditional brick-and-mortar store than online. It is true that online shopping may have a larger inventory and more possibilities, however it loses on the aspect of instant gratification. Also, doing the groceries with a few clicks on one’s keyboard will never top the physical experience of strolling through the supermarket with one’s family. It is important to realize that this concept of self-shopping carts has many possibilities, such as a screen being connected to the cart, with enables the customer to, for example, browse through available products or a see a map. However, it would probably be easier, and cost effective, to let an application on a smart-phone control the carts. The implementation of this concept of self-driving carts could cause Albert Heijn to stay ahead of its competitors, and this is ultimately what also causes them to have a first-mover advantage in the Netherlands with the introduction of this technology.

 

Group 5

 

References

AH (2016) Alles over Albert Heijn Retrieved from: http://www.ah.nl/over-ah

Anderson, C. (2006) The Long Tail: Why the Future of Business is Selling Less of More. New York City, N.Y.: Hyperion.

Atherton K., (2016), Walmart patents robot carts for better shopping. Can robot carts compete with Amazon? Retrieved from: http://www.popsci.com/walmart-patents-robot-carts-for-better-shopping

Harvard Business Review (2016) To Predict the Trajectory of the Internet of Things, Look to the Software Industry. Retrieved from: https://hbr.org/2016/02/to-predict-the-trajectory-of-the-internet-of-things-look-to-the-software-industry

IMAgency (2016) Albert Heijn. Retrieved from: http://imagency.com/work/albert-heijn/

Mintel (2016) 29% of UK online grocery shoppers are shopping for groceries more online now than a year ago. Retrieved from: http://www.mintel.com/press-centre/retail-press-centre/29-of-uk-online-grocery-shoppers-are-shopping-for-groceries-more-online-now-than-a-year-ago

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