The 4 skilled categories
When it involves artificial intelligence (AI), many expect a shiny future with various recent revenue streams, reduced expenses and ultimately higher profits. Others worry about jobs being lost to machines.
So is AI stealing human jobs? Or to place it one other way: Should we replace humans with AI?
Before we answer these questions, we first have to categorize various kinds of jobs. I’ve created a table below that divides them into 4 categories based on the yes or no answers to 2 questions. The 4 cells describe who or what’s presupposed to perform a particular task that falls into that specific category. Jobs will be described as roles, and the tasks are the issues that must be solved inside those roles.
Of course, the table is simplified for illustrative purposes and isn’t mutually exclusive, collectively exhaustive (MECE). Still, it should give finance, technology and management experts loads of food for thought.
Do we’ve got (almost) zero or low tolerance for mistakes at work? | |||
Yes | NO | ||
Can we solve it? the issue in an automatic way based only on objective facts and easy rules and principles? |
Yes
NO |
1. Traditional computer programs and other technologies, mainly for process automation
3. People |
2. AI, traditional computer programs and other technologies
4. AI and folks |
1. Traditional computer programs and other technologies, mainly for process automation
This category includes, but isn’t limited to, certain trading, money transfer, settlement, clearing and other operations at banks, trading venues and investment management firms. In the narrower sense, the usage of people is commonly needed for technical, economic, legal and regulatory reasons, amongst other things. Some people may resist streamlined processes across the board without human intervention. They are likely to follow work that will be done by machine.
2. AI, traditional computer programs and other technologies
Tasks which will fall into this category include recommending web content or applications based on user preferences and former web or app behavior. AI results can leave room for interpretation. The consequences of the choice making are usually not as critical or significant. Even traditional computer programs and other technologies will be applied. Results from such applications often deliver more and higher results than humans and at scale.
3. People
This category includes the activities of business managers, politicians or other individuals who make decisions not only on the premise of objective facts and easy rules and principles, but additionally on the premise of long-term perspectives and human values. Decision-making processes are frequently one-off, not automatic and sometimes have irreversible consequences. Human decisions are usually not necessarily based only on short-term, economic and rational reasons. What at first glance look like spontaneous or irrational reactions may very well be based on subtle calculations. In addition, people can have subjective opinions, apply different time scales, and act in response to complicated rules and principles that can’t be reduced to relatively easy algorithms. Unlike machines, humans can take responsibility for an consequence and understand the legal and ethical obligations.
4. AI and folks
This is an area where humans and AI (machines) compete for the job. People will be replaced by machines if all the following conditions are met:
- Machines provide a greater solution than humans based on cost, production quantity and quality, etc.
- There are not any legal restrictions.
- It is suitable in accordance with normal social conventions and there is no such thing as a ethical obligation to do otherwise.
In other cases, humans and machines can work together. We can solve problems by referring to the (past) data and imagining an often complex future state. Humans must be good on the latter: we’re “teachers” who know and may define what a right or fallacious answer or a future state is. We may take responsibility for decision making and its outcomes. AI has mastered many things and solved various problems that were standardized by humans, but in other ways it might be overthought by a toddler. It requires frequent human intervention.
Stock picking, portfolio management, customer support, sales, and other activities involving human interaction can fall into this category. Human-machine collaboration has also worked well within the artistic field, for instance in the shape of AI-supported computer graphics.
The solution: Focus on what only humans can do and do well
To avoid losing our jobs to machines, we want to acknowledge and concentrate on what only we humans can do and excel at. We must keep in mind that only humans can define each job, what it requires and doesn’t require, and whether it might be assigned to machines. Dividing jobs into sub-jobs after which categorizing them into these groups is something only humans can do and must be good at.
Additionally, people can change a job, redefine it, and move it from one category to a different. In this fashion, humans can and will maximize the worth of machines in order that we will concentrate on more meaningful, productive, and enjoyable activities. Ultimately, humans have feelings: These are sometimes unstable and seemingly irrational. Fortunately, machines haven’t got them and only perform the tasks that we humans can assign to them.
Of course, AI – “machines” – are only as intelligent as the info it learns from, the models and techniques used, and the people related to it. Raw data itself, data cleansing, and knowledge and experience of how the info is generated, collected, processed, stored, and analyzed are vital. It can also be vital to pick out an acceptable model and understand the goal of the evaluation. Subjective expert judgment based on knowledge and experience can also be crucial.
For various legal, ethical and economic reasons, not all human jobs must be replaced by machines. But humans equipped with machines, using a mixture of AI and human intelligence, will replace some jobs. AI may transform our businesses, nevertheless it isn’t the existential threat to human jobs that a lot of us fear. Rather, the human teams that successfully adapt to the evolving landscape will persevere. Those who don’t will likely make themselves redundant.
Essentially, it’s our job – we humans, not the machines – to check the board and make our move.
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