Last week, I looked at why jobs are being robotised and thought about how we would fare.
This week, after doing some research i’ll identify what styles of work is most easy for robotics and where we should be focussing our attention to remain in gainful employ.
While automation will eliminate very few occupations entirely in the next decade, it will affect portions of almost all jobs to a greater or lesser degree, depending on the type of work they entail. 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.
Management consultants McKinsey researched the work associated with over 800 occupations to assess which would be most affected by robots and automation. They consider 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 job activities automated, again with technologies available today.
Next they look at the technical feasibility, using currently demonstrated technologies, of automating occupational activities: and identify those that are highly susceptible, less susceptible, and least susceptible to automation. The way they did this was to consider each occupation; for example retailing, and assess what the roles involve. Retailing consists of 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). Since all of these constituent activities have a different automation potential, McKinsey arrived at an overall estimate for the retail sector by examining the time workers spend on each part of their work during the workweek.
Here’s how McKinsey assessed jobs
- Technical feasibility is a necessary precondition for automation- can he actual work be automated, but not a complete predictor that an activity will be automated.
- Cost of developing and deploying both the hardware and the software for automation.
- Cost of labour and related supply-and-demand dynamics: if workers are in abundant supply and significantly less expensive than automation, this could be a decisive argument against it.
- Benefits beyond labour substitution, including higher levels of output, better quality, and fewer errors. These are often larger than those of reducing labor costs.
- Regulatory and social-acceptance issues, such whether machines are acceptable in a particular setting, must also be considered as part of the transformation. A robot may, in theory, be able to replace a nurse, for example, but for now, this could prove unpalatable for many patients, who expect human contact.
Thus the potential for automation to be effected within a sector or occupation reflects a complex mix between the above factors and trade-offs between them.
Even when Jobs are partly automated and machines do take over some human activities in an occupation, this does not necessarily spell the end of the jobs in that line of work. Sometimes automation increases demand for occupations. For example, the large-scale deployment of bar-code scanners and associated retail point-of-sale systems in the 1980s reduced labour costs per retailer by an estimated 4.5% and the cost of the groceries consumers bought by 1.4 percent. It also enabled a number of innovations, including increased promotions. But cashiers were still needed; in fact, their employment grew at an average rate of more than 2 percent between 1980 and 2013. So even with greater automation, demand increased.
Applying expertise is making creative or planning decisions for activities. Stakeholder interactions- is interacting with customers or clients or managers or other internal or external stakeholders.
Here’s McKinsey’s estimates to the technical feasibility of what parts of jobs could be easily replaced by robots and automation. Managing other people and applying expertise seem almost impossible to automate, while data collection, data processing and predictable physical work appear those that are most easily automated by robots. Predictable physical work is things such as assembly line work, food preparation, packaging objects. Why unpredictable physical work includes construction, forestry & landscaping, and looking after animals.
Heres a more thorough review by various employment sectors
One of the critical elements blocking automation is human-to-human interactions of most occupations. So 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. In retailing, such natural-language advances would increase the technical potential for automation from 53 percent of all labor time to 60 percent. In finance and insurance, the leap would be even greater, to 66 percent, from 43 percent. In healthcare, too, while we don’t believe currently demonstrated technologies could accomplish all of the activities needed to diagnose and treat patients, technology will become more capable over time. Robots may not be cleaning your teeth or teaching your children quite yet, but that doesn’t mean they won’t in the future.
It is never too early to prepare for the future. To get ready for tomorrows automation we must challenge ourselves to understand the data and automation technologies on the horizon today. The greatest challenge facing automation are the workforce and organizational changes that will have to put in place as automation upends entire business processes.
Understanding the activities that are most susceptible to automation from a technical perspective could provide a unique opportunity to rethink how we can engage with our jobs and how digital labour platforms can better connect us to our clients and customers and projects. Automation and robotics could also inspire managers to think about how many of their own activities could be better and more efficiently executed by machines, freeing up human time to focus on the core competencies that no robot or algorithm can replace—as yet.