John McCarthy, an American cognitive scientist and computer scientist, first used the expression AI while a research fellow at the prestigious Massachusetts Institute of Technology (MIT) in the mid-1950s, describing it as “the science and engineering of making intelligent machines”; since then AI has developed into a multidisciplinary field that embraces not only computers and robotics but also computer science, neuroscience, linguistics, mathematics and psychology (Stuart, Currie, Goodman, Ives & Scott, 2015). Progressively, developments in AI, machine learning and ordinary user interfaces are enabling the automation of many knowledge workers’ duties which have long been viewed as too difficult or impracticable for machines to perform (Manyika et al., 2013). Furthermore, as the capabilities of machines continue to improve, enabling them to identify faces, transform speech in real time, learn and process language, AI’s capacity to mimic human actions and roles will expand (Stuart et al., 2015). Extreme automation via AI will progressively automate some of the abilities previously only possessed by humans (Baweja et al., 2016).
Improvements in data assimilation and collection, processing power and algorithms have enabled computer scientists to accomplish major advances in AI, moving the technologies beyond the lab and into many machine-learning systems already in commercial use in numerous applications (Barton, Woetzel, Seong & Tian, 2017). Three fundamental elements are enabling AI to flourish, namely:
Dramatic increases in computing power, driven by growing use of cloud computing; Growth in big data (BD), with a compound annual growth rate (CAGR) of more than
50% since 2010, as more devices become connected; and
Significant investments in the research and development (R&D) of basic AI technologies (Accenture, 2017a).
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AI abilities are based on pure computer processing power, which explains how, with the marked decrease in the price of servers, cloud computing and other computer architecture, AI technology continues to gain momentum (Stuart et al., 2015). Progress in AI and machine learning are critical for the improvements in advanced robots, self- driving vehicles and the capabilities of knowledge work automation (Manyika et al., 2013). Where AI could be positioned to generate major advances is in big data processing, possibly encompassing the processing of languages and images which, until now, have been the capabilities of computers. Moreover, robots and AI could even begin to produce output, analyse results, make intricate decisions and adapt assumptions to environmental issues (Baweja et al., 2016).
The developments driven by the FIR in AI, are particularly gaining momentum in the domain of decision making (which is usually associated with management), as algorithms are developed to make progressively complex choices, thus putting modern-day management under further pressure to reflect the future scope of management practice (Oosthuizen, 2016). Policies of adoption and adaption are rising rapidly in sectors such as finance, healthcare and manufacturing (Barton, Woetzel, Seong & Tian, 2017), but irrespective of the particular industry or driver of change, the pace of transformation is generally unprecedented, with disruptive changes already reshaping business models and skill sets, and this rapid pace is expected to continue for the next five years (World Economic Forum, 2016).
Modern AI systems are now capable of dislodging humans in professional practices such as accounting, engineering and law, which traditionally relied on the intense, specific knowledge of experienced subject-matter experts (Stuart et al., 2015). Legal and financial services are starting to see the returns of knowledge worker automation. Law firms, for example, now use computers that examine thousands of legal documents to help in pre-trial research (work that previously would have taken hundreds or thousands of hours of paralegal labour) and AI has performed a role in financial transactions for some time, as AI algorithms are able to analyse numerous financial broadcasts, news stories and press releases, then make decisions concerning their trading significance, and act in milliseconds (faster and with greater data recall than any human trader) (Manyika et al., 2013). Banks also utilise machine learning to identify fraud, discover claims or charges outside an individual’s normal
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purchasing pattern, and even offer financial services like ‘Robo Advisor’ which uses AI to propose bespoke, inexpensive financial advice (Manyika et al., 2013).
Barton et al. (2017) note that China has developed into one of the prominent global centres for AI advancement, having recognised that the nation’s enormous populace and varied industry mix creates huge volumes of information and offers a colossal market. China’s largest tech companies invest substantially in the R&D of AI, having calculated that automation could add 0.8 to 1.4 percentage points to GDP growth per annum, depending on the speed of implementation (Barton et al., 2017).
The present anxious debate about the continuing impact of AI and robotics on today’s workforce requires talent strategies to assess the best way in which to deal with this transition successfully (World Economic Forum, 2016). “The consequences of this unavoidable rise of smart machines, robots, AI and so-called cognitive computing are clear: our future does not lie in competing in jobs such as information storage, data processing and repetitive computational tasks (smart machines are certain to beat us, hands down) but, rather, our future lies in being more human and less like machines. In this future, making mistakes, failing, not complying and creatively destroying things are some of the key skills on which we will be able to beat machines for quite some time” (Leonhard, 2014).