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Where machines could replace humans—and where they can’t (yet)

The technical potential for automation differs dramatically across sectors and activities.

As automation technologies such as machine learning and robotics play an increasingly great role in everyday life, their potential effect on the workplace has, unsurprisingly, become a major focus of research and public concern.

Automation, now going beyond routine manufacturing activities, has the potential, as least with regard to its technical feasibility, to transform sectors such as healthcare and finance, which involve a substantial share of knowledge work.

Last year, we showed that currently demonstrated technologies could automate 45 percent of the activities people are paid to perform and that about 60 percent of all occupations could see 30 percent or more of their constituent activities automated, again with technologies available today.

In this article, we examine the technical feasibility, using currently demonstrated technologies, of automating three groups of occupational activities: those that are highly susceptible, less susceptible, and least susceptible to automation.

Toward the end of this article, we discuss how evolving technologies, such as natural-language generation, could change the outlook, as well as some implications for senior executives who lead increasingly automated enterprises.

In discussing automation, we refer to the potential that a given activity could be automated by adopting currently demonstrated technologies, that is to say, whether or not the automation of that activity is technically feasible.2 2.We define “currently demonstrated technologies”

Occupations in retailing, for example, involve activities such as collecting or processing data, interacting with customers, and setting up merchandise displays (which we classify as physical movement in a predictable environment).

The cost of labor and related supply-and-demand dynamics represent a third factor: if workers are in abundant supply and significantly less expensive than automation, this could be a decisive argument against it.

For example, the large-scale deployment of bar-code scanners and associated point-of-sale systems in the United States in the 1980s reduced labor costs per store by an estimated 4.5 percent and the cost of the groceries consumers bought by 1.4 percent.3 3.Emek Basker, “Change at the checkout: Tracing the impact of a process innovation,”

Almost one-fifth of the time spent in US workplaces involves performing physical activities or operating machinery in a predictable environment: workers carry out specific actions in well-known settings where changes are relatively easy to anticipate.

Through the adaptation and adoption of currently available technologies, we estimate the technical feasibility of automating such activities at 78 percent, the highest of our seven top-level categories (Exhibit 2).

Since predictable physical activities figure prominently in sectors such as manufacturing, food service and accommodations, and retailing, these are the most susceptible to automation based on technical considerations alone.

Within manufacturing, 90 percent of what welders, cutters, solderers, and brazers do, for example, has the technical potential for automation, but for customer-service representatives that feasibility is below 30 percent.

A service sector occupies the top spot: accommodations and food service, where almost half of all labor time involves predictable physical activities and the operation of machinery—including preparing, cooking, or serving food;

We calculate that 47 percent of a retail salesperson’s activities have the technical potential to be automated—far less than the 86 percent possible for the sector’s bookkeepers, accountants, and auditing clerks.

The heat map in Exhibit 3 highlights the wide variation in how automation could play out, both in individual sectors and for different types of activities within them.4 4.For a deeper look across all sectors in the US economy, please see the data representations from McKinsey on automation and US jobs, on

Long ago, many companies automated activities such as administering procurement, processing payrolls, calculating material-resource needs, generating invoices, and using bar codes to track flows of materials.

Examples include operating a crane on a construction site, providing medical care as a first responder, collecting trash in public areas, setting up classroom materials and equipment, and making beds in hotel rooms.

Already, some activities in less predictable settings in farming and construction (such as evaluating the quality of crops, measuring materials, or translating blueprints into work requirements) are more susceptible to automation.

The hardest activities to automate with currently available technologies are those that involve managing and developing people (9 percent automation potential) or that apply expertise to decision making, planning, or creative work (18 percent).

For now, computers do an excellent job with very well-defined activities, such as optimizing trucking routes, but humans still need to determine the proper goals, interpret results, or provide commonsense checks for solutions.

Overall, healthcare has a technical potential for automation of about 36 percent, but the potential is lower for health professionals whose daily activities require expertise and direct contact with patients.

One of the biggest technological breakthroughs would come if machines were to develop an understanding of natural language on par with median human performance—that is, if computers gained the ability to recognize the concepts in everyday communication between people.

The actual level will reflect the interplay of the technical potential, the benefits and costs (or the business case), the supply-and-demand dynamics of labor, and various regulatory and social factors related to acceptability.

E-commerce players, for example, compete with traditional retailers by using both physical automation (such as robots in warehouses) and the automation of knowledge work (including algorithms that alert shoppers to items they may want to buy).

The greater challenges are the workforce and organizational changes that leaders will have to put in place as automation upends entire business processes, as well as the culture of organizations, which must learn to view automation as a reliable productivity lever.

Understanding the activities that are most susceptible to automation from a technical perspective could provide a unique opportunity to rethink how workers engage with their jobs and how digital labor platforms can better connect individuals, teams, and projects.6 6.See Aaron De Smet, Susan Lund, and William Schaninger, “Organizing for the future,”

McKinsey Quarterly, January 2016., It could also inspire top managers to think about how many of their own activities could be better and more efficiently executed by machines, freeing up executive time to focus on the core competencies that no robot or algorithm can replace—as yet.

How automation will affect you – the experts’ view

With soft, nimble fingers, an arm stretches out to delicately pluck an apple from a shelf and place it gently into a basket.

The irregular shape and delicate flesh of these common groceries have meant they tend to be packed by human workers at Ocado’s warehouses.

But the company is pursuing robotic technology that could assist these human warehouse workers but still handle produce safely, making the process faster and cheaper for the company.

The middle class is at risk Reports suggest that 47% of people employed in the US are at risk of being replaced by machines and 35% of jobs in the UK may similarly be threatened –

Chowdhry points to the shifts that took place in factories during the industrial revolution when automatic looms and other machines took over from human weavers.

It it is not going to affect just blue collar workers Often, we think of low-wage, low-skill jobs being the most at risk, like warehouse workers or cashiers, but automation may also affect middle-income jobs, such as clerks, chefs, office workers, security guards, junior lawyers, inspectors.

So, should companies seeking to automate jobs have a moral responsibility to help the staff they are replacing to learn new skills?

Brynjolfsson and Paul Clarke, chief technology officer at Ocado, both agree that school and college education need to better prepare pupils for a world where robotic and artificial intelligence will be widespread.

The concern is that we are not updating our education, training and political institutions to keep up In the workplace, employees will also continually require new sets of skills rather than using the same ones over their entire career that could just go obsolete anyway.

We need to think about getting away from the traditional five day working week to one where I spend 60% of my time doing my job and 40% learning on a regular basis.”

With lower incomes and potential unemployment looming for middle-income workers, governments themselves could face some fundamental problems, like lost taxes and dissatisfied voting classes.

“We are also going to see an increased demand for those with social skills, interpersonal skills, who are nurturing, caring, teaching, persuasive, have negotiating skills, and are good at selling.”

Alex Harvey, head of research at Ocado Technology, which develops the software and tech for the company's retail arm, points out that the world has been designed and built for humans, and building robots to operate in these naturally complicated environments is a major technical challenge.

“It is quite a simple robot in terms of its behavioural repertoire, but it can form a nice team where the human technician is the leader and they can use the muscular power of the robot.”

The ethics problem Around 1.7 million robots are already in use around the world, but they are largely used in industrial settings where few humans are allowed to set foot.

As that number grows, and the roles they perform expand, the likelier humans are to work hand in hand with robots, side by side –

An AI algorithm, for example, could choose to make a series of financial transactions that achieve its goals, but lie outside the tangled web of regulations that govern the sector.

Machine learning systems are only as good as the data they are given to learn on, and recent studies have suggested artificial intelligence can develop sexist and racist tendencies.

The majority of people working in the technology industry are white males, with men making up between 70% and 90% of the employees at some of the biggest and most influential companies.

Meanwhile, Bill Gates recently suggested yet another ethical red flag: that robots themselves may have to be taxed to make up for lost levies on income from employees.

Others have suggested as robots take on more tasks, there could be a growing case for universal basic income, where everyone receives state benefits.

Machine learning systems and modern AI are usually trained using large sets of images or data that are fed in to allow them to recognise patterns and trends.

This might be fine if we want to find CT scans that show signs of disease, for example, but if we use a similar system to identify a suspect from a fragment of CCTV footage, knowing how it did this may be crucial when presenting the evidence to a jury.

The team programmed the robot, called Betty, to trundle around the offices monitoring for clutter building up, checking whether fire doors were closed, measuring noise and counting workers at their desks outside normal hours.

Surprisingly, those working alongside Betty actually responded to their mechanical worker in a positive way, even coming to its aid if the robot ever got stuck in a corner.

McKinsey: One-third of US workers could be jobless by 2030 due to automation

As much as one-third of the United States workforce could be out of a job by 2030 thanks to automation, according to new research from McKinsey.

'Even if there is enough work to ensure full employment by 2030, major transitions lie ahead that could match or even exceed the scale of historical shifts out of agriculture and manufacturing,' according to a report by the McKinsey Global Institute published this month.

'Unpredictable' jobs such as gardeners, plumbers, or providers of child and elder care are also less likely to see automation over the next decade, as they remain challenging to automate and don't usually earn high wages, according to McKinsey.

'Beyond retraining, a range of policies can help, including unemployment insurance, public assistance in finding work, and portable benefits that follow workers between jobs' as well as '[p]ossible solutions to supplement incomes, such as more comprehensive minimum wage policies, universal basic income, or wage gains tied to productivity,' the researchers wrote.

'For workers around the world, policy makers, and business leaders — and not just social scientists who specialize in socio-economic paradigms — that should give pause for thought, and be a spur for action.'

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