Wise Up to What’s Most Important in a Career
Concept Stretching for Adaptability
Click here to jump past the motivational preamble and get right to the tactical AI suggestions.
It is impossible to grasp what a forty-year career will bring. Around 1984, as an undergraduate, I was gifted an HP programmable calculator. It could do trig functions, execute polynomial formulas, and do basic linear algebra. I was thrilled! It saved me a lot of boring number crunching. That was advanced technology forty years ago.
Now I can talk to a machine and have it problem solve or create with me. The tech from my school days bears little resemblance to today’s leading edge.
The implications of rapid change, much of it due to technology, are profound. As of 2021, the job market in IT, finance, and sales demanded 10% more skills each year. Even more striking, 30% of the skills needed just three years earlier were no longer in demand. A study that examined jobs over about 80 years showed most people now work in professions that didn’t exist in 1940.
No crystal ball exists for most workplaces. Uncertainty is the only certainty. That means adaptability is the most important skill.
And becoming adaptable requires a very different path from what academia instills.
The Wisdom Pivot
People in the education realm have noticed the changing world. There’s little that educators agree on, but one is that so-called durable or 21st-century skills like creativity, critical thinking, communication, collaboration, and tech savvy are of increasing importance.
I call these ‘wisdom’ skills, so people have a label they intuitively understand, but it’s an imperfect choice. For example, there are qualities inherent in my notion of wisdom, like open-mindedness and divergent thinking, that may not fit some cultural interpretations.
Wisdom skills contrast with expertise skills most visibly in being oriented toward a wholistic interpretation instead of a detailed one. We consider experts to be people who know a lot that the average human doesn’t in some area, mainly in areas of specialized need. Wisdom has different connotations, including the ability to gather relevant information, consider many perspectives, choose the right assistance and processes, and to make sound judgments over a range of challenges.
You can’t bank on the specialized knowledge you have now – your expertise. Knowledge in many fields has a half-life of a few years. Rather, the ability to quickly develop new expertise, or to be decently good at many things, are the skill imperatives.
The bane of expertise is near-sightedness. If people spend too much time in narrow areas of knowledge and thought, then they may never gain other perspectives that undergird adaptability. They live in an echo chamber.
The idea of expertise myopia has evidence. When experts were asked to forecast geopolitical events, on average they did scarcely better than random chance. Still, they were confident in their forecasts. The best forecasters tended to think wisely – focusing on key information, on processes and meta-principles, and were more willing to change their minds.
There is widespread understanding that the skills of wisdom are driven by experience and associated reflection. Not just any experience, but varied ones so we accumulate many perspectives from many situations. That’s a process that takes a long time. Educators may employ experience substitutes like case studies, virtual environments, and games, but those have been too laborious to create for widespread impact.
With today’s AI, people can easily build pseudo-experiences that represent wisdom aspects of current jobs or professional aspirations. They can reflect often on key concepts and relate them to diverse areas. Maybe you need to practice communicating with a certain kind of personality, or you want to get better at recognizing when a project is in trouble.
Most of the time, what you’re trying to improve is some kind of judgment skill.
Judgment Playgrounds
People get better through experience, but experience is a very slow teacher that often doesn’t show up.
Many skills we never get to practice. Learning wisdom skills is naturally limited by the range of our experiences. I worked with numerous communities in my career, including aviation, public health, law enforcement, disaster management, and military operations. A critical part of each of their jobs is preparing for situations they might never see in a thousand careers.
Our own jobs have unusual situations too, and how you handle those can separate the best from the rest. What should you do when a co-worker starts behaving very strangely? When do you jettison a customer because they’re not worth the aggravation? When should you bend the rules because they weren’t designed well for today’s situation? What are the long-term implications of a key strategic decision? You must be around a long time to experience a lot of unusual situations.
AI can be a great help in creating meaningful pseudo-experiences and critical thought partners. Broadening your perspectives is no longer limited to reading what happened to others. Now you can, to some degree, engineer your own mini-experience.
Imagine that you aspire to be an insurance actuary in the next rung of your career. The hardest skills in many professions are nuanced judgments in unusual situations, and AI can give you some guided practice if prodded by something like:
To AI: “I want to you to run a game to teach me the judgment skills for insurance actuaries, which is a job I aspire to.
The game will present a scenario in which I am asked to judge and/or describe a rationale or judgment process. Based on my response, provide feedback if I seem stuck, starting with hints and progressing to explain a key concept or give the answer when I repeatedly err. Then decide the difficulty level of the next scenario and repeat the steps.
More difficult scenarios involve less complete or certain information, multiple objectives, or competing values. The easiest scenarios should be doable by a college educated adult within three attempts.
Output a title of the type of judgment, describe the scenario, the judgment or explanation required of me, and the available information, at a minimum. Write the output in a consistent format. Run the game entirely in text. Do not write code. Begin.”
A company with inexperienced actuaries may use something similar, perhaps with added emphasis on concepts and situations that the guru actuaries see as most troubling to newbies.
I believe the training and skill assessment worlds will soon flood with job emulation games and platforms. The best way to measure hiring risk, assess worker aptitude, and accelerate learning is to have people do the job, even if only virtually. Games used to be expensive to make, but the constraint of development risk has largely eroded because of AI, so much so that each person can create their own wisdom skill practice.
Concept Stretching
Sometimes it’s not the entire job that needs practice, but particular concepts need strengthening. Then, what I call concept stretching is important.
Let’s take the perspective of an insurance employer who wants new actuary hires to get to the expertise level of their best as soon as possible. The best actuaries in the company say the newbies over-apply the “law of large numbers,” which can lead them to bad decisions.
The “law of large numbers” is a statistical concept. An actuary’s job is assessing the level of risk the company is taking, and the law of large numbers says that if more people are insured, then the estimated risk gets closer to the true risk. Except that’s not always the case, and AI can help the inexperienced understand when and why by helping to illuminate the boundaries of concept validity.
Explore Concept Boundaries
A company could now quickly fill this niche training need through a simple exchange with the AI (and ample testing for accuracy).
To AI: “Create a game that presents actuarial scenarios to me and asks whether I think the "law of large numbers" applies. Give me situations of increasing difficulty, and allow yes, no, or maybe answers. Ask whether I want hints or explanations.”
This kind of training will certainly help for a specific job function, but remember the ultimate goal is adaptability. That means finding ways to extend concepts you encounter in your work or life to similar ones in other domains.
There are two ways to do so. Brains have a vast web of concepts. The utility of this concept web is related to how many concepts there are, how accurately the concepts are represented, and the richness of relationships between concepts.
Seek Interdisciplinary Analogies
One key approach is to seek identical or analogous concepts in other domains. Often the key concepts are similar to ones in other professions or in some aspect of society. Sometimes identical concepts are given a different name in another field. In the actuarial example, the law of large numbers is a broadly applicable statistical property, so there’s direct application to many professions. At other times, the best you can do is ask about analogous situations. Either way, the key is to give a bigger life to the concept.
To AI: “Present me with situations outside of actuary in which I have to decide whether the law of large numbers applies.”
To AI: “Run a game where you present a series of concepts that might or might not be an analogy (but not identical) to the law of large numbers in actuarial science. My job is to tell you if they are analogies and in what way, then you give your opinion, and then we try again.”
Debate Abstract Heuristics
Finally, two concepts can relate to one another at a more abstract level. That abstraction could create new concepts, especially concepts that help us quickly assess the type of situation or range of applicability of other concepts. Such rules of thumb, or heuristics, are often unconscious, but we can use AI to bring words to such intuition, at least for further debate.
You can use AI to assist the creation of abstract concepts by asking it to provide an overarching rationale for the similarity it sees in analogous concepts. Reflection on the nature and validity of the abstraction are just the kind of thinking that wisdom loves.
To AI: "Choose five concepts analogous to the law of large numbers. For each concept, explain its rationale for similarity to the law of large numbers. Then, create one or more heuristics that capture the nature of these similarities. For each heuristic, list all the concepts it applies to, explain its range of applicability, including situations where it works well and where it might break down or be misleading. Finally, provide an example of how this heuristic might be applied in a real-world scenario."
Through that prompt I learned that AI thinks “the collective outperforms the individual” is a good heuristic that could be applied to the Law of Large Numbers, Wisdom of the Crowd (large groups of people are collectively smarter than individual experts), and the Central Limit Theorem (estimates of the average are on a bell curve). All of them are concepts that an answer can get better if we can analyze lots of examples, but it also describes situations where the heuristic isn’t correct. The concepts connected by the heuristic may be new ones to us and the AI user but pointing us to new knowledge that broadens our thinking and connects to what we already know is precisely the point.
Finding your wisdom is aided by varied experiences, and AI will increasingly give us engaging, job-specific educational options. But you often don’t need anything fancy…just the desire to stretch concepts a bit.
All workers are facing career uncertainty, including highly educated professionals. AI can be a powerful ally in growing broad-based career skills like critical thinking, helping us develop wisdom skills that traditionally took lifetimes to cultivate. By leveraging AI, we can practice judgment in rare scenarios, stretch our understanding of key concepts, and accelerate the acquisition of adaptable skills.
Uncertainty isn’t cause for paralysis, but that can be the effect if we are not attentive to our long-term learning needs. Learning for a rainy day may seem useless if one can’t be clear about the learning needs. By focusing on adaptation itself – on the perspective broadening, concept association, and heuristics that broadly shape thinking – you will be better prepared when novel situations arise.
The speed with which AI can develop pseudo-experiences that help build judgment and other wisdom-related skills has changed the game. Not only can people be exposed to more situations, but now it can fit your learning needs more precisely, whether in emulating a job function, or in learning concepts more robustly and creating abstract connections that are fuel for future adaptation.
When I present this adaptation message to college students and early career professionals, it can feel like I’m piling on. They’re working hard to be great specialists, and I’m telling them that’s not enough. Take a deep breath. This is a marathon, not a sprint. Tweak one of the prompts in this article to relate to one notion you used today. It’ll take seconds, and the lessons from little excursions will accumulate. When the world throws your career a monkey wrench, you’ll still have options.
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