Prompt managers are coming: understand how artificial intelligence will change the future of work
Employees and managers have gained new skills and demands with the arrival of artificial intelligence in companies.
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Who watched the movie “Interestellar”, had the opportunity to understand more clearly the concept of doubling time. In my view, cinema has the function of creating images in people's heads: it prepares us for what is emerging, causes this “emergence” or simply communicates what already exists, but that we still do not know.
Time is not so linear, despite what we think. It is like a silk thrown on top of a chair, which is with overlapping ripples, full of folds.If we were a small being (which, in fact, we are, we are before the universe) walking on silk, we could see the bright future through one of these ripples closer to us, even though we are in the present.
As the executive leader of my company, one of the necessary skills is to summarize meetings and create minutes, with important points, learnings, tasks and responsible. A capacity that is practically invisible, but necessary, and until recently, exclusively human.
In recent months, several applications have emerged with artificial intelligence embedded, able to attend meetings, create summary and task listing as well as I did. After testing many, I am stepping out of this assignment and delegating to AI. Now, AI has become the one who creates and edits the summary, not me anymore.
If I am using AI this way today, there is a good chance that this – behavior delegating the summary to – AI will become commonplace in the future. This can have a significant impact on economy that we know.
It has to do with what I mean about seeing the bright future in the present and the non-linearity of time. If we extrapolate this experience from AI applications, we can assume what the next months and years of our professional life will be like.
Artificial intelligence in the economy
The last decades have been guided by the knowledge economy. For you, what was worth economically was knowing and using knowledge at the right time. This was primarily driven by the creation of personal computers and the internet, starting in the 1970s and accelerating to this day.
But what happens when that same skill becomes something that computers can do faster and sometimes as well as us?
We will likely move from executors to managers, from executing the job to learning how to allocate – resources by choosing which job to do, he said, deciding if the work is good enough and editing it when it is not.
This seems to me to mean the beginning of a transition from a knowledge economy to a resource allocation economy. We will no longer be evaluated by how much we know (because everything is available at a prompt), but by how we were able to allocate it, manage resources to get the job done and generate better results (knowing how to create the best prompts).
There is a class of professionals who dedicate themselves daily to this type of work: managers. They need to know how to evaluate talent, manage without micromanaging and estimate how long a project will take, for example. – employees people from the rest of the economy, who do the real – work do not need these skills so much today.
But what it seems to me, in this new model that seems to be emerging, is that it must happen. I suppose even novice employees will utilize AI, which will force them to take on a certain role of manager – prompt manager. Instead of managing people, they will allocate work to AI models and ensure that the work is well executed. They will need many of the same skills as managers today, albeit in slightly modified form.
Below are some qualities that today's managers need and that tomorrow's employees – prompt managers will need as part of this new allocation economy.
Clarity of vision
Managers today need to have a clear vision of the work they want and need to accomplish. Managers must craft a vision that is articulate, specific, concise, and rooted in a clear purpose. Prompt managers will need the same skill.
The better articulated the view, the more likely the prompt will execute it properly and the result will be closer to the desired one. As prompts become more specific and concise, the work done will improve. Language models may not need, by themselves, a clear purpose, but prompt managers will probably have to identify a clear purpose for their own good and engage better with the work.
Articulating a concise, specific and coherent vision is difficult.It is a skill acquired over years of work. But fortunately, this seems to me a place where language models can also help.
Refined palate
The best managers know what they want and how to talk about it. The worst managers are those who say “this is not right”, but when asked “why?”, they cannot express the problem. Or even speak something without conviction.
It is like a good wine lover who manages to elaborate on the characteristics and express them in words after trying a label, rather than an unaccustomed one who can only say that it is good or bad (even if it is not).
Prompt managers will face the same problem. The more refined your taste buds, the more language models will be able to create something coherent for them. Fortunately, language models are very good at helping humans articulate and refine their taste buds. Therefore, it is a skill that is likely to be needed significantly broadly in the future.
The ability to assess talent
If you have clarity of vision and refined taste, the next thing you need to do is evaluate who (or what) is able to perform it.
Every good manager knows that hiring well is everything. If employees are doing the work, the quality of the result will be a direct reflection of their skills and abilities.
Being able to properly judge employee skills and delegate tasks to people who can accomplish them is a significant part of what makes a good manager.
Tomorrow's prompt managers should learn the same things. They will need to know which AI models to use for which tasks. They will need to be able to quickly evaluate new models they have never used before to determine if they are good enough. They will have to know how to divide complex tasks between different suitable models to produce the highest quality work.
Model assessment will be a skill in itself. But there is reason to believe that it will be easier to evaluate models than humans, because the former are easier to test. A model is affordable day or night, is usually cheap and tends to get even more, never gets bored or complains and returns results instantly.
Thus, tomorrow's prompt managers will have an advantage in learning these competencies, such as, because current management skills are protected by the relative cost of giving a manager a team of people to work with.
Once they have gathered the necessary resources already to get the job done, they will face the next challenge: making sure the job is good.
Know when to go into detail
The best managers know when and how to go into detail. Inexperienced managers make one of two mistakes. Some micromanage tasks to the point of doing work for their employees, which is not scalable (i've been through it). Others delegate tasks to such an extent that they are not well executed or are not carried out in a manner aligned with the goals of the organization (i have also gone through this).
Good managers know when to go into detail and when to let their subordinates take the ball and move on. They know what questions to ask, when to check in, and when to leave things as they are. They understand that just because something is not done as they would do, it does not mean it has not been done well.
These are not problems that employees in the knowledge economy have to deal with. But they are exactly the kind of problems that prompt managers in the allocation economy will face.
Knowing when and how to go into detail is a skill that can be learned –, and fortunately, language models will be built to check in intelligently during crucial periods, where supervision is required. Therefore, it will not be entirely up to prompt managers to do this.
The big question is, is this a good path?
Would an allocation economy be good for humanity?
I believe that a transition from the knowledge economy to an allocation economy should not happen overnight. When I talk about doing “ prompt” management, this seems to replace – micro-skills like summarizing meetings in – emails rather than entire end-to-end tasks. This should not happen yet, at least for a while.
Even if there is capacity to replace complete tasks, there are many parts of the economy that will not be able to overcome the delay for a long time, or ever.
Days ago, I had to make an emergency purchase at a neighborhood hardware store. And when I passed the cashier, I asked to pay by credit card, and the cashier girl pointed her finger at a paper pasted on the wall on which was written “only money or debit”.
I think that's a good thing. When it comes to change, the dose makes the poison. The economy is large and complex, and I think we will have time to adapt to these changes. And the slow transfer of human thought to mechanical thought is not new. Generative AI models are part of a long-term process.
People who are better able to use language models in their everyday lives will have a significant advantage in the economy. There will be tremendous rewards for knowing how to allocate intelligently.
Today, management is a skill that only a few know about, because it is expensive to train managers: you need to give them a team of people to practice. But AI is cheap enough so that tomorrow everyone has the opportunity to be a manager. And this will significantly increase the creative potential of every human being.
It is up to us as a society to ensure that with the new and amazing tools at our disposal, we can overcome the challenges we are experiencing today — and those that are emerging faster and faster.