Learn AI Without Wasting Your Time🧑🏿💻
Learn AI based on the job you actually do.
“Learn AI!” 📢
Professionals keep hearing they need to learn AI or risk being left behind. The problem is, when the conversation prioritizes the threat with no starting point, it becomes more overwhelming than useful.
You don’t need to learn everything about AI, just what’s relevant to your role.
I couldn’t cover every job in existence, but here’s a non-exhaustive breakdown to help some of you get started:
Engineers🧑🏿💻
What to learn:
Machine learning basics – You’ll need to understand how models are trained, evaluated, and deployed if you want to work on AI products, even at a high level. Free platforms like Google’s ML Crash Course and Fast.ai are great places to get started.
Core ML frameworks – Understanding the tools used to build and run machine learning models helps you collaborate across teams, even if you're not the one training models. Get familiar with how frameworks like PyTorch, TensorFlow, and Scikit-learn work at a high level.
Python – It’s the dominant language for AI. Even if you code in something else, most AI/ML tooling, libraries, and documentation live in Python. You can begin learning Python on Free Code Camp.
LLM integration – A lot of apps rely on GPT, Claude, or similar models through APIs. Learn how to integrate them into products to stay relevant in modern engineering workflows.
Why you should learn it:
As an engineer, you’ll be expected to build, scale, or maintain AI-powered features, not just be aware they exist.
Product Managers⚒
What to learn:
Prompt engineering - Understanding how LLMs respond to inputs helps you write clearer specs, define edge cases, and improve UX for AI-powered features.
AI ethics & limitations - You need to understand the legal, ethical, and functional limits of AI to build responsibly and avoid harming users.
Data literacy - You’ll likely be collaborating with data scientists and engineers so being able to speak their language/understand the jargon helps you scope work accurately and avoid confusion.
Why you should learn it:
As the person driving the implementation of AI features, it’s important to understand what’s possible, or risky, to your roadmap and users.
Designers (UX/UI)🎨
What to learn:
Generative design tools - Tools like Midjourney, DALL-E, and Runway can speed up how you brainstorm, prototype, and execute visual concepts. You can create mood boards, generate UI elements, or explore different styles without starting from scratch. You’re not necessarily replacing your creativity process, but speeding up the early stages of design and helping you iterate faster.
AI/UX patterns - AI changes how people interact with products. Features like autocomplete, search suggestions, adaptive layouts, and chat interfaces don’t follow traditional UI patterns. You need to understand how users expect to engage with AI-driven features so you can design experiences that feel intuitive and helpful, but still human.
Why you should learn it:
AI is changing how digital products work which means the user experience is changing too. While you’re designing for users, you’re also designing with AI as a core part of the experience. Figuring out how it fits into your design flow keeps your work relevant and users better supported.
Marketers📣
What to learn:
Prompting for content creation – You’ll need to train AI to write in your tone, which means using structured prompting. That means giving clear context, formatting instructions, tone guidance, and examples so it’s actually creating for you, not just rewriting what you already said.
AI SEO tools – Tools like Clearscope and Surfer SEO use AI to analyze top-performing content and suggest keywords, structure, and readability tweaks. They help you create content that ranks and sounds human instead of just using a bunch of keywords and hoping for the best.
Content generation tools – Jasper, Copy.ai, and ChatGPT can help you write blogs, landing pages, ad copy, captions, and emails faster. However, they still need direction. If you don’t guide the AI with your brand voice, audience, goals, and format, you’ll spend more time fixing generated content rather than using it.
Why you should learn it:
AI is changing how content gets produced. If you know how to use it right, your work stays competitive and you’ll do it in half the time.
Data Analysts📊
What to learn:
ML modeling – You don’t need to build models from scratch, but you do need to understand how they work. You should know the fundamentals (training data, features, overfitting, etc.) because it will help you keep up in convos with data scientists and not misread what a model’s results mean.
Python for data tasks – You can use other languages, but Python is great for automating reports, building data pipelines, running analysis, and working directly with AI tools. If you want to move beyond pulling queries and start doing more impactful work with AI, learn Python.
Model evaluation metrics – Knowing what precision, recall, and F1 score actually measure helps you figure out if a model is actually working or just looks like it is. This makes better at catching weak models before they go live.
Why you should learn it:
Aside from cleaning data, you’re using it to drive decisions. AI is becoming a bigger part of that process so knowing how it works puts you ahead.
Technical Recruiters🫱🏾🫲🏿
What to learn:
AI sourcing tools – Tools like SeekOut use AI to bring awareness to candidate profiles that don’t show up in regular searches. They analyze things like job history, skills, and activity across platforms. Learning how to use these tools and reviewing the quality of their results helps you find potential candidates faster. They should be used as an addition to your current sourcing strategy, but not as a total replacement.
Boolean prompting with AI – Tools like ChatGPT can create complex Boolean strings for you based on the role and candidate type you’re looking for. Just describe what you need and the AI gives you search queries you can use in LinkedIn or your ATS. You can also use it to write personalized outreach and tailor messages by tone, level, or background.
Bias in AI tools – Many AI hiring tools are trained on biased data like old resumes or performance reviews from biased companies. These tools can unintentionally screen out qualified candidates, especially from underrepresented groups. If you're using them, you need to understand where bias shows up so you can protect your pipeline and give every candidate a fair shot.
Why you should learn it:
AI is reshaping hiring for both recruiters and job seekers. It can make your job easier, but it can also dismiss qualified talent if you’re not paying attention. Learning how to use these tools responsibly and catch their mistakes helps you build a better hiring process.
Career Coaches👩🏽🏫
What to learn:
AI resume reviewers – Tools like Teal HQ and Jobscan analyze resumes and offer suggestions based on job descriptions or ATS patterns. However, they’re not always accurate. You should know how to interpret their feedback, filter out what doesn’t make sense, and explain to clients which suggestions are worth applying and when to skip them.
Mock interviews with AI – Tools like Google’s Interview Warmup can help clients practice answering real interview questions. They’re useful for helping people hear themselves, get more comfortable under pressure, and identify areas where their answers need improvement. You should pair these tools with human coaching for a stronger prep strategy.
Prompting for job search strategy – ChatGPT can help your clients write better cover letters, tailor resumes, summarize experience, and explore new career paths. Once you learn how to prompt it, you can hand over clear instructions that make their job search less overwhelming and more focused.
Why you should learn it:
Your clients are already using AI tools or getting screened by them. If you want to coach them effectively, you need to understand what they’re up against and how to use those same tools to their advantage.
Developer Advocates🙋🏽♂️
Prompt engineering - Writing code samples, docs, and demos takes time. Learning how to prompt AI correctly helps you generate good first drafts, speeds up your workflow, and keeps content clear and helpful for other devs.
OpenAI/ AI APIs - AI APIs are becoming a regular part of many dev workflows. Understanding how they work lets you build with them, talk about them confidently, and support others doing the same.
Code explanation via LLMs - AI tools can help break down complex code into something more digestible whether you're livestreaming, writing tutorials, or just walking through a concept. It's a useful way to make your content more accessible.
Why you should learn it:
Your role is all about helping developers discover and use tools. Learning how AI fits into that ecosystem helps you stay current and support your audience in more relevant ways.
Program Managers🗂️
AI scoping and risk planning - AI features work differently than traditional software. Knowing how to estimate timelines and anticipate risks makes it easier to set realistic expectations and avoid surprises during delivery.
AI collaboration tools - Tools like Notion AI can help you manage projects more efficiently. They're helpful for things like summarizing updates, drafting briefs, or organizing work across teams.
Ethical decision making - When AI features are on the table, ethical questions come up, especially around privacy, fairness, and bias. Having a good understanding helps you ask better questions and contribute to more thoughtful decision making.
Why you should learn it:
You don’t need to build AI but you do need to manage how it gets built and delivered. Knowing how it works helps you lead with more confidence and allows you to make better calls with your team.
Sales Professionals💰
What to learn:
AI powered outreach tools - Tools like Lavender AI or ChatGPT help improve your email writing by giving instant feedback on tone, clarity, and structure. Once you get comfortable with them, they can save you time and improve response rates.
CRM automation - Platforms like Salesforce and Apollo are adding more AI features that support prospecting, follow-ups, and insights. Learning how to prompt these tools can help you stay organized and close more.
Objection handling with AI - You can use AI tools to prepare for common objections or fine-tune how you respond to tough questions. It’s a good way to practice and improve your pitch.
Why you should learn it:
AI tools are becoming a bigger part of the sales process. Learning how to use them well helps you stay efficient, personalize your outreach, and adapt to how selling products and services is changing.
Thanks for the free game
Great article & breakdown!