Are you still doing algorithmic work? Or are you lucky enough to do heuristic work?
No idea what I’m talking about? Well, algorithmic vs. heuristic work is a concept that behavioural scientists came up with in oder to describe how creative and individual our work is.
Simply put, an algorithmic task is one in which you follow a set of established instructions down a single pathway to one conclusion.
A heuristic task is the opposite. Precisely because no algorithm exists for it, you have to experiment with possibilities and devise a novel solution.
Now, which of the two sounds more like the job you are currently doing?
If you are having someone telling you exactly what to do, you are merely following instructions, and tend to do the same tedious task over and over again, you are doing mostly algorithmic work. It is relatively simple, repetitive work that doesn’t require much thinking on your own.
In the past, especially due to the industrialisation, most jobs used to be like that. People worked after a very clearly laid-out plan, may it be in blue-collar jobs or even in white-collar jobs such as accounting, law or business. There was always a guideline or a formula or a manual that could be followed.
This however has been changing in the last two decades. Algorithmic work is more and more being outsourced to countries where well-trained employees can produce the same kind of results for a fraction of the price. It’s either that, or there is software to replace the need of human work altogether. Algorithmic work is more and more becoming obsolete.
Which is why heuristic work is becoming increasingly more important.
As mentioned before, heuristic work means that there is no clear predetermined path. The solution to the problem you’re working on has not been found yet and therefore there is no existing manual of how to get there. Which means that you are being a pioneer. Pretty scary stuff, huh? But also exciting; and challenging; and rewarding; and impossible to replace.
These are the kind of jobs that are more and more becoming predominant in the western culture. McKinsey estimates that about 70 percent of the job growth in the US comes from heuristic work. And sure, these kind of jobs require you to be highly educated and creative and innovative, which in a way make them “harder”. But if you are then able to do this kind of heuristic work, you will have a competitive advantage over large parts of the world, and your results will not be easily reproduced.
Motivation for Algorithmic vs. Heuristic work
But the difference between algorithmic and heuristic work goes much deeper than just the safety of the job. The two describe completely different types of workers with completely different sets of values.
While algorithmic work is mostly viewed as a chore and a means to an end, heuristic work is often perceived as intrinsically motivating and inspiring. Many of the people who do heuristic work don’t do it for the money and might even be willing to relinquish money in order to keep doing what they do.
This is interesting to consider: while algorithmic work needs external motivation (also known as carrots and sticks), heuristic work is often completely free of monetary reward, supervision, or punishment if workers don’t perform. People simply work because they see the value in the work they do and they enjoy the challenge that this type of work provides. Just think of open source projects like Wikipedia. No-one gets paid, no-one gets supervised and yet a large group of like-minded people does this kind of heuristic work together since it makes them feel challenged and valued.
The separation of jobs into algorithmic and heuristic provides a unique view on how we work. For employees it is worth considering if they are really doing what they love and what they would want to work on even in their free time. If they are not doing a job that is challenging and creative and that gives them the opportunity to implement their own ideas, it is likely that they are doing easily replaceable algorithmic work. This means they will be less motivated, will care less about the result and will not be fulfilled by their job in general.
For employers on the other hand it is necessary to think about how they want to get their staff to perform well. Is a salary raise or a bonus at the end of the year really the best way to do so? Do they really have to supervise and measure the performance of their employees all day, every day? Or shouldn’t companies rather wonder why it is necessary for them to resort to this kind of extrinsic measures in the first place? Maybe they should try to shift the work they offer from algorithmic to heuristic instead.