The development of technologies can lead to the era of technological unemployment. Today, automation plays a key role in managing operations and supply chains, and these concepts are associated with the future of work (Berger & Frey 2016; Brotchie et al. 2017). The purpose of this report is to demonstrate the potential of automation for affecting operations in UK industries, to review the recent literature on the problem, and to apply research and evidence to evaluating the impact of technological unemployment on the company in the UK insurance industry.
Description of the Chosen Company
Aviva is one of the leading multinational insurance companies, which is headquartered in the United Kingdom. The organisation is categorised as a public limited company that operates in the insurance and financial services market. Aviva was founded in 2000, after a merger of two British insurance companies (Aviva 2018a). It employs about 30,000 employees who work in different countries. Aviva offers insurance plans of different types for individuals and businesses. The company provides health and life insurances, as well as pensions and investment plans. As a result of providing a wide range of insurance services, the company serves more than 30 million customers in more than 15 countries all over the globe (Aviva 2017b; Aviva 2018b). These customers include individuals and businesses of a different size. The company actively works in the commercial insurance sector.
Literature Search
Technological unemployment is a phenomenon associated with advancing the use of technologies in businesses and reducing jobs. Thus, it is defined as a situation in the economy and management when more tasks became performed by machines or with the help of certain technologies while decreasing the demand for human labour to complete some operations and causing increases in unemployment rates (Barry & Aho 2016; Danaher 2017; Kim, Kim & Lee 2017). The first discussions of technological unemployment are related to the British Industrial Revolution when workers faced the threat of being substituted by machines.
“Luddite” riots in the 1810s demonstrated the public’s negative attitude to mechanisation. However, companies focused on advancing their manufacturing processes, and in the late part of the nineteenth century and the early part of the twentieth century, Ford Motor Company became one of the large corporations to use changed assembly lines that had replaced the work of several persons (Fiorelli 2018; Frey & Osborne 2017; Leitão, Colombo & Karnouskos 2016). Productivity increased significantly, but low-skilled workers were negatively affected by the process. The beginning of the twentieth century was associated with electrification, the active use of engines, and the transport revolution (Faria 2015; Gervais et al. 2015). Still, the later part of the twentieth century was characterised by the shift to information processing (Loi 2015; Wajcman 2017). The spread of computer technologies contributed to creating many jobs for IT professionals despite opponents’ fears (McDougall 2014; Vicini 2016). Nevertheless, routine tasks and rule-based manufacturing were significantly changed by computers, and decreases in employment became observed in the 1970s-1980s.
In the1990s-2000s, the situation regarding technological unemployment became controversial. On the one hand, more specialists in the sphere of IT became demanded. Still, the number of such jobs was not appropriate and reflected unemployment trends (Nica 2016; Peters 2017; Slack, Brandon-Jones & Johnston 2011). On the other hand, the demand for low-skilled workers who could perform manual tasks increased. Consequently, researchers began to discuss risks of technological unemployment that became viewed in the twenty-first century as the trend for the future (Kumar 2016; Pedersen et al. 2016; Smith 2016). The current use of big data and algorithms allows for automating as many tasks and operations as possible (Nolan, Carter & Dalal 2016). Different types of algorithms, including Google’s algorithms, are used in many organisations, decreasing the demand for human resources.
The current understanding of technological unemployment is associated with threats of using robots and computers to replace people in a range of operations and processes. The use of machines is supported in hazardous environments and settings where physical tasks are performed (McAfee & Brynjolfsson 2016; Ng 2016). This situation decreases the demand for people’s work. Thus, industries and areas where the use of computers will be promoted include financial services, banking, accounting, and engineering among others (Brynjolfsson & McAfee 2014; Cortes, Jaimovich & Siu 2017). Researchers emphasise risks that robots will replace people in many positions in the nearest future, and the United Kingdom and the United States will face significant unemployment rates because more than 30% of their jobs can be automated, as it is presented in Figure 1 (Campa 2017; Frey & Osborne 2015).
However, there is also an opposite view according to which robots and computerisation are discussed as beneficial for people who can save their resources to focus on creative tasks. Not all researchers agree that the development of artificial intelligence leads to technological unemployment because automation of processes is important to decrease costs, increase productivity, and improve services without affecting the quality of work of employees (Cockshott & Renaud 2016; David 2017; Dunlap & Lacity 2017; Makridakis 2017; McClure 2017). Therefore, the existing literature on technological unemployment provides many opposite views regarding this phenomenon and its impacts on firms and economy (Murren & Block 2017; Pantea, Sabadash & Biagi 2017).
Scenario Development
In their study, Frey and Osborne (2017) presented a list of occupations that are at the highest risk of being affected by automation. Thus, for insurance sales agents, the probability of automating their tasks is 0.92; for insurance appraisers and insurance claims processing clerks, it is 0.98; for insurance underwriters, the probability is 0.99 (Frey & Osborne 2017). According to these findings, companies from the insurance industry are characterised by high susceptibility to automation causing technological unemployment for insurance clerks and agents because tasks performed by these employees are routine, they are based on using computers, software, algorithms, and big data (DeCanio 2016; UiPath 2017; Wollschlaeger, Sauter & Jasperneite 2017). Consequently, advanced technologies can lead to further computerisation of these employees’ duties (LaGrandeur & Hughes 2017; Strawn 2016; Złotowski, Yogeeswaran & Bartneck 2017).
Entrepreneurs are interested in using computers for performing repetitive and routine tasks, and the company selected for the analysis is Aviva. Despite risks of replacing insurance sales agents, insurance appraisers, and insurance claims processing clerks working with computer programs and software, it is possible to discuss this scenario as low-impact (Berriman & Hawksworth 2017). It is possible to predict that, by 2020, the number of insurance agents in Aviva will decrease by 25%, and by 2030, the minimal number of insurance clerks will work for the company (decreased by 50-60%) (Aviva 2017a; Donnellan 2017; Strawn 2017). However, Aviva is aware of both opportunities and risks of computerisation.
Benefits of automation in the insurance industry include possibilities for saving resources for receiving and processing documents and claims. The required time will be reduced, and the quality of operations will increase (Lamberton, Brigo & Hoy 2017; Mokyr, Vickers & Ziebarth 2015). It will be possible to conduct uninterrupted operations and data analysis during 24 hours a day (Groskopf 2017; Myers & Fox 2017). The work with mixed data will be automated with the help of using computer programs and software that allow for analysing various files and electronic formats (Decker, Fischer & Ott 2017; Fuei 2017). The analysis will be conducted automatically with reference to applying different regulations, standards, and laws. The company has applied more than 40 applications to enhance processing information and claims (Harris 2017; Kehoe et al. 2015; Konečný 2016). The risk of errors in filling in documents, conducting appraisals, and analysing data will decrease by 90%.
Aviva accepts the importance of transforming into a digital leader in the industry with the focus on automation as it is necessary to develop new sales channels and customer-oriented relationships. Thus, “artificial intelligence and robotic automation are likely to increasingly transform the efficiency of insurance operations such as underwriting and claims” (Aviva 2017a, p. 71). Despite the necessity of decreasing the number of insurance clerks because of implementing computer programs and applications and developing telemarketing, Aviva will provide employees with training to develop their skills as financial advisers and multi-agents, as well as professionals in telemarketing (Moodie 2017; Morgan 2017; Qureshi & Syed 2014). This approach will provide the company with resources to address technological unemployment and preserve jobs for more than 15,000 employees. Consequently, if operations performed by insurance clerks and agents are changed in Aviva with computers and robots, the company will provide employees with positions related to telemarketing and using IT.
Conclusion
This report has demonstrated impacts of automation on operations in the insurance industry with reference to the experience of Aviva. The review of the recent literature on the problem has presented the development of the concept of technological unemployment. Furthermore, the discussed research and evidence have been applied to evaluating the impact of technologies on companies in the United Kingdom with reference to Aviva’s scenario.
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