Automation has the potential to transform industries. Untiring and efficient, robotics, and artificial intelligence (AI) can replace human workers, thus creating economic dislocation. Unlike humans who are prone to mistakes and ailments of the flesh, machines can be programmed to perform a task and will do so without stopping.
AI and robotics increase productivity, improve quality and quantity of output, maintain consistency, and reduce labor costs.
The McKinsey Report
McKinsey & Company, a global management consulting firm, released a report concerning the automation of 2,000+ work activities from more than 800 occupations in the United States. By quantifying the amount of time spent on these activities and the technical feasibility of automating them with current technologies like voice search, they found that 45% of activities people are paid to perform can be automated, and around 60% of all occupations could see 30% of more of their constituent activities automated.
For further analysis, McKinsey divided jobs into three categories:
1. Highly Susceptible to Automation
- Predictable physical work: 78% of the time spent can be automated. Welding and soldering on an assembly line, food preparation, packaging goods, etc.
- Data processing: 69% of time spent can be automated
- Data collection: 64% of time spent can be automated
2. Less Susceptible to Automation
- Unpredictable physical work: 25% of time spent can be automated
- Construction, forestry, raising outdoor animals, etc.
- Stakeholder interaction: 20% of the time spent can be automated.
3. Least Susceptible to Automation
- Applying expertise: 18% of time spent can be automated
- Coding, copywriting, etc.
- Managing others: 9% of time spent can be automated
Regarding jobs with high susceptibility to automation, McKinsey concluded by saying that, “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.”
The Effect of Increase Labor Wages
There was a 5% wage inflation last year, and it’s expected to be 4% this year. These increases in the minimum wage, especially in the fast-food industry, have led some brands to turn to robotics and automation to offset costs associated with higher wages.
Wendy’s, a prime example, recently announced that it plans on installing self-ordering kiosks in 1,000 of its locations by the end of the year, an endeavor that it started last year.
David Trimm, CIO of Wendy’s, explained that “there is a huge amount of pull from (franchisees) to get them.” Similarly, “They are looking to improve their automation and their labor costs, and this is a good way to do it,” says Darren Tristano, VP with Technomic, a food-service research and consulting firm.
The purpose of these kiosks is twofold. First, they greatly reduce labor costs. Second, they give younger customers the ordering experience they enjoy. Additionally, they provide high order accuracy and can collect data from customers, thus empowering marketing tactics.
The typical store will receive three kiosks for about $15,000, and it’s estimated that they will receive a payback on them in less than two years.
The End of Programming?
As AI and robotics continue to improve and evolve, many worry that economic dislocation isn’t constrained to the fast-food industry. Instead, they believe that AI’s machine learning capabilities will impinge on jobs with intellectual labor.
After all, AI can search more thoroughly, piece codes faster, and achieve better results than a human.
Exemplifying the concerns outlined above, researchers at Microsoft and the University of Cambridge have created a system called DeepCoder that can write its code using machine learning and a technique called program synthesis.
In its most basic form, program synthesis takes lines of code from existing software and pieces them together to create new programs, just like a programmer might do.
This allows people from all walks of life, from experienced coders to introductory-level students, to build basic programs.
In essence, they need only describe an idea and let the program do the heavy lifting. With a given list of inputs and outputs for each code fragment, DeepCoder learns which pieces of code were needed to achieve the desired results. It can then create programs in fractions of a second, improving over time as it learns which combinations work and which don’t.
In its current state and limitations, DeepCoder can work with a maximum of five lines of code. It won’t be taking over any programming jobs just yet, but who knows what the future holds. For now, it’s doing some of the tedious parts of programming, leaving coders to devote their time to more sophisticated work.
We’re Safe… for Now
Automation is nothing new, nor is something to be feared. We have self-checkouts in grocery stores, industrial automation in car assembly plants, and even online shopping.
For the most part, automation benefits us by replacing hard physical labor and monotonous work. It also improves our well-being by taking over jobs that pose safety issues.
Our concerns, if any, should come when advances in AI and robotics make it indistinguishable from a human mind. Until then, if there is a then, we’re okay.