Skills for the AI Workforce

In the evolving landscape of AI, one thing is clear: it's not about elimination ➡️ but augmentation. As AI technologies grow, human capital will play an even more pivotal role. Yet, a critical talent gap remains. According to Lenovo's Global Study of CIOs, 97% fear their organization's ambitions are at risk without talent equipped with the right AI-focused skills. This underscores the strategic importance of recruiting and retaining AI-skilled professionals for many organizations. The concept of 'Augmented Intelligence' is gaining traction. Here, AI is seen as a tool to enhance and amplify human capabilities, not replace them. This approach ensures people remain at the heart of innovation and decision-making, leveraging AI for superior results. Understanding this balance between automation and augmentation is crucial. Automation can handle repetitive tasks, freeing humans to focus on creative and complex problem-solving. Those who can work alongside AI or in roles requiring human ingenuity will become increasingly valuable. To close the talent gap, it's time to redefine how AI and human intelligence work together – united, they are a powerful force for the future. 🌟 #AI #AugmentedIntelligence #Innovation #TalentGap

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

If you’re building anything with LLMs, your system architecture matters more than your prompts. Most people stop at “call the model, get the output.” But LLM-native systems need workflows, blueprints that define how multiple LLM calls interact, how routing, evaluation, memory, tools, or chaining come into play. Here’s a breakdown of 6 core LLM workflows I see in production: 🧠 LLM Augmentation Classic RAG + tools setup. The model augments its own capabilities using: → Retrieval (e.g., from vector DBs) → Tool use (e.g., calculators, APIs) → Memory (short-term or long-term context) 🔗 Prompt Chaining Workflow Sequential reasoning across steps. Each output is validated (pass/fail) → passed to the next model. Great for multi-stage tasks like reasoning, summarizing, translating, and evaluating. 🛣 LLM Routing Workflow Input routed to different models (or prompts) based on the type of task. Example: classification → Q&A → summarization all handled by different call paths. 📊 LLM Parallelization Workflow (Aggregator) Run multiple models/tasks in parallel → aggregate the outputs. Useful for ensembling or sourcing multiple perspectives. 🎼 LLM Parallelization Workflow (Synthesizer) A more orchestrated version with a control layer. Think: multi-agent systems with a conductor + synthesizer to harmonize responses. 🧪 Evaluator–Optimizer Workflow The most underrated architecture. One LLM generates. Another evaluates (pass/fail + feedback). This loop continues until quality thresholds are met. If you’re an AI engineer, don’t just build for single-shot inference. Design workflows that scale, self-correct, and adapt. 📌 Save this visual for your next project architecture review. 〰️〰️〰️ Follow me (Aishwarya Srinivasan) for more AI insight and subscribe to my Substack to find more in-depth blogs and weekly updates in AI: https://lnkd.in/dpBNr6Jg

I have already talked about the potential for AI to create efficiencies and increase productivity in the workplace. But to realize and retain these benefits, business leaders need to ensure the talent they recruit understand how AI works and how to harness it. According to a recent Fortune article, 63% of hiring leaders say it's more challenging to source candidates with AI skills than applicants for other tech roles. To meet demand, organizations need to rethink their hiring strategy in three key ways: 1. Shift to skills-based hiring: it is likely that the jobs candidates are applying for did not exist when the candidates were in education. At Capgemini we’re removing traditional barriers to entry level jobs and considering non-traditional skills and experience. 2. Broaden the talent pipeline: community workforce programs, for example, offer diverse, under-tapped talent pools that bring fresh thinking and practical skills into the tech workforce. 3. Develop internal talent: invest in upskilling existing employees to fill AI-related roles. This is something we are already doing through our Capgemini University and its digital campuses focused on Gen AI, data, cloud, cybersecurity and more. Our commitment to lifelong learning is key — in 2024, our employees benefited from more than 25.7 million hours of learning, reflecting a culture of “learn-it-all” and continuous development As roles evolve, so must our approach to hiring and nurturing talent. By focusing on capability, adaptability, and continuous learning, we can build a workforce ready to harness AI’s full potential.

Is Using AI to Write LinkedIn Posts Right or Wrong? Let’s discuss.... Yesterday, I interviewed candidates for a role in my company. Part of the process involved a case study exercise. Imagine my surprise when 90% of the responses were eerily similar—some even word-for-word. It didn’t take long to realize they’d lifted content directly from ChatGPT, with no added thought, context, or originality. It got me thinking: in a world where AI tools like ChatGPT are so easily accessible, how do we maintain authenticity and originality, especially on platforms like LinkedIn where personal branding is everything? Let me tell you about a client I worked with recently. She wanted help building her thought leadership on LinkedIn. She’d been experimenting with AI to draft her posts. While the articles were technically sound, they didn’t carry her voice. There was no story, no spark, nothing that showed the uniqueness of her expertise. They read like well-polished reports, but they didn’t connect. And without connection, visibility and influence are hard to build. So, is it “wrong” to use AI? Absolutely not! But the key is how you use it. Here are some tips for Executives who want to use AI to create Thought Leadership content on LinkedIn that stands out while staying authentic: Overcome the Fear of the Blank Page: Many times the biggest hurdle is starting- AI can help. Use tools like ChatGPT to brainstorm ideas, draft outlines, or suggest titles. Think of it as your creative collaborator, not your replacement. Fact-Check Everything: A while ago I crosschecked some stats given by chat GPT and found some inaccuracies, especially in the interpretation. AI doesn’t always get it right. Always double check quotes, stats or industry-specific terms to ensure accuracy. Your expertise should always guide the narrative. Not the other way round Add Your Story: This is the special sauce- when you include your personal stories, anecdotes, experiences, insights, and voice onto the draft. A story only you can tell is what sets your thought leadership content apart. Refine for Your Voice: It can be tempting to let Chap GPT’s polished tone, takeover, but the magic is you. How do you want to sound? How do you want to show up? Do you want to be witty with a dash of professionalism? Tailor drafts so your voice and style runs through. While AI is a useful tool, it doesn't replace your years of experience and professional value- use it to refine your thoughts or drive creativity, but let your insights lead the way. Remember thought leadership is about sharing your unique perspective and connecting with others authentically. No AI tool can replace that. What do you think? Are you using AI for LinkedIn posts? How do you navigate authenticity in the age of AI? Let me know in the comments—I’d love to hear your thoughts! #Thoughtleadership #Executivevisibility #womeninleadership #AI

In a world racing for tech, automation, and AI, we often assume one thing, "If I learn the tool, I’ll win." But here's a truth I’ve seen across 4,000+ professionals I’ve coached: Yes, technical skills open doors Soft skills keep them open and widen them An article from PeopleMatters reveals some telling figures: 1. 92% of executives say human capabilities are as important as, or more important than, technical expertise. 2. Organisations are calling for stronger communication, emotional intelligence, and adaptability: the skills AI can’t easily replicate. Let me translate this in real-world terms: You might learn every new tool, every new language, every new platform. But when change hits fast, what will set you apart is how you connect, how you understand, how you lead when the map is blank. Here’s what I’ve seen as a professional working in this space: 1. Technical knowledge can get you into the room. But relationships keep you in the conversation. 2. Clarity of thought beats volume of slides. Listening deeply beats talking loudly. 3. Adaptability, empathy, and communication aren’t soft, they’re strategic. If you’re thinking, “I'll upgrade my tech stack first, and worry about soft skills later”, Here’s your wake-up call: This “later” might already be too late. 🔻Action for you today: 🔰Maybe it’s saying less and listening more 🔰Pick one human skill you’ll actively build this week 🔰Maybe it’s asking one better question in a meeting 🔰Maybe it’s giving feedback that doesn’t feel like critique Because when tools evolve, business models shift, and technology accelerates, One thing remains constant: People Still Lead People P.S. Which human skill do you believe will give you the strongest edge right now? Let’s discuss. #SoftSkillsMatter #PowerSkills #FutureOfWork #CommunicationSkills #VrindaSpeaks

10 skills I would learn immediately if I wanted to remain employed when AI significantly impacts my industry - these capabilities make you irreplaceable as automation accelerates. I'm observing AI systematically eliminate entire job categories. However, specific skills remain fundamentally untouchable because they require what AI cannot replicate: nuanced human judgment, sophisticated emotional intelligence, and strategic influence through relationships. The 10 skills that genuinely protect your career: - Strategic storytelling - Translating complex data into compelling narratives that change stakeholder minds and drive organizational action. AI generates analytical reports. Humans create strategic meaning and emotional resonance. - High-stakes negotiation - Reading conversational subtext, managing competing egos, finding workable compromise under significant pressure. Algorithms cannot navigate real-time power dynamics and unspoken interests. - Organizational political literacy - Understanding who actually holds decision-making influence, how choices really get made beyond org charts, and where unspoken veto power resides. - Trust-building at scale - Creating authentic professional relationships that generate career opportunities before they're publicly posted. AI cannot replicate genuine human connection and relationship capital. - Ethical judgment in ambiguous situations - Making consequential decisions when the "correct answer" depends on organizational context, cultural nuance, and potential consequences that AI cannot fully evaluate. - Crisis decision-making under uncertainty - Choosing strategic direction with incomplete information when delay costs more than imperfect action. - Cross-functional influence without formal authority - Achieving results through professionals you don't directly manage. Purely human interpersonal skill. - Pattern recognition across diverse industries - Identifying non-obvious connections between different sectors that create genuinely innovative solutions. - Facilitating high-conflict conversations - Navigating interpersonal conflict, mediating between competing organizational interests, de-escalating tension while preserving critical professional relationships. - Creative problem-solving within constraints - Developing novel solutions when standard methodologies fail and supporting data doesn't yet exist. Notice what's conspicuously absent from this list? Technical skills. Because those capabilities face automation first. The positions AI eliminates are roles that fundamentally followed documented procedures. The roles AI cannot replace require sophisticated judgment, strategic influence, and capability to navigate complex human dynamics. Sign up to my newsletter for more corporate insights and truths here: https://vist.ly/4bqdy #ai #futureofwork #careeradvice #careerstrategy #artificialintelligence #automation #executiverecruiter #eliterecruiter #jobmarket2025 #softskills #leadership

90% of LinkedIn creators get overwhelmed and quit within months. But, with AI, you don’t have to. I almost quit too. I used to spend hours every week second-guessing what to post… Burning my weekends to create carousel copy… Writing entire LinkedIn posts from scratch… Through trial and error, I developed a system that just works. I now use a single ChatGPT project—called ChatOS—to plan, write, and repurpose all my content *without* burning out. This isn’t about using AI to go viral. It’s about using it to stay consistent—especially when life gets busy. Inside this post, I’ll walk you through the 7 steps I use to automate my LinkedIn content system using AI. This is the same system that helped me: ✅ Post 3x a week without overthinking ✅ Grow to 20,000+ followers ✅ Build a quiet, meaningful side business—while working a full-time job Here’s the breakdown: 1. Create one ChatGPT Project (not a bunch of chats) 2. Set up detailed custom instructions in the project 3. Enable memory so ChatGPT actually learns you 4. Plan your content weekly with clean prompts 5. Repurpose using templates and past posts 6. 3-posts: how-to, personal, lead magnet 7. Review results with ChatGPT weekly That’s it - so simple! 📌 If you found this helpful, follow Justin Chia here on LinkedIn. I help busy solopreneurs grow their LinkedIn presence consistently using AI, so they can generate more leads for their business—without feeling overwhelmed. PS - I'll take any questions in the chat in the first 2 hours of posting! 😄

LLM applications are frustratingly difficult to test due to their probabilistic nature. However, testing is crucial for customer-facing applications to ensure the reliability of generated answers. So, how does one effectively test an LLM app? Enter Confident AI's DeepEval: a comprehensive open-source LLM evaluation framework with excellent developer experience. Key features of DeepEval: - Ease of use: Very similar to writing unit tests with pytest. - Comprehensive suite of metrics: 14+ research-backed metrics for relevancy, hallucination, etc., including label-less standard metrics, which can quantify your bot's performance even without labeled ground truth! All you need is input and output from the bot. See the list of metrics and required data in the image below! - Custom Metrics: Tailor your evaluation process by defining your custom metrics as your business requires. - Synthetic data generator: Create an evaluation dataset synthetically to bootstrap your tests My recommendations for LLM evaluation: - Use OpenAI GPT4 as the metric model as much as possible. - Test Dataset Generation: Use the DeepEval Synthesizer to generate a comprehensive set of realistic questions! Bulk Evaluation: If you are running multiple metrics on multiple questions, generate the responses once, store them in a pandas data frame, and calculate all the metrics in bulk with parallelization. - Quantify hallucination: I love the faithfulness metric, which indicates how much of the generated output is factually consistent with the context provided by the retriever in RAG! CI/CD: Run these tests automatically in your CI/CD pipeline to ensure every code change and prompt change doesn't break anything. - Guardrails: Some high-speed tests can be run on every API call in a post-processor before responding to the user. Leave the slower tests for CI/CD. 🌟 DeepEval GitHub: https://lnkd.in/g9VzqPqZ 🔗 DeepEval Bulk evaluation: https://lnkd.in/g8DQ9JAh Let me know in the comments if you have other ways to test LLM output systematically! Follow me for more tips on building successful ML and LLM products! Medium: https://lnkd.in/g2jAJn5 X: https://lnkd.in/g_JbKEkM #generativeai #llm #nlp #artificialintelligence #mlops #llmops

ChatGPT is the new Excel. Here’s your first step toward AI. Many companies are racing to adopt AI, but the biggest opportunity often goes unnoticed: empowering your team with AI tools. It’s not just about building new AI products; it’s about integrating AI into the daily workflow of your employees. The tools—like ChatGPT, Claude, and Perplexity—are available, but the knowledge gap is significant. While people experiment with these tools, few companies provide the right training to maximize their value. A well-trained workforce using AI effectively is a game changer. This skill set not only accelerates daily tasks but also builds the foundation for larger AI initiatives. Companies that fail to build this muscle now are not only leaving productivity gains on the table but also signaling to their most innovative employees that they’re not serious about AI. The wrong step? Banning AI tools like ChatGPT. The right step? Training employees on their effective use. Here’s what you need to be doing: 1 — Align on an AI Assistant (ChatGPT, Perplexity, Claude, etc.) Start by choosing one of the key AI assistants—whether it’s ChatGPT, Claude, or Perplexity—or a combination of them. The great news is that all of these now offer enterprise-grade plans that help you manage your teams efficiently. Plus, they come with major certifications like SOC 2, GDPR, CCPA, and CSA Star, ensuring compliance and security for your business. 2 — Make AI Part of Your Team’s Daily Toolkit Make it clear across your company: just as everyone uses a computer, email, PowerPoint, or Excel daily, AI assistants are going to be a prerequisite for everyday work. Part of becoming an AI-powered organization is ensuring these tools are integrated into everyone’s daily routine. 3 — Organize Structured Training Set up a comprehensive training program that teaches your employees how to work effectively with these tools. Focus on prompt engineering, real use cases, and practical examples. Just as important, provide clear guidelines on what not to do—such as entering sensitive IP or customer/employee information—to ensure proper usage and avoid risks. There’s a lot of FOMO out there, and many companies are rushing to figure out how to implement AI projects. But a prerequisite to all of this is having your workforce turbocharged and powered by AI assistants. Whether or not you end up building your own AI-powered features, this will help boost your team’s overall productivity. It will also build the familiarity and intuition your team will need for working with AI-powered services—or vendors who are leveraging this technology. All in all, it’s a win-win: a low-effort, low-cost, easy way to get started with AI adoption and transformation.