Skills for the AI Workforce

Another year of rapid AI advances has created more opportunities than ever for anyone — including those just entering the field — to build software. In fact, many companies just can’t find enough skilled AI talent. Every winter holiday, I spend some time learning and building, and I hope you will too. This helps me sharpen old skills and learn new ones, and it can help you grow your career in tech. To be skilled at building AI systems, I recommend that you: - Take AI courses - Practice building AI systems - (Optionally) read research papers Let me share why each of these is important. I’ve heard some developers advise others to just plunge into building things without worrying about learning. This is bad advice! Unless you’re already surrounded by a community of experienced AI developers, plunging into building without understanding the foundations of AI means you’ll risk reinventing the wheel or — more likely — reinventing the wheel badly! For example, during interviews with job candidates, I have spoken with developers who reinvented standard RAG document chunking strategies, duplicated existing evaluation techniques for Agentic AI, or ended up with messy LLM context management code. If they had taken a couple of relevant courses, they would have better understood the building blocks that already exist. They could still rebuild these blocks from scratch if they wished, or perhaps even invent something superior to existing solutions, but they would have avoided weeks of unnecessary work. So structured learning is important! Moreover, I find taking courses really fun. Rather than watching Netflix, I prefer watching a course by a knowledgeable AI instructor any day! At the same time, taking courses alone isn’t enough. There are many lessons that you’ll gain only from hands-on practice. Learning the theory behind how an airplane works is very important to becoming a pilot, but no one has ever learned to be a pilot just by taking courses. At some point, jumping into the pilot's seat is critical! The good news is that by learning to use highly agentic coders, the process of building is the easiest it has ever been. And learning about AI building blocks might inspire you with new ideas for things to build. If I’m not feeling inspired about what projects to work on, I will usually either take courses or read research papers, and after doing this for a while, I always end up with many new ideas. Moreover, I find building really fun, and I hope you will too! [Truncated for length. Full text: https://lnkd.in/gwv8nsgN ]

I spent 3+ hours in the last 2 weeks putting together this no-nonsense curriculum so you can break into AI as a software engineer in 2025. This post (plus flowchart) gives you the latest AI trends, core skills, and tool stack you’ll need. I want to see how you use this to level up. Save it, share it, and take action. ➦ 1. LLMs (Large Language Models) This is the core of almost every AI product right now. think ChatGPT, Claude, Gemini. To be valuable here, you need to: →Design great prompts (zero-shot, CoT, role-based) →Fine-tune models (LoRA, QLoRA, PEFT, this is how you adapt LLMs for your use case) →Understand embeddings for smarter search and context →Master function calling (hooking models up to tools/APIs in your stack) →Handle hallucinations (trust me, this is a must in prod) Tools: OpenAI GPT-4o, Claude, Gemini, Hugging Face Transformers, Cohere ➦ 2. RAG (Retrieval-Augmented Generation) This is the backbone of every AI assistant/chatbot that needs to answer questions with real data (not just model memory). Key skills: -Chunking & indexing docs for vector DBs -Building smart search/retrieval pipelines -Injecting context on the fly (dynamic context) -Multi-source data retrieval (APIs, files, web scraping) -Prompt engineering for grounded, truthful responses Tools: FAISS, Pinecone, LangChain, Weaviate, ChromaDB, Haystack ➦ 3. Agentic AI & AI Agents Forget single bots. The future is teams of agents coordinating to get stuff done, think automated research, scheduling, or workflows. What to learn: -Agent design (planner/executor/researcher roles) -Long-term memory (episodic, context tracking) -Multi-agent communication & messaging -Feedback loops (self-improvement, error handling) -Tool orchestration (using APIs, CRMs, plugins) Tools: CrewAI, LangGraph, AgentOps, FlowiseAI, Superagent, ReAct Framework ➦ 4. AI Engineer You need to be able to ship, not just prototype. Get good at: -Designing & orchestrating AI workflows (combine LLMs + tools + memory) -Deploying models and managing versions -Securing API access & gateway management -CI/CD for AI (test, deploy, monitor) -Cost and latency optimization in prod -Responsible AI (privacy, explainability, fairness) Tools: Docker, FastAPI, Hugging Face Hub, Vercel, LangSmith, OpenAI API, Cloudflare Workers, GitHub Copilot ➦ 5. ML Engineer Old-school but essential. AI teams always need: -Data cleaning & feature engineering -Classical ML (XGBoost, SVM, Trees) -Deep learning (TensorFlow, PyTorch) -Model evaluation & cross-validation -Hyperparameter optimization -MLOps (tracking, deployment, experiment logging) -Scaling on cloud Tools: scikit-learn, TensorFlow, PyTorch, MLflow, Vertex AI, Apache Airflow, DVC, Kubeflow

You don’t become an expert Agentic AI developer by just learning prompts or calling an API. To build 𝘳𝘦𝘢𝘭 AI agents, you need to master a cross-disciplinary skillset — from system design and semantic search to context management, deployment, and continuous learning. I put together this visual: 𝗧𝗼𝗽 𝟱𝟬 𝗦𝗸𝗶𝗹𝗹𝘀 𝗳𝗼𝗿 𝗔𝗴𝗲𝗻𝘁𝗶𝗰 𝗔𝗜 𝗗𝗲𝘃𝗲𝗹𝗼𝗽𝗲𝗿𝘀 — the roadmap I wish I had when I started diving into building intelligent, autonomous agents. Here are some patterns I’ve observed: 1. 𝗦𝗲𝗺𝗮𝗻𝘁𝗶𝗰 𝘀𝗲𝗮𝗿𝗰𝗵, 𝘃𝗲𝗰𝘁𝗼𝗿 𝗱𝗮𝘁𝗮𝗯𝗮𝘀𝗲𝘀, 𝗮𝗻𝗱 𝗥𝗔𝗚 are non-negotiable for scalable context retrieval.     2. 𝗠𝘂𝗹𝘁𝗶-𝗮𝗴𝗲𝗻𝘁 𝗰𝗼𝗼𝗿𝗱𝗶𝗻𝗮𝘁𝗶𝗼𝗻 becomes essential when you go beyond a single use case.         3. 𝗠𝗲𝗺𝗼𝗿𝘆 𝗮𝗻𝗱 𝗽𝗲𝗿𝘀𝗼𝗻𝗮𝗹𝗶𝘇𝗮𝘁𝗶𝗼𝗻 are what differentiate a generic chatbot from an adaptive expert.     4. 𝗦𝗲𝗰𝘂𝗿𝗶𝘁𝘆, 𝗯𝗶𝗮𝘀 𝗺𝗶𝘁𝗶𝗴𝗮𝘁𝗶𝗼𝗻, 𝗮𝗻𝗱 𝗳𝗲𝗲𝗱𝗯𝗮𝗰𝗸 𝗹𝗼𝗼𝗽𝘀 make your system trustworthy and resilient.     5. 𝗛𝘂𝗺𝗮𝗻-𝗶𝗻-𝘁𝗵𝗲-𝗹𝗼𝗼𝗽, 𝘄𝗼𝗿𝗸𝗳𝗹𝗼𝘄 𝗮𝘂𝘁𝗼𝗺𝗮𝘁𝗶𝗼𝗻, and 𝗱𝗲𝗽𝗹𝗼𝘆𝗺𝗲𝗻𝘁 𝘀𝘁𝗿𝗮𝘁𝗲𝗴𝗶𝗲𝘀 bring it all into production.    If you’re serious about building in this space, treat this less like a checklist—and more like a curriculum. What would 𝘺𝘰𝘶 add to this list? And what are you focusing on right now?

Every day, I see headlines about what roles AI will replace. But the real question I hear from leaders is: What roles will AI *create*? Let’s start with the data: - ‘Artificial Intelligence Engineer’ is the #1 fastest-growing job in the U.S. - ‘Artificial Intelligence Consultant’ is the #2 fastest-growing job. - Searches for ‘AI Engineer’ increased by 1,640% in five years. - AI and data skills are the fastest-growing in importance. AI isn’t just changing how work gets done. It is reshaping our teams. And the future of teams is hybrid - humans and AI working side by side, doing what we do best. And within these hybrid teams, two new types of roles will arise: AI Ops (Super Orchestrators) and Super Contributors. 1. Super Orchestrators or AI Ops These are systems thinkers who build the foundation for how work gets done with AI. Typical job titles are GTM Engineer, AI Ops, and AI Solutions Architect. They train models, clean data, run experiments, and iterate. In effect, they create the engine that allows everyone at their company to scale their work with speed. 2. Super Contributors These are individual contributors who use AI to 10x their output. Their job titles may look the same – content marketer, account executive, customer service manager – but their impact is completely different. These folks will use AI to do more end to end tasks. They are marketers who create multi-modal content and launch entire campaigns – in the time it takes to write a blog post. They’re salespeople who automate prospecting, do deep research before every call, and connect with more buyers in a day than others do in a week. They’re customer service professionals who use AI to handle tier 1 tickets and flag at-risk accounts – so they solve complex problems proactively. Super Contributors don’t just increase impact within their teams. They drive growth for their companies. Leaders, I’d love to hear from you. What new roles are emerging in your teams? P.S. This post is part of a series on Leadership in the Age of AI. Next, I’ll share my advice on what skills to look for when hiring AI talent. The goal is to spark conversations we can all learn from. If you have a question, leave a comment! (*Data sources: LinkedIn News, Exploding Topics, World Economic Forum)

Every customer and government leader I meet is asking, “How can we make AI a force for good for our people, and not a threat?” 92% of jobs are expected to undergo some level of transformation due to advancements in AI. The work begins with identifying and enabling the new skills and training needed for AI preparedness. That’s why I’m honored to share the insights from the AI-Enabled ICT Workforce Consortium's inaugural report, “The Transformational Opportunity of AI on ICT Jobs.” This report examines the impact of AI on 47 ICT job roles and offers tailored training recommendations. It's a unique guide to the skills needed for the AI future, with recommendations that couldn't be clearer, timelier, or more urgent. Here are some of the top takeaways: - 92% of ICT jobs will undergo high or moderate transformation due to AI. - 40% of mid-level and 37% of entry-level ICT positions will see high levels of transformation. - Skills like AI ethics, responsible AI, prompt engineering, and AI literacy will become crucial. - Foundational skills such as AI literacy and data analytics are essential across all ICT roles. Read the full report here: https://lnkd.in/gWfPc8WT The risks associated with an under-skilled, unprepared workforce are global in scale, ranging from economic wage gaps to trade imbalances, technological stagnation, social and ethical issues, and national security threats. This creates a pressing need for a coordinated effort to reskill and upskill employees around the world. By investing in a long-term roadmap for an inclusive and skilled workforce, we can help all populations participate and thrive in the era of AI. Led by Cisco and joined by industry giants like Accenture, Eightfold, Google, IBM, Indeed, Intel Corporation, Microsoft, and SAP the Consortium will train and upskill 95 million people over the next 10 years through their individual organizations' commitments.

We are still in the infancy of AI. But using ChatGPT won't be enough. These skills will be crucial for years to come: Up until recently, I thought I was pretty skilled with AI. I'd been consistently learning about it every day for years. But building Searchable has genuinely opened my eyes. I'm more convinced than ever of its incredible potential. However some AI skills are more useful than others. If I had to start over and learn AI again... These are the top 10 I'd focus on: 1. Prompt Engineering → This is the difference between average or great output. → Learn this to have the AI think like an actual strategist. Tools: ChatGPT, Claude, Gemini, Perplexity. 2. AI Agents → AI that doesn't just reply, it finishes tasks end-to-end. → Use it to automate jobs you'd normally hand to an intern. Tools: OpenAI Agents, Crew AI, LangGraph, LangChain. 3. Workflow Automation → Combining tools so routine work happens without you. → Use it for tasks that are repeated like reporting. Tools: Make, Zapier, n8n, Bardeen 4. Agentic AI → AI that can plan, adapt, and self-correct on its own. → Use it for complex multi-step tasks like research. Tools: OpenAI o1, Claude, Reflexion, DSPy 5. Multimodal AI → AI that works across text, images, audio, and code. → Use this to turn a rough idea into a full campaign. Tools: Gemini, Claude, OpenAI Vision, Stable Audio 6. RAG (Retrieval-Augmented Generation) → This is teaching AI to pull from your ownable data. → Use it for tasks where accuracy is absolutely essential. Tools: Pinecone, LlamaIndex, Haystack, Elastic 7. AEO / GEO (Answer & Generative Engine Optimisation) → This is making sure your brand shows up in AI answers. → Use this if your business requires multi-step marketing. Tools: Searchable, Profound, Trakkr.ai, Mentions.so 8. AI Tool Stacking → Combine your best tools so they run as one system. → You can then build always-on workflows to cut costs. Tools: Notion AI, ClickUp AI, Airtable AI, Zapier AI 9. AI Content Generation → Content at scale without having a big marketing team. → Leverage this for daily posting, video edits, etc. Tools: Descript, Saywhat, OpusClip, ElevenLabs 10. LLM Management → This is about controlling cost and performance of AI. → Without this, you won't be able to properly track ROI. Tools: Arize AI, TruLens, Helicone, Weights & Biases A lot of folks are worried AI will "replace them." Depending on your role, it could do. But AI is coming, so my question to you is this: Will you complain it's coming and do nothing, or will you do something about it, knowing it's coming anyway? Your answer will dictate your future success. If you're interested in upskilling for AI... It's a topic I cover in my weekly newsletter, Step by Step. Join 200k+ builders getting value from it every week: https://lnkd.in/eUTCQTWb ♻️ Repost to help your network learn about AI. And follow Chris Donnelly for AI content.

If you’re AI-curious but can’t decide where to start, this one’s for you 👇 The AI space is vast. Buzzwords fly. Roles overlap. And it’s easy to get stuck wondering: 👉 Should I become a Data Scientist, ML Engineer, or Product Manager? Instead of chasing titles, map your strengths and figure out where you fit best in the AI lifecycle. 📌 I put together this infographic + a blog post to help you find your lane, with 10 clear roles you can actually train for (even without a PhD or a Stanford badge). 🚀 The 10 Career Paths in AI, Simplified: ➡️ AI/ML Researcher or Scientist – creating new algorithms, publishing papers, pushing the frontier ➡️ Applied ML Scientist / Data Scientist – solving real-world problems with models and experimentation ➡️ ML Engineer / MLOps / Software Engineer (ML) – taking models to production and scaling them ➡️ Data Engineer – building the infrastructure to move and manage data ➡️ Software Engineer – writing core product code with ML components ➡️ Data Analyst – analyzing data to drive insights and business impact ➡️ BI Analyst – working with KPIs, reporting, and decision frameworks ➡️ AI Consultant – advising teams and clients on adopting AI responsibly ➡️ AI Product or Program Manager – aligning AI capabilities with user needs and business goals ➡️ Hybrid Roles – wearing multiple hats across technical and strategic functions 🧭 How to choose the right one for you: → Start with your natural strengths: coding, communication, business thinking, or data sense → Identify the part of the AI lifecycle you enjoy most: research - build - deploy - iterate → Stack the right skills intentionally: • Coders: Python, PyTorch, prompt design, eval frameworks • Data Infra: SQL, Spark, Airflow, Lakehouse, vector DBs • Insights: Analytics, causal reasoning, dashboard tools • Translators: AI roadmap building, governance, storytelling → Focus on shipping evidence of work: demo apps, notebooks, open-source PRs, or experiments → Develop a T-shaped skill profile – go deep in one role, but stay conversational across others 💡 A few truths to keep in mind: → You don’t need to be a “10x coder” to work in AI → Problem-solving > job titles → Projects > perfect resumes → Cross-functional skills are a force multiplier – clear writing, ethical reasoning, and stakeholder empathy go a long way → There’s no “entry-level” in AI – just entry-level impact 📖 Curious to explore deeper? Check out the full blog, and save the infographic to use as a compass for your AI journey: https://lnkd.in/daQNHPyg

GenAI won't kill critical thinking. Comfortable leaders will. AMLE 's "Critical Thinking in the Age of Generative AI," a 2025 systematic review, and Microsoft's survey all point to the same tension ➤ AI can sharpen your thinking—or slowly dull it. Here are 9 ways to stay sharp: 1️⃣ "Treat AI as a first draft, never a final say"  ↳ GenAI's confident tone tricks your brain into skipping evaluation. ✅ Act on it: Ban "copy–paste" from AI into decision-critical docs. Require one human edit plus rationale before anything AI-generated moves upward. 2️⃣ "Ask AI to argue against itself"  ↳ Questioning and comparison strengthen critical thinking. ✅ Act on it: Always follow one answer with: "Now, give me the strongest counterargument." Share that practice with your team as a standard operating rule. 3️⃣ "Separate speed from wisdom"  ↳ Fast answers feel good; wise answers feel uncomfortable first. ✅ Act on it: For decisions that feel "too easy" after AI, pause and ask: "What are we not seeing?" Use AI to surface opposing viewpoints and edge cases—not just best practices. 4️⃣ "Build 'social critical thinking,' not just solo analysis"  ↳ Challenge assumptions together. ✅ Act on it: In key meetings, assign one person "AI skeptic" and another "AI translator." End with: "What assumptions are we accepting because AI made them sound reasonable?" 5️⃣ "Use AI to find blind spots, not excuses"  ↳ Confidence in AI can reduce scrutiny; leaders can reverse that. ✅ Act on it: Ask, "Whose perspective is missing?" and use AI to simulate that viewpoint. Include ethical, cultural, or stakeholder perspectives as separate prompts. 6️⃣ "Turn AI mistakes into a leadership curriculum"  ↳ Reflective use of AI strengthens thinking. ✅ Act on it: Collect "AI near-miss" stories and discuss them in leadership meetings. Ask: "What almost went wrong? What saved us? What changes next time?" 7️⃣ "Make your own thinking visible"  ↳ Leadership thinking is contagious. ✅ Act on it: Narrate your process: "Here's what AI suggested. Here's how I challenged it. Here's the decision." Encourage your direct reports to model the same. 8️⃣ "Audit where you've gone on AI autopilot"  ↳ Over-reliance creeps in quietly. ✅ Act on it: List 3 areas where you now "trust" AI outputs without checking. For each, design one review step that reintroduces human judgment. 9️⃣ "Upgrade your questions, not just your tools"  ↳ Tools are only as powerful as the questions behind them. ✅ Act on it: Replace "What should we do?" with "Given A, B, C constraints, what are 3 non-obvious options?" Evaluate question quality in team retros, not just answer quality. The question to keep asking: "Is AI helping me think better—or just faster?" Your leadership edge depends on the difference. Coaching can help; let's chat. ♻️ Repost it to your network and follow Joshua Miller for more tips on coaching, AI-era leadership, career + mindset. ⸻ #ai #leadership #executivecoaching #careeradvice #manager #mindset

If you're in tech, you're sitting on a goldmine right now. While everyone's debating AI job displacement, the engineering sector is quietly becoming the biggest AI beneficiary. The World Economic Forum projects 78 million net new jobs by 2030, and IT and Engineering is leading the charge. This shift is creating entirely new job categories that didn't exist two years ago. Here are five emerging growth areas for IT and Engineering: 1. AI-native product development → AI Product Managers who understand ML lifecycles and enterprise pain points. 2. AIOps infrastructure → MLOps engineers are moving companies from AI experiments to production. Every enterprise needs these skills. 3. AI cybersecurity → Red teamers for LLMs are literally paid to break AI systems.  4. Enterprise data infrastructure → Vector database engineers managing RAG pipelines are helping AI systems access the right information at the right time. 5. Vertical AI specializations → LegalTech AI specialists, FinTech AI analysts, HR tech AI specialists—domain expertise + AI fluency is the new superpower. The numbers back this up: $632 billion in AI spending (including applications, infrastructure, and IT services) by 2028. This will lead to new AI roles in engineering, product, data, and operations to maintain these AI systems. Bottom line: The engineers who adapt fastest will have the most opportunities. In my latest newsletter, I break down exactly how to transition into each of these roles, plus the specific tools and skills that matter most. What AI role are you most curious about? #AI #Engineering #IT #FutureOfWork

AI is moving fast. But here’s the uncomfortable truth: Most leaders aren’t evolving fast enough with it. I recently read Herminia Ibarra and Michael G. Jacobides’ work on the 5 Critical Skills Leaders Need in the Age of AI, and it felt like a mirror. Because these aren’t just “AI skills.” They’re leadership skills we should’ve been building all along. Here are the 5 that hit hardest: 1. 🌐 Span organizational boundaries → build networks across industries, functions, and disciplines to sense shifts coming from AI and beyond. 2. 🏗️ Redesign organizations → don’t just bolt technology on old systems; rethink structures, processes and value creation. 3. 🤝 Orchestrate team collaboration → integrate human + AI contributions in decision‑making while creating a culture of psychological safety. 4. 🎯 Coach and develop talent → guide team members through change, helping them build confidence to work in new, AI‑augmented ways. 5. 🧭 Lead by example → personally experiment with new tools, stay curious, and model the mindset of continuous learning. When I applied these in a recent cross-functional project, the tech was the easy part. The hard part? Helping people trust themselves in an AI-augmented workflow. But once they did, everything accelerated. Delivery. Collaboration. Innovation. All because the leadership shifted, not the tools. AI won’t replace leaders. But it will expose leaders who refuse to grow. 💬 Which of these 5 skills do you think your organization needs to strengthen the most right now and why? ♻ Share this with your network if it resonates. ☝ And follow Stuart Andrews for more insights like this.