This is Part 2 of our AI-Proof Your Career series. If you haven't read the foundational article (How to AI-Proof Your Career) start there. Our series will conclude with an expert-led AI training course later this year.
Once you've decided that AI fluency is a worthwhile skill, a practical follow up would be to ask what specifically you should be learning. Since this is such a new topic it’s easy to feel unsure of where to begin your journey.
When people think about developing AI expertise they likely point to skills related to the actual building of the technology (machine learning, neural networks, programming languages - things you'll likely never need if you’re reading this article). We’re more focused on the skills most working professionals can actually learn immediately. The goal isn't to build more advanced AI models, it’s to learn how to use the existing tools to make yourself better at the job you already have.
Below are the five skills that matter most for that goal. For each one, you'll find why it's worth your time and a quick way to gauge where you stand today. None require a technical background, and all of them build on experience you've already spent years developing.
1. Practical AI Fluency
These are the basic foundational skills. Can you write a clear prompt? Can you hand a real task to an AI tool and get something useful back? Can you chat back and forth with it to improve the result?
What good looks like: You don't just ask "write me an email." You give it context, a goal, a tone, and an example, and you refine from there. You treat it like you’re training a new hire, not typing into a search bar.
Quick self-check: When was the last time you used an AI tool on actual work, not just for fun or exploration. You had the tool complete one of your actual work tasks? If the answer is "never", this is your starting point. It's also the most learnable skill on this list and the one with the fastest payoff, so the good news is you can close this gap quickly.
2. Judgment and Quality Control
AI is not perfect. It’s often wrong. An essential part of the process is quality control. You’re an intelligent, experienced person who knows what they’re looking for. It may take some adjustments to make sure you’re receiving the outputs you desire.
What good looks like: You read AI output like an editor. You ask yourself - is the information accurate? Does it sound like my voice? Would I confidently put my name on it? It may be technically fine but wrong for your client, audience, or situation.
Quick self-check: Think about the last time AI gave you something - did you take it at face value, or did you put it through the ringer? This skill is really just your professional judgment, the thing you've spent your whole career fine tuning.
3. Emotional Intelligence
Most of your job is going to stay exactly as it is today. The parts that involve interactions with other people aren't going anywhere. If anything, as the routine work gets automated, it will become even more essential to know how to read a room, calm a frustrated stakeholder, mentor a teammate, know when to push and when to listen.
What good looks like: You handle interpersonal situations with ease. You’re a confident communicator, you’re relied upon, people trust you to act with kindness and integrity.
Quick self-check: This one's less about a gap to close and more about a strength to protect and lean into. Ask yourself: am I investing in the human side of my work, or letting it coast. Never stop investing in your relationships.
4. Creativity and Asking the Right Questions
AI is great at answering the question you ask it, but it’s unable to decide which questions are worth asking. This type of critical thinking will remain human work. Those who are able to ask the right questions will likely be the ones who yield the best results.
What good looks like: You use AI to think with you, not to think for you. You bring it interesting problems and use it to test ideas, generate options, and find new angles. You use the output to decide the correct direction to pursue.
Quick self-check: Are you using AI to skip the thinking part, or to amplify it? If you're outsourcing the judgment calls, that's a habit worth breaking.
5. Continuous Learning
The tools everyone's obsessed with right now will look vastly different in a couple of years. It’s important to embrace the constant changes by adopting a mindset geared towards continuous learning.
What good looks like: You treat learning as an ongoing habit, not a one-time event. You remain curious and want to constantly try learning new things. You believe learning is an opportunity, not a burden.
Quick self-check: What’s your goal? Do you want to add a tool that will help you quickly solve a problem today? Or do you want to become an expert who continually provides value and sets themselves apart from others?
Where to Start
Start with any of the five where you know you have a clear skill gap. For most people it's the first one, practical fluency, simply because it's the one you build through practice, and it’s hard to practice when you don’t know where to start.
That's exactly why we built our upcoming live AI workshop the way we did: small cohorts, hands-on practice with real prompts, and feedback in the moment so you're not guessing whether you're doing it right.
More information on the training sessions will be announced shortly!
