M.009 We need AI Skills. But what is it?
The answer is simpler than you think
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"Going forward, everyone needs AI skills."
This mantra is repeated everywhere these days, as artificial intelligence steadily infiltrates more and more areas of our lives. And its easy to understand why, since it makes intuitive sense that "AI skills" is needed to benefit from what's ahead and to not fall behind.
Yet, in the same rooms where people call for more AI skills, there is often a big shiny elephant in the form of a question that far fewer raise: What exactly do we mean with AI skills?
When I give talks on this topic, I often ask the audience if people with AI skills can raise their hands. At one talk, I had 1 out of 500 in the audience putting up their hand. Similar numbers are more the rule than the exception across different talks and teachings.
This begs the question: Is the seemingly low prevalence of AI skills because AI skills are rare? Or is it because we are confused about what exactly we mean with AI-skills, and therefore fail to see it even when it's there?
I think the latter is true. There are likely far more latent AI skills out there than we give credit for, but we simply don't see it because we don't have sufficient clarity on what AI skills really are.
The Car Analogy
A good place to start unpacking AI skills is with another technology that once changed the world: the car.
In contrast to AI, the car was demystified so long ago that we can't even remember when. If I ask an audience if anyone have car skills, I wouldn't get any insecure looks. Instead, I would likely get a follow up question: what type of car skills are you referring too?
Are we thinking about car skills in the form of being able to drive a car? Being able to evaluate which car is better suited to your personal needs? Being able to design a car to fit with someone's needs? Being able to fix and amend the functionality of a car? Or are we talking about being able to construct the key components that make up a car, like its engine?
With cars, we intuitively acknowledge that there are a lot of different types of skills we could deem as being high car skills, each depending on the purpose we are discussing.
Someone could be an excellent driver, while being totally ignorant of how the mechanics really work or which bolts to screw if the car stops. Someone else could be able to build a car from ground up, but would be left behind in the first turn on the race track if there was a race.
The issue with most discussions about AI skills is that they often don't carry these nuances related to purpose. Which is demonstrated by the fact that I seldom get the "depends on what you mean with AI skills"-question when I ask for AI-skills in talks.
Skills are always related to a purpose, and by throwing everything into one bucket we get unclarity. A likely reason is that AI is still wrapped in so much mystery. So let’s make an attempt to rectify this.
Mapping the AI Landscape
We can start extending the car analogy over to AI and see if we get any wiser about what AI skills can be if we take the different purposes into account.
Doing so reveals at least three distinctively different purposes of AI: Building and improving the AI-models (the engines). Designing and building the AI-applications (the car). And using AI-applications to solve problems (driving the car).
From this we can then separate between three broad buckets of AI skills, associated with each of the three broad purposes. This gives us AI-engineering skills, AI-designer skills, and AI-driver skills.
The Engineering skills
While we often think of generative AI models like GPT-4o, Gemini 2.5 Pro or Claude 4.0 Sonnet when we think about AI, very few of us interact with these models directly. Instead we interact with them through applications that integrate these models one way or another.
The AI models, like LLMs and diffusion models, can therefore be seen as the engine and other "raw" capabilities that are hidden under the hood of the applications. The engine is indeed important for the functionality and performance of a car, which is why we need smart engineers to both build better, more capable and more efficient engines. Being able to build AI-engines, also requires, unsurprisingly, deep technical knowledge and skills.
Because the models are so important for application performance and in final stage, usage, the top AI-engineers are in very high demand and earn increasingly big bucks. But for most of us that don't seek to build, modify or improve our own models, its less important to understand the deeper mechanics of the technology to release potential gains from AI. Just as most of us don't need to know how to build a car engine to benefit from cars.
The Designer skills
The next layer in the AI world are the applications, which is the equivalent of the actual car. It's the design, features and experiences that are built around the models and their raw capabilities. And it's the layer of the AI-world that most of us actually interact with.
Just as there are many types of cars with different features, designs and capabilities, so there are many AI-driven applications. Some are broad swiss-army type tools like ChatGPT, Claude, and Gemini. Others are integrated in existing platforms like Notion and Slack, while others again more narrow, tailored and fine-tuned to specific use cases like for example Harvey for law.
Regardless of the type of AI-application, the best builders and designers are the ones who understand the real problems of users, and that can envision and build solutions to help solve these problems. While technical AI-skills are still important to build solid AI-applications, the real value for the designer comes from complementing these technical skills with domain expertise, creativity, field competence, knowledge of UI/UX, and ability to translate abstract problems into concrete solutions.
Knowing what makes a useful car therefore requires deep understanding of driver needs, not just technical building skills.
Building AI applications has become dramatically easier due to the general democratisation of technology combined with AI. But even though more of us now can build or modify our own AI-applications (more on that in this post), most of us won't. We will primarily be users of AI-applications built by others.
The Driving skills
The last layer is where we find the driver: The user of the AI applications. The person sitting behind the wheel, trying to use the technology to solve some of their everyday problems.
While this role might seem trivial and borderline disappointing, its worth noting that it is through using applications that the real value creation for most of us can happen with AI. We all have many different problems we need to solve, and for more and more of these problems, an AI application may be the solution.
Its also worth noticing that it is the drivers who hold the keys to unlock the potential productivity gains from AI, that has yet to broadly materialise. Because many of us are currently like a person who really struggles with getting efficiently from A to B, but never even considered that the car might be the solution. Or we are the person driving a Ferrari on the highway in first gear, stopping at the first opportunity to walk home instead because we think it goes too slow.
So what then does it take to have good AI skills as a driver?
The technical aspect of the driver skill set is almost mundane. Even the most advanced AI applications today are so user-friendly that they come without manuals or formal onboarding. The technical skill needed for most of us to make progress is simply knowing what solutions exist and having an idea of what they can do. We need to know that cars are available, we need to be able to switch them on, and we need to learn how to switch gears.
The more interesting part is the other skills that make up a good driver. And most of these are deeply human skills that doesn't have anything to do with AI per se. Like domain expertise and deep knowledge in your field, that allows you more effectively delegate work, amplify your own thinking, quality control output, rethink processes, and see new opportunities. Communication skills to provide clear articulation of problems, requirements and ideas. Creative problem solving, critical thinking, curiosity, and much more.
All of this might not be equally important in every area where AI is used, just like there are different human skill sets that matter for excelling as a race car driver, and a family car driver taking the family on a road trip. But the key point is that it is the human skills that in the end of the day that affects how good drivers each of us are of AI applications.
What This Actually Means
A major blocker for value creation with AI is that too many mistakenly believe that using AI requires much technical capabilities. For most of us, it doesn't matter much, as we are primarily drivers.
This is a simple point, but it carries some important implications. For example for how organizations should go about with implementing the technology and upskill their people on AI.
Today, many companies try to boost AI-skills by sending their employees technically focused online learnings. Like "what are LLMs" or "how do they work". This is equivalent of sending mailmen training videos about how car engines work before they are allowed to drive off to deliver the mail.
The key organisational problem stalling AI-progress is not that people lack technical AI-skills. Its rather that they don't know what tools are available to them. They lack the autonomy and confidence to experiment and find good problem-solution matches. They lack clarity on which direction to go, and what internal traffic rules they must obey. And they lack the understanding that much of the skill set they already have, is what they should utilise when working with AI tools.
For each of us, a good place to start is therefore not with understanding the deeper workings of the technology, but by getting in the driver’s seat. Then, as we get comfortable with driving, we will gradually see more and more problems for which AI can be the solution. And for many of these AI use cases, the key to unlock real value will be our domain expertise, ability to communicate clearly, our understanding of tasks and processes (which i write more about here), your creativity or critical thinking.
And if you after gaining driving experience want to learn more about how the car works or how you can modify or build one of your own, then it makes sense to complement your driver’s skills with the more technical designer- or engineering skills. An exciting point about such a journey, is that we now really don't need much technical skills either to build many useful AI solutions ourselves (more on this here).
The bigger point then is perhaps that we all have to start somewhere, and for most who will use the car to pick up kids from sports practice or commute to work, it makes more sense to start by taking the car for a ride, than to dive deep into the inner workings of the engine.