The Digital Transformation, DX, is having a profound impact on jobs by:
- Replacing human workers with automation
- Removing the need for some type of jobs
- Changing the way jobs are performed, usually requiring different set of skills
- Creating new job opportunities in new emerging sectors
- Increasing the demand for DX support
These changes are happening everywhere although the pace of change is different depending on the geographical area (see section 5) and the market segment. This different demand creates a worldwide competition for skills made stronger by the possibility of offering (part of) those skills from remote. In turns, this may accelerate the DX since the access to remote skills requires the shift of activities to the cyberspace where geographical distance is no longer a factor.
Replacing human workers with automation
The replacement of blue collar workers with machines is an old story. The Industrial Revolution did that but the “replacement” was totally obscured by the flanking of machines to human workers and the amazing increase of human labour needed to complement machines’ activity. The increased productivity in turns, increase wealth and stimulated demand driving further increase of jobs to sustain production.
At the turn of the last century robots started to make a dent into the assembly lines job, leading to job losses. Automation spread beyond the assembly lines of factories. ATM (Automatic Teller Machine…) made a dent into bank white collars and so on. At the same time a booming service industry and a rapidly surging tourism, entertainment and leisure industry, fuelled by increasing free time and availability of money that could be diverted from basic needs (these having become way cheaper) sustained the job market. As shown in the graphic, the pattern of consumer spending in the US (representative of most Western Countries) changed significantly in the last century. Notably, considering expenditures on food, housing, apparel, healthcare and entertainment there is a significant shift (sharp decrease in food and apparel, sharp increase in lodging and entertainment). What is even more notable is that whilst in 1900 “other” areas of expenditure totalled 13%, in 2003 these reached 39% and the trend is still going on today.ùThe sharp decrease in food expenditure does not mean that people are eating less today than hundred years ago, actually, they are eating more … and better quality food. The point is that on the one hand the productivity increase in the food value chain is decreasing the cost and on the other hand people earn more money today than hundred years ago. However, productivity increase goes hand in hand with labour cost decrease and since workers earn more money today it means there are many less workers in that value chain than hundred years ago: the job losses in agriculture, courtesy of automation, have been staggering: up to 76% of jobs were in agriculture back in 1,300, that dropped to 60% in 1,800, 40% in 1,900. Today the percentage is around 2%. The drop in the last 100 years has been incredible and has been matched with an incredible increase in productivity.
Interesting to note that in the last 50 years productivity in manufacturing in the G7 Countries has been around a 4% per year, with a bigger increase in the 1950-1973 period that reached –in Japan- 10% yearly. This productivity increase resulted in increased jobs between 1 and 4% (Japan) in that first period followed by a shrinking between 0.5 to 3% in the following periods: this is the result of an expansion of factories in the first period and expansion of automation in the second that led to a first wave of job losses.
Responding to job losses, assembly lines workers reinvented themselves as cooks, waiters, guide, taxi drivers…, others trained for new skills …
Automation is far from being complete and will continue in this decade although at a slowing pace for blue collars (on factories floor most of menial work has already been automated).
The application of AI is leading to white collar jobs automation, first in line being telemarketers, receptionists, legal assistants, back office workers, data analysts, data harvesters, … If in the past the highest impact was on lower salary jobs, repetitive jobs usually held by low education workers, now the impact is expected on better paid, better educated workers.
A recent study by Metropolitan Policy Program at Brookings is showing the foreseen impact of AI in a variety of white collar jobs, pointing out that whilst robotics and computers automated the factory floor and the repetitive tasks of white collar workers AI is clearly bent over high paid, high educated workers.