- 12/04/2017
- Category: Commentaries
Over the past year, we have pretty much glorified the dawn of technology. It has posed tremendous impact on basically every segment of our life. As we wake up every morning, we get to our phones to fetch news, instead of waiting for newspaper guy to deliver our daily subscriptions – which may still be widely done around the globe, only reduced in number. Also, as widely known by many, people start engaging technology in improving their economic productivity. We have entered, what many regarded as, the fourth industrial revolution, with the widespread utilization of industry 4.0. It is beyond the revolution of robots and automated machineries in the economy, as even these robots and machines are connected to a network – the internet – which virtually govern the whole production directives. This industrial blueprint claims to make industry 30% faster and 25% cheaper.[i] However, amidst all the talks on efficiency and boosted performance, an alleged threat emerges. Apparently, jobs are at stake, as efficiency moves away from human works to robot and AI driven. Labor economists suggest that this is a real threat that has not yet garnered much attention. And this is not coming, it is already happening.
Automation has impacted the way jobs are distributed between humans and robots (and AI/Artificial Intelligence). McKinsey reports that intelligent process automation (IPA) is the integral part in the next generation operating model for the digital world,[ii] and highlights that IPA takes the robot out of the human.[iii] It provides 50 to 70% of tasks automation, which cuts cost up to 20 to 35% annually and straight-through process time of 50 to 60%, with return on investments in often triple-digit percentages.[iv] Enabled by machine learning, this innovation leads to more efficient robotic works encompassing a lot of job loads and classifications.[v] This even allows to significantly decrease human intervention overtime, even to handle exceptions.[vi]
A recent study by Ball State University suggests that 5 million job loss since 2000 was mainly driven by automation and boost in efficiency (87%), compared to foreign migrants and trades (13%).[vii] Another study by PriceWaterhouseCoopers also found that 30% of workforce in the UK, 35% in the US, 38% in Germany and 21% in Japan will face serious threats from AI and robot automation by 2030.[viii] Automation also affects high degree of work productivity across wide range of job field. According to McKinsey, about 60% of all occupations may expect at least 30% of their constituents’ activities being automated. However, in general, the sector that may receive automation the most is the predictable physical work (around 78% of the sector which may include wielding and soldering on an assembly line, food preparation, or packaging objects), compared to the unpredictable physical work (around 25% of the sector which may include construction, forestry, or raising outdoor animals).[ix] According to the same report, several job field which may be adopting in the most intensive frequencies including accommodations and food services, manufacturing, agriculture, constructions, even professional and management. This is widening and emphasizing the possible harms automation may bring to the workforce and job fields.
AI automation even begins to penetrate the white-collar workers, denying the general assumption that blue collar workers are the ones receiving most impacts – particularly jobs layout and shortage – from automation. Legal works, for example, start equipping AI technology in their works. The clerical works of screening legal documents and providing relevant information for certain cases have been proven to be more accurate and efficient under AI intervention (an AI technology called natural language). Although other lawyer’s tasks like writing legal deposition and performing on court are not yet automated. However, which such amount of improvement the technology has made and done to the industry, it is very likely that in the future the works of lawyers may fully or partially be run by AIs. The comparison can be quite revealing, as a patent may require the works of at least three partners, five associates, and four paralegals. Today, a comparable project only needs one partner, two associates, and one paralegal.[x]
If we look at the debate closely, there has also been a lot of talks regarding the silver lining of automation. Some says that robots and AIs can never bring out the utmost efficiency without human intervention.[xi] Humans will still remain as the central key players in the whole economic engine. Some also argues that some works can never be replaced by AIs. A research conducted by Deloitte shows a profound shift towards the ‘caring’ jobs; the number of nursing assistants increased by 909%, teaching assistants by 580%, and care workers by 168%.[xii] Another compelling argument in the AI-human labor relationship is a notion which says that an adoption of AI technology will also open for other job opportunities, like what happen during the first industrial revolution, where steam technology did not let everyone go jobless, but prompted new job fields and opportunities. Or the ATM revolution in the 1960s. The machines performed the job that used to belong to tellers. As ATM took over the cash dispensing work, more intensive job opened, instead.[xiii] David Autor, an MIT professor, strongly argues that automation does not only replace human works, it also complements labors, raises outputs in a way that has never happened before, which in turn demands for higher labor flows. He also argues that the press and media has overexaggerated the extent of automation role in complementing labor works which increases income and productivity.[xiv] He further adds that succumbing to the notion that automation will trigger mass unemployment is the same as succumbing the notion that there is only finite number of jobs, and it is wrong.
Taking the middle way of both directions, we indeed shall take a wiser approach to understand and eventually suggest prescription for the problem. One thing to address, unemployment remains an economic hurdle for many countries, especially in the developing worlds, even before the issue of automation arrived. Innovative job openings and alternative fields resulting from automation may arrive in the developed world, with the advanced maturity in the economy (industries) and policy (governments). But the developing world still has a lot to work on. Before they get into actual adoption of AIs and robot, governments have to identify ways of adopting them, to make sure that the use will not harm the whole job market holistically. Job markets in the developing worlds are saturated enough by tight competitions and lump economies. Limiting the number of opportunities is indeed unfair. Making sure that everyone has jobs is already hard enough, let alone providing replacement jobs or even prompting innovations. Of course, keeping all the benefits and possible economic leap from the automation is also not to be traded off. Calculating the situation for the suitable context, in this case developing nations, is strongly needed. Determining one best solution is difficult, but should not be put aside, and should start to be strongly considered. One thing that needs to be taken by the developing nation is that more people need to be more informed about the dawn of automation and the real threats it may possess. Elon Musk, the CEO of the Tesla Company, quotes that artificial intelligence summons the ‘demon.’ But surely, we can always cast away demons, can’t we?
picture: pexels.com
[i] Lorenz, M. (2015). ‘Markus Lorenz: Industry 4.0: How intelligent machines will transform everything we know’. A Video of TED Institute uploaded to their YouTube channel available here < https://www.youtube.com/watch?v=uBZmJOHIN8E> [Accessed 10 April 2017]
[ii] Bollard, A., Larrea, E., Singla, A., and Sood, R. (2017). The next-generation operating model for the digital world. McKinsey (Online). Available at: http://www.mckinsey.com/business-functions/digital-mckinsey/our-insights/the-next-generation-operating-model-for-the-digital-world [Accessed 11 April 2017]
[iii] Berruti, F., Nixon, G., Taglioni, G., and Whiteman, R. (2017). Intelligent process automation: the engine at the core of the next generation operating model. McKinsey (online). Available at: http://www.mckinsey.com/business-functions/digital-mckinsey/our-insights/intelligent-process-automation-the-engine-at-the-core-of-the-next-generation-operating-model [Accessed 11 April 2017]
[iv] Berruti, F., Nixon, G., Taglioni, G., and Whiteman, R. (2017). Intelligent process automation: the engine at the core of the next generation operating model. McKinsey (online). Available at: http://www.mckinsey.com/business-functions/digital-mckinsey/our-insights/intelligent-process-automation-the-engine-at-the-core-of-the-next-generation-operating-model [Accessed 11 April 2017]
[v] Pyle, D., and San Jose, C. (2015). An executive’s guide to machine learning. McKinsey [online]. Available at: http://www.mckinsey.com/industries/high-tech/our-insights/an-executives-guide-to-machine-learning [Accessed 11 April 2017]
[vi] Laurent, P., Chollet, T., and Herzberg, S. (2015). Intelligent automation entering the business world. Deloitte (online). Available at: https://www2.deloitte.com/content/dam/Deloitte/lu/Documents/operations/lu-intelligent-automation-business-world.pdf [Accessed 11 April 2017]
[vii] Hicks, M.J. and Devaraj, S. (2015). The myth and the reality of manufacturing in America. Ball State University Center for Business and Economic Research. Available at: http://projects.cberdata.org/reports/MfgReality.pdf [Accessed 11 April 2017]
[viii] Hawksworth, J., Kupelian, B., and Berriman, R. (2017). Consumer spending prospects and the impact of automation on jobs. PriceWaterhouseCoopers (online). Available at: http://www.pwc.co.uk/services/economics-policy/insights/uk-economic-outlook.html [Accessed 11 April 2017]
[ix] Chui, M., Manyika, J., and Miremadi, M. (2016). Where machines could replace humans – and where they can’t (yet). McKinsey (online). Available at: http://www.mckinsey.com/business-functions/digital-mckinsey/our-insights/Where-machines-could-replace-humans-and-where-they-cant-yet [Accessed 11 April 2017]
[x] Lohr, S. (2017). A.I. is doing legal work. But it won’t replace lawyers, yet. The New York Times (online). Available at: https://www.nytimes.com/2017/03/19/technology/lawyers-artificial-intelligence.html [Accessed 11 April 2017]
[xi] Manyika, J., Chui, M., Mirermadi, M., Bughi, J., George, K., Willmott, P., and Dewhurst, M. (2017). A future that works: automation, employment, and productivity. McKinsey (online). Available at: http://www.mckinsey.com/~/media/McKinsey/Global%20Themes/Digital%20Disruption/Harnessing%20automation%20for%20a%20future%20that%20works/MGI-A-future-that-works-Full-report.ashx [Accessed 11 April 2017]
[xii] The Economist. (2016). Artificial Intelligence: The impact on jobs. (online) Available at: http://www.economist.com/news/special-report/21700758-will-smarter-machines-cause-mass-unemployment-automation-and-anxiety [Accessed 12 April 2017]
[xiii] Gillespie, P. (2017). Rise of the machines: Fear robots, not China or Mexico. CNN Money (online). Available at: http://money.cnn.com/2017/01/30/news/economy/jobs-china-mexico-automation/ [Accessed 12 April 2017]
[xiv] Autor, D. (2015). Why are there still so many jobs? The history and future of workplace automation. Journal of Economic Perspectives, 29(3), pp. 3-30.
