Artificial Intelligence Upskilling

“Get upskilled with AI” has become a ubiquitous phrase. I hear it frequently when speaking with PMs, Engineers, and PgMs who are trying to shape their careers in an “AI-focused” world. It is often the default advice for anyone looking to stay relevant. However, when I dig deeper into what this upskilling actually looks like, a pattern emerges: most people are consuming the same content and developing very similar skills. Does this mean we are witnessing a consolidation of roles and skills? Not quite. What it really reflects is a lack of clarity around what skills will actually be required for different AI-centric roles. The majority of available training and content is designed to make people effective AI consumers, not AI strategists or AI builders. Learning about AI is unquestionably important. But “learning AI” is analogous to “learning engineering.” The question is always: what kind of engineering, and for what purpose? Computer engineering and mechanical engineering share foundations, but their scopes, tools, and career paths are fundamentally different. AI is no different. You can never be an “AI expert” in the abstract. Even a few years ago, there were clear distinctions between NLP, computer vision, and reinforcement learning as each with its own depth and rigor. The terminology may have evolved, but the underlying reality has not. The starting point, therefore, should be role clarity. Decide where you want to go and which role you are targeting: Product Management, Forward Deployed Engineer (FDE), Software Engineer, or AI Strategy (Consultant/Strategist). There are others, but these are among the most common. Once you made that decision, you can deliberately build the right muscles across the dimensions that role truly demands. For PMs and consultants in particular, understanding what to build and why matters far more than how to implement it. Don’t confuse using AI with building AI skills. #ExperienceFromTheField #WrittenByHuman

A 12-week AI upskilling roadmap helped Amazon fill 40% of job openings internally (but most companies ignore it). Everyone panics about the AI skills crisis. The World Economic Forum data tells a different story. Skills obsolescence dropped from 57% during the pandemic to 39% projected for 2025 to 2030. The crisis is no longer accelerating. It is becoming solvable. Amazon demonstrated what a structured approach can achieve through a $1.2 billion upskilling system that delivered measurable outcomes: → 700,000 employees retrained → Apprenticeship graduates earn $21,500 more annually → 40% of internal job openings filled by reskilled employees The models that deliver results are surprisingly simple. 📌 Weeks 1 to 4: Build AI literacy. Focus on prompt engineering basics, master three to five role-specific AI tools, and complete one micro-certification. 📌 Weeks 5 to 8: Apply skills at work. Automate two to three tasks, track time saved, document quality improvements, and share learnings. 📌 Weeks 9 to 12: Build proof. Create one portfolio project, quantify impact, and position yourself for AI-adjacent roles. Technical skills now last 12 to 18 months, while digital skills decay in three. A six-month delay reduces your adaptation window by one-third. This is why the workforce is splitting. Around 48% get redeployed or upskilled. Another 11% are left behind despite employer commitments. Start now! Audit yourself against the top WEF skills such as AI, big data, cybersecurity, critical thinking, and adaptability. A score of zero to three indicates high risk. Select one high-value skill for the next 90 days and choose certifications with proven wage-premium outcomes. What is the one skill you are committed to building?

I think AI upskilling is failing in many organizations because we are treating it like a training initiative instead of a fundamental shift in how work gets done. In my view, the mistake starts when leaders frame the problem as “our people need to learn AI tools.” That framing already limits the outcome. Tools are the easy part. What is hard, and mostly ignored, is helping people understand how AI changes decision-making, ownership, and accountability in their day-to-day work. Without that shift, no amount of courses or certifications will move the needle. I also believe we overestimate the value of generic AI literacy programs. Teaching everyone the same concepts, at the same depth, assumes that AI adds value in the same way across roles. It does not. A manager, a product leader, an analyst, and an operations professional each need very different instincts when working with AI. When training is disconnected from real workflows and real decisions, it becomes theoretical very quickly. Another issue I see is that organizations focus too much on explaining the technology and too little on rethinking the work itself. People are taught how models behave, but not how to question a process that existed before AI was available. The real opportunity is not in optimizing old workflows with AI, but in asking whether those workflows should exist at all in their current form. From what I have observed, the organizations that make progress treat AI upskilling as an ongoing capability-building effort. Learning happens through practice, experimentation, and reflection, not through one-off sessions. Just as importantly, they create psychological safety so people can experiment without feeling that every misstep puts their role at risk. I do not think AI upskilling works unless leadership is visibly involved. When executives treat AI learning as something to be delegated, adoption stays shallow. When leaders actively learn and adapt their own ways of working, it signals that this is about evolving the organization, not simply automating it. For me, AI upskilling is ultimately about judgment. Helping people know when to rely on AI, when to challenge it, and how to take responsibility for outcomes in an AI-augmented world. Until we design upskilling around that reality, most efforts will continue to look busy without being effective. I write about #artificialintelligence | #technology | #startups | #mentoring | #leadership | #financialindependence   PS: All views are personal

The "Prompt Whisperer" vs. The "AI Philosopher": Which One Are We Training? When I design AI upskilling programs for governments and universities, I see the same pattern: everyone wants to start with prompting. It's immediate, visible, and feels like progress. But recent research (Generative AI Literacy: Twelve Defining Competencies, EPFL/Idiap, 2024) challenges this instinct. AI literacy isn't just about what you can get the machine to produce. It's about how you think, decide, and act alongside it. The paper offers something rare: a compass for navigating AI-powered work. Twelve competencies spanning technical fluency, ethical judgment, and critical evaluation. Not a command list, but a framework for decision-making. Here's the shift that matters: ➡️ It's not just drafting an email. It's evaluating whether the tone and content reflect authentic voice. ➡️ It's not just generating an image. It's questioning the biases embedded in its training data. ➡️ It's not just using a tool. It's understanding the governance and societal implications of its deployment. In practice, this means effective AI literacy programs need both: ⚙️ A toolkit to build confidence through hands-on skills 🧭 A compass to ground decisions in ethics, context, and judgment Without both, adoption becomes either shallow or unsafe. The future belongs to professionals who master this duality: fast enough to use the tools, wise enough to guide their impact. 👉 Here's my question for you: If you're building AI capabilities in your team today, where would you start: prompt fluency or critical evaluation skills? And what's driving that choice? Link to paper: https://lnkd.in/dnETJgcb #AILiteracy #FutureOfWork #GenerativeAI #Upskilling #AITransformation #AIEthics

It's only January, and the job market is brutal.   At the end of the day, the only thing we can control is our own actions. From what I'm seeing in job postings and hiring conversations, 2026 needs to be the year you upskill in AI. The reason is simple: AI isn't a "nice-to-have" anymore. Employers are starting to treat AI fluency as a baseline requirement, and the pace of change isn't slowing down.   The World Economic Forum reports that employers expect 39% of workers' core skills to change by 2030. LinkedIn reports that job listings mentioning AI literacy as a skill rose more than six times year over year.   This isn't about becoming an engineer overnight. It's about learning how to: - Turn a messy problem into a clear prompt and something you can actually use - Verify results instead of blindly trusting them - Use AI to draft, summarize, analyze, and automate the repetitive stuff - Understand the boundaries: privacy, security, bias, copyright, and when to disclose you used it   Here's a practical approach that actually sticks: - Pick one weekly task (emails, proposals, research, reporting, curriculum, SOPs). - Build a simple AI workflow for getting drafts and structure in place. - Add a verification step (check sources, cross-check facts, do a second review). - Save your best prompts and templates so you can reuse them. - Repeat with one new task each month.   If you're hiring: look for candidates who can show you disciplined AI usage and good judgment, not just "AI enthusiasm."   If you're job searching: think of AI as a way to multiply your output and prove you can adapt. #AIUpskilling #AI #GenerativeAI #FutureOfWork #WorkforceDevelopment #Upskilling #Reskilling #CareerDevelopment #JobSearch #Hiring #TalentDevelopment #DigitalTransformation Sources: - World Economic Forum, Future of Jobs Report 2025 (skills outlook, 39% core skills shift by 2030) - https://lnkd.in/eU4Kmcag   - NIST, AI Risk Management Framework (AI RMF 1.0) (trustworthy AI characteristics and risk management) - https://lnkd.in/e6JbWYXP - Indeed Hiring Lab, AI at Work Report 2025 (analysis of skill transformation and job postings) - https://lnkd.in/enrY4i_B - Harvard Business School D^3 Institute, “Future Proof with AI” (workforce upskilling program) - https://lnkd.in/e5VkhZvk - Amazon CEO Andy Jassy on generative AI (training and adoption expectations) - https://lnkd.in/eyK5YBS5 - Shopify CEO memo on AI as a baseline expectation (widely reported) - https://lnkd.in/eNtQgatn  

AI-READY UPSKILLING: I spent three weekends (and 15 years) building a framework to help product designers figure out where they stand with AI. It's called E-P-I-A-S × SAE. And before you ask, yes, I know it'll need updates before the ink is dry. The name? Yeah. It ain't perfect. But something is better than nothing right now. The idea is simple: there are two ways to grow with AI tools. - DEEPER - WIDER Let me quickly clarify. DEEPER = (Explorer → Practitioner → Integrator → Architect → Steward), or E-P-I-A-S as an acronym that really rolls off the tongue ;-) WIDER = (Manual → AI-Assisted → Partially Automated → Guided Automation → Mostly Automated), or basically the Society of Automotive Engineers' self-driving framework you may have heard about with all the "auto pilot" talk these years Most designers are somewhere between SAE L1 and L2, trying to figure out what "good" looks like. And within that level of AI sensibilities, they're in one of the E-P-I-A-S stages. This framework gives you a map for where you might be, and where you might want to go next. Here's the thing everyone misses: an S-Steward at L1 (someone who's built org standards for ChatGPT) is more valuable than an E-Explorer at L4 (someone fumbling with advanced toolchains). If you're a fan of the T-shaped approach to design, then you won't have a problem figuring out whether you want to find more breadth, or else more focus. That said, right now depth of judgment beats breadth of tooling. For sure. If you're a designer trying to level up your AI skills (or a design leader trying to upskill your team) this approach should be useful to you. It's open-sourced over here https://lnkd.in/gUGyGY6P as a markdown file in case you want to ingest it with your agent. Full mobile-legible framework in the article below 👇. Part of the #DesignInTech Report 2026, dropping at SXSW. Feedback welcome. —JM

25% 𝐨𝐟 𝐆𝐨𝐨𝐠𝐥𝐞 𝐂𝐨𝐝𝐞 𝐢𝐬 𝐍𝐨𝐰 𝐖𝐫𝐢𝐭𝐭𝐞𝐧 𝐛𝐲 𝐀𝐈 – 𝐖𝐡𝐚𝐭 𝐃𝐨𝐞𝐬 𝐓𝐡𝐢𝐬 𝐌𝐞𝐚𝐧 𝐟𝐨𝐫 𝐃𝐞𝐯𝐞𝐥𝐨𝐩𝐞𝐫𝐬? With AI now generating over a quarter of Google’s code, we’re witnessing a pivotal shift in the tech industry. AI isn’t here to replace developers but to transform how we work. 𝑻𝒉𝒆 𝒒𝒖𝒆𝒔𝒕𝒊𝒐𝒏 𝒊𝒔: 𝑨𝒓𝒆 𝒚𝒐𝒖 𝒓𝒆𝒂𝒅𝒚 𝒕𝒐 𝒂𝒅𝒂𝒑𝒕 𝒂𝒏𝒅 𝒔𝒕𝒂𝒚 𝒂𝒉𝒆𝒂𝒅? Here are some steps to make yourself “AI-proof” and leverage Generative AI in your career: 1. 𝐔𝐩𝐬𝐤𝐢𝐥𝐥 𝐢𝐧 𝐆𝐞𝐧𝐞𝐫𝐚𝐭𝐢𝐯𝐞 𝐀𝐈: Familiarize yourself with tools and platforms like ChatGPT, GitHub Copilot, and Google’s Notebook LM. Learning how AI generates and optimizes code is essential for today’s developers. 2. 𝐄𝐦𝐛𝐫𝐚𝐜𝐞 𝐂𝐨𝐧𝐭𝐢𝐧𝐮𝐨𝐮𝐬 𝐋𝐞𝐚𝐫𝐧𝐢𝐧𝐠: Technology evolves fast, and so should your skills. Courses in machine learning, prompt engineering, and AI ethics will give you an edge in a landscape where AI is integrated with everyday tasks. 3. 𝐅𝐨𝐜𝐮𝐬 𝐨𝐧 𝐇𝐢𝐠𝐡𝐞𝐫-𝐋𝐞𝐯𝐞𝐥 𝐒𝐤𝐢𝐥𝐥𝐬: As AI takes over repetitive coding tasks, prioritize problem-solving, system architecture, and creative thinking. These are the skills that will remain invaluable, regardless of automation. 4. 𝐄𝐧𝐡𝐚𝐧𝐜𝐞 𝐂𝐨𝐥𝐥𝐚𝐛𝐨𝐫𝐚𝐭𝐢𝐨𝐧 𝐒𝐤𝐢𝐥𝐥𝐬: AI will increasingly serve as a “co-developer,” so being able to review, debug, and refine AI-generated code collaboratively will be critical. 5. 𝐒𝐭𝐚𝐲 𝐂𝐮𝐫𝐢𝐨𝐮𝐬 𝐚𝐧𝐝 𝐎𝐩𝐞𝐧-𝐌𝐢𝐧𝐝𝐞𝐝: This is a dynamic and transformative period in tech. Embracing AI with curiosity rather than resistance will allow you to thrive. AI is here to enhance our capabilities, not replace them. By focusing on skills that AI can’t easily replicate, we can secure our relevance in the ever-evolving tech landscape. #AI #GenerativeAI #Upskilling #FutureOfWork #TechInnovation #DeveloperSkills #LinkedInLearning

84% of Indian professionals feel “unprepared” for AI. It’s not for lack of trying. It’s what they’re trying to learn. Applications have doubled. Recruiters still can’t find talent. More people applying. Fewer people worth hiring. The gap isn’t about learning one more AI tool. It isn’t about getting another certification. The gap is this: Most professionals are upskilling for the TOOL. Almost nobody is upskilling for the THINKING. I see it in every cohort I run. Someone walks in saying “teach me ChatGPT.” I ask them: “What’s the one problem your client keeps complaining about?” Blank stare. That blank stare is the skills gap. 72% want to switch jobs but don’t know what skills matter anymore. Here’s what matters: the ability to ask a better question than the machine can generate. Curiosity. Critical thinking. Communication. Not another prompt engineering course. The professionals who’ll thrive aren’t the ones who learned the latest tool. They’re the ones who understood their customer’s problem so deeply that any tool becomes useful. Are you upskilling for the tool — or for the thinking? #CyborgMindset #AIAdoption #FutureOfWork #B2BFounders

AI is here. Markets are evolving. Job roles are transforming faster than ever. By 2030, 70% of the skills we use today will change. So, how do you stay ahead? By upskilling—strategically. LinkedIn News India’s ‘Skills on the Rise 2025’ highlights the 15 fastest-growing skills—from AI Literacy to Strategic Thinking—that are defining the future of work. But knowing what’s in demand is just the first step. The real question is: How do you actually upskill? Here’s how to do it the smart way: ✅ Pick skills that align with your career goals. Don’t chase trends blindly. Choose skills that enhance your strengths and open new opportunities in your field. ✅ Learn by doing. Watching courses is great, but real learning happens when you apply new skills in projects, case studies, and problem-solving scenarios. ✅ Make AI your co-pilot, not your competition. AI won’t replace you—but someone who knows how to use it might. Start with AI literacy, Prompt Engineering, and LLMs to stay relevant. ✅ Sharpen your human skills. Creativity, Strategic Thinking, and Communication will always be in demand—because machines can’t replicate them. ✅ Network and engage. Surround yourself with people who are already excelling in these skills. Join discussions, share insights, and build connections. The future belongs to those who adapt. What skill are you focusing on this year? #LinkedInInsiderIndia #LinkedInNewsIndia

Reflecting on my post of yesterday and the comments of Patrick Senti. Yesterday I mentioned there is more to upskilling than prompt engineering. Today, I am taking this a step further. 🛣 The journey towards true AI literacy requires a learning journey. Prompt engineering is part of this, but as already mentioned yesterday, topics like the impact of AI on individual roles, the variety of tools available and how to evaluate them, making informed decisions based on data and probabilities, responsible usage of AI tools whether developed internally or externally, and recognizing tasks for AI-driven automation. This is what users need to know. Now do you require a "standard" learning journey for everyone? Or do you need tailored sessions for those in Finance, Marketing, Sales, and so on? This to me all depends on your ambition level with regards to AI overall. If you go big on the topic - you may want to tailor your upskilling not only for users but users in a specific area. Ideally, you connect this to your use case roadmap. 💡 Imagine that you know what use cases will be implemented in the next 2-3 years - and you ensure the users affected will get the exact right skills, knowledge but also mindset toward the topic of AI - it will massively increase chances of adoption and value generated from your AI initiatives. As Patrick Senti rightfully pointed out, this exercise can also be done for managers, and developers, and about the topic of governance. And we can even take this a step further and consider executives, product owners, and so on. 🚀 Now the key to a successful tailored upskilling strategy for AI is a solid well well-thought-through vision, ambition level, and strategy for AI, including a prioritized roadmap of use cases. Otherwise, you might as well just use a standard learning journey. #neverstoplearning #upskillingstrategy #ailiteracy