Tagged: labor

Machine decision making and workflow engineering

Did you ever want to climb Mount Everest?

If you did, you would have to split such a goal into many tasks: You would first need to find out what resources are needed for it, who could help you, how to prepare mentally and physically, etc. You would come up with a list of tasks that, in a sequence, form your plan of achieving the goal.

The same logic applies to all goals we humans have, both in companies and private lives, and it also applies when evaluting what tasks, given a goal, can be outsourced to machine decision making.

The best to way to conduct such an analysis is to view organizational goals as a sequence of inter-related job tasks, and then evaluate which particular sub-tasks humans are best at handling, and vice versa.

  1. Define the end goal (e.g., launch a marketing campaign)
  2. Define the steps needed to achieve that goal (strategy) (e.g., decide targeting, write ads, define budget, optimize spend)
  3. Divide each step into sub-tasks (e.g., decide targeting: analyze past campaigns, analyze needs from social media)
  4. Evaluate (e.g., on a scale of 1-5) how well machine and human perform in each sub-task (e.g., write ads: human = 5, machine = 1)
  5. Look at the entire chain and identify points of synergy (where machine can be used to enhance human work or vice versa (e.g., analyze social media by supervised machine learning where crowd workers tag tweets).

We find, by applying such logic, that there are plenty of such tasks in organizational workflows that currently cannot be outsourced to machines, out of variety of reasons. Sometimes the reasons relate to manual processes, i.e. the overall context does not support optimal carrying out of tasks. An example: currently, I’m manually downloading receipts from a digital marketing service account => I have to manually log-in and retrieve the receipts as PDF files, and then send them as email attachment to book-keeping. Ideally, the book-keeping system would just retrieve the receipts via an application programming interface (API) automatically, eliminating this unnecessary part of human labor.

At the same time, we should a) work to remove unnecessary barrier to work automation where it is feasible, b) while thinking of ways to provide optimal synergy from human and machine work inputs. This is not about optimizing individual work tasks, but optimizing the entire workflows toward reaching a specific goal. At the moment, there is little research and attention paid to this kind of comprehensive planning, which I call ”workflow engineering”.

What jobs are safe from AI?

There is enormous concern about machine learning and AI replacing human workers. However, according to several economists, and also according to past experience ranging back all the way to the industrial revolution of the 18th century (which caused major distress at the time), the replacement of human workers is not permanent but there will be new jobs to replace the replaced jobs (as postulated by the Schumpeterian hypothesis). In this post, I will briefly share some ideas on what jobs are relatively safe from AI, and how should an individual member of the workforce increase his or her chances of being competitive in the job market of the future.

“Insofar as they are economic problems at all, the world’s problems in this generation and the next are problems of scarcity, not of intolerable abundance. The bogeyman of automation consumes worrying capacity that should be saved for real problems . . .” -Herbert Simon, 1966

What jobs are safe from AI?

The ones involving:

  1. creativity – machine can ”draw” and ”compose” but it can’t develop a business plan.
  2. interpretation – even in law which is codified in most countries, lawyers use judgment and interpretation. Cannot be replaced as it currently stands.
  3. transaction costs – robots could conduct a surgery and even evaluate before that if a surgery is needed, but in between you need people to explain things, to prepare the patients, etc. Most service chains require a lot of mobility and communication, i.e. transaction costs, that are to be handled by people.

How to avoid losing your job to AI?

Make sure your skills are complementary to automation, not substitute of it. For example, if you have great copywriting skills, there was actually never a better time to be a marketer, as digital platforms enable you to reach all the audiences with a few clicks. The machine cannot write compelling ads, so your skills are complementary. The increased automation does not reduce the need for creativity; it amplifies it.

If the machine would learn to be creative in a meaningful way (which is far far away, realistically speaking), then you’d do some other complementary task.

The point is: there is always some part of the process you can complement.

Fear not. Machines will not take all human jobs because not all human jobs exist yet. Machines and software will take care of some parts of service chains, even to a great extent but in fact that will enhance the functioning of the whole chain, and also that of human labor (consider the amplification example of online copywriting). New jobs that we still cannot vision will be created, as needs and human imagination keep evolving.

The answer is in creative destruction: People won’t stop coming up with things to offer because of machines. And other people won’t stop wanting those things because of machines. Jobs will remain also in the era of AI. The key is not to complain about someone taking your job, but to think of other things to offer, and develop your personal competences accordingly. Even if you won’t, the next guy will. There’s no stopping creativity.

Read more:

  • Scherer, F. M. (1986). Innovation and Growth: Schumpeterian Perspectives (MIT Press Books). The MIT Press.
  • David, H. (2015). Why are there still so many jobs? The history and future of workplace automation. The Journal of Economic Perspectives, 29(3), 3–30.