Future Job Role Analytics

The most important skills today and in the next years will be human capabilities: critical and analytic thinking, resilience, leadership and influence, overlaid with technological literacy and AI skills to amplify these human capacities. World Economic Forum's new Future of Jobs Report provides a deep and broad analysis of the drivers of labour market transformation, the outlook for jobs and skills, and workforce strategies across industries and nations. It's a really worthwhile deep dive if you're interested in the topic (link in comments). Here are some of the highlights from the Skills section, which to my mind is at the heart of it. 🧠 Analytical Thinking Leads Core Skills. Skills like analytical thinking (70%), resilience (66%), and creative thinking (64%) top the list of core abilities for 2025. By 2030, the emphasis shifts even more towards AI and big data proficiency (85%), technological literacy (76%), and curiosity-driven lifelong learning (79%). This shift underscores the critical role of technology and adaptability in future workplaces. 📉 Skill Stability Declines but at a Slower Rate. Employers predict that 39% of workers' core skills will change by 2030, slightly lower than 44% in 2023. This reflects a stabilization in the pace of skill disruption due to increased emphasis on upskilling and reskilling programs. Half of the workforce now engages in training as part of long-term learning strategies compared to 41% in 2023, showcasing the growing adaptation to technological changes . 🌍 Economic Disparities in Skill Disruption. Middle-income economies anticipate higher skill disruption compared to high-income ones. This disparity highlights the uneven challenges of transitioning labor forces across global regions, particularly in economies still grappling with structural changes. 🚀 Tech-Savvy Skills in High Demand. The adoption of frontier technologies, including generative AI and machine learning, is increasing the demand for skills like big data analysis, cybersecurity, and technological literacy. These trends indicate that businesses are aligning workforce strategies to integrate these advancements effectively. 📚 Upskilling Is the Norm, Not the Exception. By 2030, 73% of organizations aim to prioritize workforce upskilling as a response to ongoing disruptions. This reflects a shift in corporate investment priorities towards human capital enhancement to maintain competitiveness.

After spending three decades in the aerospace industry, I’ve seen firsthand how crucial it is for different sectors to learn from each other. We no longer can afford to stay stuck in our own bubbles. Take the aerospace industry, for example. They’ve been looking at how car manufacturers automate their factories to improve their own processes. And those racing teams? Their ability to prototype quickly and develop at a breakneck pace is something we can all learn from to speed up our product development. It’s all about breaking down those silos and embracing new ideas from wherever we can find them. When I was leading the Scorpion Jet program, our rapid development – less than two years to develop a new aircraft – caught the attention of a company known for razors and electric shavers. They reached out to us, intrigued by our ability to iterate so quickly, telling me "you developed a new jet faster than we can develop new razors..." They wanted to learn how we managed to streamline our processes. It was quite an unexpected and fascinating experience that underscored the value of looking beyond one’s own industry can lead to significant improvements and efficiencies, even in fields as seemingly unrelated as aerospace and consumer electronics. In today’s fast-paced world, it’s more important than ever for industries to break out of their silos and look to other sectors for fresh ideas and processes. This kind of cross-industry learning not only fosters innovation but also helps stay competitive in a rapidly changing market. For instance, the aerospace industry has been taking cues from car manufacturers to improve factory automation. And the automotive companies are adopting aerospace processes for systems engineering. Meanwhile, both sectors are picking up tips from tech giants like Apple and Google to boost their electronics and software development. And at Siemens, we partner with racing teams. Why? Because their knack for rapid prototyping and fast-paced development is something we can all learn from to speed up our product development cycles. This cross-pollination of ideas is crucial as industries evolve and integrate more advanced technologies. By exploring best practices from other industries, companies can find innovative new ways to improve their processes and products. After all, how can someone think outside the box, if they are only looking in the box? If you are interested in learning more, I suggest checking out this article by my colleagues Todd Tuthill and Nand Kochhar where they take a closer look at how cross-industry learning are key to developing advanced air mobility solutions. https://lnkd.in/dK3U6pJf

Imagine you're the CFO of a global company and someone pitches you a recruitment automation solution that will do the work of 400 recruiters and save you $30M per year. What would you do? When I was at LinkedIn's Talent Connect in October, I attended a workshop with John Vlastelica in which he shared that a global company had decided to implement a recruiting automation solution that would allow them to save $30M in costs by eliminating 400 recruiter positions. They also reduced the time to hire from 11 days down to 3. He shared that another company had used recruitment automation software to hire 300,000 workers with minimal human involvement - people only came into the process after background checks had been performed. They also maintained candidate quality and candidate experience while increasing the speed of hire. These kinds of case studies should not surprise anyone, although it is sobering to anyone in talent acquisition - the rapid advancement of AI and automation in recruiting is both exciting and concerning. On the one hand, the potential for efficiency gains, cost savings, and improved candidate experience is huge and undeniable, as these examples demonstrate. On the other hand, we must also be mindful of the human impact - thousands of recruiters are seeing their roles transformed or eliminated. As talent acquisition professionals, it's important to be thinking about how to adapt and provide value in this changing landscape. Some key questions to consider: -How can we upskill and position ourselves to work alongside AI rather than be replaced by it? -What are the uniquely human elements of recruiting that AI can't replicate, and how do we double down on those? -How might our roles evolve to focus more on passive talent sourcing, talent intelligence/advisory, strategic workforce planning, employer branding, candidate engagement, and employee experience? For companies considering or implementing recruitment automation, I believe it should be a thoughtful, strategic decision - not just a blind cost-cutting measure. Here are some key considerations: -What is the optimal mix of human and automated touchpoints to balance efficiency and candidate experience? -How will the balance of AI and human involvement vary based on the labor market dynamics for each role? Roles with talent scarcity may require more human touch to attract and engage candidates, while high-volume roles with ample supply lend themselves to greater automation. -How will we redeploy or reskill displaced recruiters? -How do we maintain our employer brand and human touch with increased automation? The future of recruiting is undoubtedly both human and machine - but the mix is up to each company and may vary by role/department. I'm curious to hear your thoughts - have you been impacted by AI/automation? How are you and/or your company preparing for the intersection of AI/automation and recruiting? #AI #Recruiting #FutureOfWork

New evidence says discourse on how AI will reshape work is getting it wrong. It’s not that some jobs get automated away while others are augmented. Automation and augmentation are playing out in the same roles at the same time. In other words, AI is reshaping work within jobs rather than eliminating them. The “winners vs. losers” frame doesn’t hold. Our latest research at The Burning Glass Institute mines millions of job postings before and after the advent of LLM’s to track how AI is already reshaping skill demand. The finding is striking: we found a 0.87 correlation between the roles experiencing the greatest automation effects and those experiencing the greatest augmentation effects, meaning the jobs most vulnerable to automation are also those most empowered by AI. Tasks are disappearing and intensifying simultaneously—within the same roles, at the same time. In fact, we find that skills most exposed to AI automation were 16% more likely to see demand decline than baseline skills. Skills most exposed to AI augmentation were 7% more likely to see demand increase.   Project managers aren’t disappearing, but our analysis shows that spreadsheet-heavy tasks are fading while strategic, judgment-intensive work is growing. Financial analysts aren’t getting replaced, but model-building is automated while interpretation and decision-making matter more. The unit of change isn’t the job. It’s the task mix inside the job. Our paper, "Beyond the Binary", offers some of the first empirical evidence from the AI Tracking Hub, a multistakeholder initiative led by the Burning Glass Institute to move the AI–work conversation from forecasts to observation. If jobs aren’t vanishing but transforming from within, the real question isn’t “Which jobs are safe?” It’s whether our institutions—education, training, workforce policy—are built for continuous change rather than one-time transitions. You can find the report on https://lnkd.in/ej5FJu2J. I so enjoyed the collaboration with coauthors Benjamin Francis, Shrinidhi Rao, and Gwynn Guilford, and I am grateful as always to Gad Levanon and Stuart Andreason for their work to bring data-driven, empirical understanding to the workforce impacts of AI. #AI #artificialintelligence #jobs #economics #work.

I am often asked how I transitioned from IT services into manufacturing. Some are also aware that this sectoral shift was preceded by my evolution across various roles and are curious how I handled it. My answer has invariably been that it was a mix of mindful choices and opportunities utilised, where every step felt organic and complementary. Each step added a new layer to the professional I am today. In IT services, I learnt speed, agility, and technical breadth; was trained to think fast, deliver faster, and solve complex problems by working together with other SMEs. It’s a world that taught me to be future ready, think on my toes, be customer-oriented, and deeply aware of delivery excellence. Moving into the industry changed the lens, since providing solutions and bringing change are very different asks. Where IT services promoted rapid innovation, manufacturing taught me to focus on adoption. Where services emphasized deadlines and delivery, industry stressed business alignment, ROI, and business value. In services, the deadline was always yesterday; in industry, the goal is to make every decision count — not for IT, but for the business as a whole. Leading digital transformation initiatives helped me shift from the breadth of technology to the depth of implementation — from “what can we do?” to “what truly moves the needle?” I realised the responsibility was from "strategy to change"; it was about enabling outcomes, shaping mindsets, and transforming operations at scale. As for my role transitions, everything added something unique to my toolkit: ☑️As a developer, I understood code and best practices. ☑️As a business analyst, I learnt the art of requirements elicitation . ☑️As a product manager, I understood strategy and how to balance priorities. ☑️As a consultant, I learned how to shape and sell solutions. ☑️As a digital transformation specialist, I became better at systems thinking and change enablement. ☑️And now, apart from all the above, I know the importance of asking the hard questions — Why aren’t the solutions accepted? Are we solving for the right problems? Every new role led to a mindset shift. Every transition was an opportunity to unlearn and learn. And I am still learning everyday! What lessons have shaped your career transitions? #lifelessons #newyearthoughts

𝗧𝗵𝗲 𝗙𝗼𝗿𝗴𝗼𝘁𝘁𝗲𝗻 𝗥𝗼𝗹𝗲 𝗼𝗳 𝗘𝗻𝘁𝗿𝘆-𝗟𝗲𝘃𝗲𝗹 𝗝𝗼𝗯𝘀 𝗶𝗻 𝗮𝗻 𝗔𝗜 𝗘𝗰𝗼𝗻𝗼𝗺𝘆 AI promises massive productivity gains. But it may also be quietly eroding how expertise is built. As AI enables senior employees to do more on their own, many entry-level roles—the primary source of learning by doing—are disappearing. This matters because the most valuable workplace skills are often 𝘁𝗮𝗰𝗶𝘁: absorbed through experience, not taught in classrooms or manuals. According to a recent study, today’s rush to automate early-career work may be socially excessive. While automation boosts short-term productivity, it also disrupts the intergenerational transfer of tacit knowledge. The result is a trade-off: higher output now, but weaker skills in the next generation—ultimately slowing long-term economic growth and, in some cases, reducing overall welfare. The implications are not trivial. Even modest levels of AI-driven automation at the entry level could lower long-run U.S. per-capita growth by an estimated 𝟬.𝟬𝟱 𝘁𝗼 𝟬.𝟯𝟱 𝗽𝗲𝗿𝗰𝗲𝗻𝘁𝗮𝗴𝗲 𝗽𝗼𝗶𝗻𝘁𝘀 𝗮𝗻𝗻𝘂𝗮𝗹𝗹𝘆. Over time, that compounds into a meaningful economic drag. AI co-pilots offer a partial remedy. They can help workers who missed early learning opportunities catch up later in their careers. But they also introduce a new tension: if AI makes skill gaps easier to mask, it may reduce incentives for juniors to develop those skills in the first place. 𝗧𝗵𝗲 𝗺𝗲𝘀𝘀𝗮𝗴𝗲 𝗶𝘀 𝗹𝗼𝘂𝗱 𝗮𝗻𝗱 𝗰𝗹𝗲𝗮𝗿: 𝗔𝗜 𝗰𝗵𝗮𝗻𝗴𝗲𝘀 𝗻𝗼𝘁 𝗷𝘂𝘀𝘁 𝗵𝗼𝘄 𝘄𝗼𝗿𝗸 𝗶𝘀 𝗱𝗼𝗻𝗲, 𝗯𝘂𝘁 𝗵𝗼𝘄 𝗲𝘅𝗽𝗲𝗿𝘁𝗶𝘀𝗲 𝗶𝘀 𝗳𝗼𝗿𝗺𝗲𝗱. 𝗔𝗻𝗱 𝗴𝗿𝗼𝘄𝘁𝗵 𝗱𝗲𝗽𝗲𝗻𝗱𝘀 𝗼𝗻 𝗯𝗼𝘁𝗵. To capture AI’s full potential, policy, firms, and universities must protect and expand early-career learning—through mentorships, apprenticeships, practical education, and AI systems that complement junior roles rather than erase them. 𝗣𝗿𝗼𝗱𝘂𝗰𝘁𝗶𝘃𝗶𝘁𝘆 𝘄𝗶𝘁𝗵𝗼𝘂𝘁 𝘀𝗸𝗶𝗹𝗹 𝗳𝗼𝗿𝗺𝗮𝘁𝗶𝗼𝗻 𝗶𝘀 𝗻𝗼𝘁 𝗽𝗿𝗼𝗴𝗿𝗲𝘀𝘀—𝗶𝘁’𝘀 𝗯𝗼𝗿𝗿𝗼𝘄𝗲𝗱 𝗴𝗿𝗼𝘄𝘁𝗵. 𝗥𝗲𝗳𝗲𝗿𝗲𝗻𝗰𝗲: Ide, Enrique. (2025). Automation, AI, and the Intergenerational Transmission of Knowledge. 10.48550/arXiv.2507.16078.

Across America, the warning signs are no longer whispers. Automation and AI are poised to wipe out 30 to 50 percent of job roles in the coming years. But the truth is, anyone can become a movement leader for the age of AI. No title or permission required. Here’s how: Step One: Rewire Your Role AI isn’t just for engineers. It’s in your browser, your inbox, your workflow. Learn it, use it, teach it. Automate part of your job and coach others to do the same. You don’t need a boss’s blessing to be the one who spreads smart tools. Step Two: Reinvent It Entirely Once you’ve chipped away at your task list, zoom out. Ask yourself: If I were designing this job from scratch, with AI as my teammate, what would it look like? Step Three: Don’t Go It Alone The biggest gains come when innovators reimagine together. Pull in a few peers you admire for their curiosity from neighboring teams. Show them your role and work redesign. Invite them to co-create the next bigger version of how all your work together could be reimagined. Commit to having each other’s backs! This is what radical adaptability looks like. It’s how people future-proof not just their jobs, but their professional brands and career value. Companies are desperate for internal changemakers, don't make it difficult for the leadership in your organization to find you and your curious proactive peers already rebuilding the future of work from the inside.

For the longest time we've had two main options to help people perform: upskilling or performance support. Just-in-case vs just-in-time. Push vs pull. With AI, we now have a third - enablement. It's different from what we've had before: 𝐔𝐩𝐬𝐤𝐢𝐥𝐥𝐢𝐧𝐠 ("teach me") - commonly done through hands-on learning with feedback and reflection, such as scenario simulations, in-person role-plays, facilitated discussions, building and problem-solving. None of that has become less relevant, but AI has enabled scale through AI-enabled role-plays, coaching, and other avenues for personalised feedback. 𝐏𝐞𝐫𝐟𝐨𝐫𝐦𝐚𝐧𝐜𝐞 𝐬𝐮𝐩𝐩𝐨𝐫𝐭 ("help me") - support in the flow of work, previously often in the format of short how-to resources located in convenient places. AI has elevated that in at least two ways: through knowledge management, which helps retrieve the necessary, contextualised information in the workflow; and general & specialised copilots that enhance the speed and, arguably, the expertise of the employee. Yet, 𝐞𝐧𝐚𝐛𝐥𝐞𝐦𝐞𝐧𝐭 (‘do it for me’) is different – it takes the task off your plate entirely. We’ve seen hints of it with automations, but the text and analysis capabilities of genAI mean that increasingly 'skilled' tasks are now up for grabs. Case in point: where written communication was once a skill to be learned, email and report writing are now increasingly being handed off to AI. No skill required (for better or worse) – AI does it for you. But here's a plot twist: a lot of that enablement happens outside of L&D tech. It may happen in sales or design software, or even your general-purpose enterprise AI. All of which points to a bigger shift: roles, tasks, and ways of working are changing – and L&D must tune into how work is being reimagined to adapt alongside it. Nodes #GenAI #Learning #Talent #FutureOfWork #AIAdoption

𝗧𝗵𝗲 𝗹𝗶𝘀𝘁 𝗶𝘀𝗻��𝘁 𝗮 𝗽𝗿𝗲𝗱𝗶𝗰𝘁𝗶𝗼𝗻. 𝗜𝘁’𝘀 𝗮 𝘄𝗮𝗿𝗻𝗶𝗻𝗴. A warning about which careers depend most on patterns, repetition, and information flow, the exact things AI is getting better at every month. Microsoft’s study highlights the first 40 roles with the highest AI applicability, and the results are… revealing. Not just writers and analysts, but customer reps, educators, clerks, and even data scientists appear on the list. Here’s what the carousel shows, and what it really means: 𝟭. 𝗖𝗼𝗻𝘁𝗲𝗻𝘁 & 𝗖𝗼𝗺𝗺𝘂𝗻𝗶𝗰𝗮𝘁𝗶𝗼𝗻 𝗥𝗼𝗹𝗲𝘀 𝗟𝗲𝗮𝗱 𝘁𝗵𝗲 𝗥𝗶𝘀𝗸 𝗭𝗼𝗻𝗲 Writers, editors, journalists, announcers - any job centered on turning information into words now competes directly with LLM-level efficiency. 𝟮. 𝗞𝗻𝗼𝘄𝗹𝗲𝗱𝗴𝗲 𝗪𝗼𝗿𝗸𝗲𝗿𝘀 𝗔𝗿𝗲𝗻’𝘁 𝗘𝘅𝗲𝗺𝗽𝘁 Mathematicians, statisticians, analysts, historians… These roles rely on structured reasoning, which models now mimic shockingly well. 𝟯. 𝗥𝗲𝗽𝗲𝘁𝗶𝘁𝗶𝘃𝗲 𝗦𝗲𝗿𝘃𝗶𝗰𝗲 𝗥𝗼𝗹𝗲𝘀 𝗦𝗲𝗲 𝘁𝗵𝗲 𝗛𝗶𝗴𝗵𝗲𝘀𝘁 𝗜𝗺𝗺𝗲𝗱𝗶𝗮𝘁𝗲 𝗜𝗺𝗽𝗮𝗰𝘁 Ticket agents, telemarketers, phone operators, switchboard staff - the tasks here are predictable, rules-based, and easy for AI to automate. 𝟰. 𝗘𝘃𝗲𝗻 “𝗖𝗿𝗲𝗮𝘁𝗶𝘃𝗲” 𝗮𝗻𝗱 “𝗛𝘂𝗺𝗮𝗻-𝗙𝗮𝗰𝗶𝗻𝗴” 𝗝𝗼𝗯𝘀 𝗦𝗵𝗼𝘄 𝗨𝗽 Models, hosts, promoters, concierges - not replaced, but heavily transformed as AI handles decision-making, matching, and personalization. 𝟱. 𝗧𝗲𝗰𝗵𝗻𝗶𝗰𝗮𝗹 𝗝𝗼𝗯𝘀 𝗔𝗿𝗲𝗻’𝘁 𝗦𝗮𝗳𝗲 𝗘𝗶𝘁𝗵𝗲𝗿 Web developers, data scientists, and financial advisors appear on the list - showing that coding and data are no longer unchallenged domains. 𝗧𝗵𝗲 𝗥𝗲𝗮𝗹 𝗠𝗲𝘀𝘀𝗮𝗴𝗲 The study isn’t saying these 40 jobs disappear. It’s saying these 40 jobs change first. Because AI doesn’t replace people, It replaces tasks. And when a job is made of tasks AI can perform at near-zero marginal cost… the job evolves. The future belongs to people who learn to work with AI, not in spite of it. If your role is on this list, you’re not doomed - you’re early. Early to adapt, early to upskill, and early to leverage AI as a multiplier instead of a threat.

Research isn’t just gathering facts. It’s a structured, layered process, starting with framing the right question, pulling diverse sources, and synthesizing meaningful insight. An analyst might spend hours deciding where to look, validating sources, cross-checking contradictions, and shaping a usable output. That’s often many days of work for a well-rounded report. AI change the mechanics. With a well-structured prompt, a language model can simulate this entire workflow in parallel: → Scanning dozens of sources → Filtering based on context and credibility → Surfacing inconsistencies → And synthesizing a clear, structured report The outcome? What takes a human team days can be delivered in under 30 minutes, without cutting corners. But let’s be precise about what’s happening in those 30 minutes: Behind the scenes, the model: →Understands the brief instantly →Searches and filters live data →Reads and cross-checks 30–50+ sources →Writes structured content in real-time →Generates visuals on demand →Packages it all together What would take a team of humans: →Hours of sequential effort →Multiple roles (researcher, writer, designer, editor) →Coordination and review cycles gets compressed into parallel tasks executed within seconds or minutes. So yes, you receive the report in 30 minutes. But what you’re getting is hours of analysis, compressed, structured, and scaled. That’s the value of deep research with LLMs: Speed, yes, but more importantly: structure, insight, and strategic value. 🎥 In the video tutorial, we walk through a real use case: How we used ChatGPT’s deep research capabilities and Gamma to build a full competitor analysis report