Big Data, ML and AI to Transform Employment Scenario

by January 22, 2018 0 comments

According to sources in the recruitment industry, the year 2018 is going to witness a massive increase in demand for professionals with expertise in emerging technologies such as Artificial Intelligence (AI) and Machine Learning. Even though people specializing in Big Data and Analytics will still be sought after, AI and ML are going to be the next big thing.

Robotics and automation have already made massive encroachment in the manufacturing sector. According to a study by Oxford University’s Department of Engineering, nearly 47% jobs will face the risk of being automated, over a span of two decades.

Employment in transportation, logistics and office administration is at a high risk of replacement. Self-driven vehicles, including big trucks, are already up and running on the roads. Though robots have been mainly utilized in the manufacturing sector only, millions of service jobs could be on the radar next. Automation in service industries could be more indicative, considering that the service sector has a larger scope for employment than agriculture or manufacturing.

However, technological elevation is a double-edged sword. Though it will wipe out some jobs, it will also create a need for other few.

For example, in the retail industry, automation has led to self-service cashier desks. But the forthcoming adoption of computerized reading glasses or goggles will give shoppers the potential to stroll through aisles and see foods with certain attributes; like the ones that are gluten free or vegan. The other materializing products or apps will allow shoppers to find and analyze products more easily with their smartphones.

Blue-collar and white-collar jobs will be eliminated, basically, anything that requires middle-skills (meaning that it requires some training, but not much). This leaves low-skill jobs, as described above, and high-skill jobs which require high levels of training and education.

There will assuredly be an increasing number of jobs related to programming, robotics, engineering, etc. After all, these skills will be needed to improve and maintain the AI and automation being used around us.

But will the people who lost their middle-skilled jobs be able to move into these high-skill roles instead? Certainly not without significant training and education. What about moving into low-skill jobs? Well, the number of these jobs are unlikely to increase, especially as the middle-class loses jobs and stops spending money on food service, gardening, home health, etc.

This is why the transition could be extremely painful. It’s no secret that rising unemployment has a negative impact on society; less volunteerismhigher crime, and drug abuse are all correlated. A period of high unemployment, in which tens of millions of people are incapable of getting a job because they simply don’t have the necessary skills, will be our reality if we don’t adequately prepare.

In many cases, the nature of jobs will change rather than disappear because of AI. And, automation will also enable some workers to focus on higher value, more rewarding, and creative work, removing the monotony from their day jobs.

There has always been disruption whenever a new technology has ticked. We saw it with the industrial mass production, the internet, personal computing, and enterprise technology. In all those instances, the technology created periods of rapid disruption in terms of jobs. New technology has always made some tasks and roles disappear more quickly, but it also has created new occupations. This is how our society has evolved. The same is true with AI.

But the AI revolution changes the balance of the scale – the demand for humans versus machines. Machines are becoming very good at performing certain types of tasks such as repetitive chores, knowledge identification and retrieval, and pattern recognition. They never tire and remain vigilant at all times. This leads to the replacement of some jobs. But in most cases the technology only replaces bits of roles, freeing up the time for humans to focus on the tasks where they remain uniquely good at – emotional intelligence, common sense, resolving dilemmas, compassion, wisdom, creativity, and innovation, for example. So we see a sort of rebalancing in the AI economy where there is disruption, yes, but not ‘disruption of jobs’.

Having said that, AI does place a burden on both individuals and society to enable workers to refocus and retrain. It might even require a new type of education for a reinvention of skills. AI should boost productivity and will not necessarily reduce total employment in the long run. But it could also widen income inequality if workers do not develop the necessary knowledge and skills to thrive in the digital economy.

AI can potentially penetrate every business function within an organization. There’s currently a lot of impact on HR administration, payroll, and account processing. Automation takes up administrative tasks, with virtual agents replicating the work of a human being – so the technology copies, emulate, and learns from a worker. Then it keeps learning from its own experiences.

Ultimately, it performs repetitious, slightly lower value tasks, allowing the workers to focus on where they can bring in more creativity and innovative thinking. We see this happening quite a lot inside financial business among others.

Other important transformations are around the application of predictive analytics to production lines and process industries. Here the machinery and systems are constantly monitoring their own behavior, comparing it with data from historical performance. The machine is, therefore, able to predict when it’s going to need maintenance, when it is going to fail, and when human intervention is required. This is happening right now in environments such as the chemical, petrochemical, and manufacturing industries. The impact is quite remarkable – self-monitoring machines, perhaps on the production line or consumer-facing (like vending machines), learn the high and low demand periods, call for maintenance and order their own stock. The time and effort saved on these routine tasks are not insignificant.

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