Impact of Automation

🤖 The European Union needs to rapidly upskill its citizens if it's going to capitalise on the benefits that artificial intelligence can bring, according to a report from LinkedIn's Economic Graph team. AI has been hailed as a technology that can help humans with everything from boosting office productivity to drug discovery. But a lack of talent is one of the biggest hurdles. 🗒 AI talent makes up just 0.41% of EU workers, LinkedIn's report, AI in the EU, found. While that's a 126% increase on 2016, and more than the UK (0.35%) and the US (0.34%), the bloc still needs more people who know how to get the most out of the technology. 📍 As it stands, just 26.3% of the EU's AI talent is female, which is less than the UK (27.7%) and the US (29.8%). It will take 162 years to reach gender parity if the gap keeps on closing at the current rate, according to the report. Addressing the gender imbalance in AI is one way the EU could try and close the skills gap, according to the report. In terms of AI's impact on the workforce, women are likely to be disproportionately impacted by AI, and generative AI (gen AI) in particular, which is capable of creating a variety of content including emails and presentations. Gen AI is poised to impact a number of jobs that tend to be held by women including medical clerks, clinical research assistants and sales operations assistants. 🗣️ What’s your take on these findings? Are you aware of AI’s impact and its presence within the EU workforce? We’d love to hear your thoughts in the comments. Full report: https://lnkd.in/g3_EhhiP 🖊️ Sam Shead  📸 Getty Images #AIInTheEU

What is CI/CD automation? CI/CD automation is a process of automating the following stages of the software delivery pipeline: Continuous integration (CI): This involves automating the process of building and testing your code every time a change is made. Continuous delivery or deployment (CD): This involves automating the process of deploying your code to production. 𝗔𝗿𝗰𝗵𝗶𝘁𝗲𝗰𝘁-𝗗𝗿𝗶𝘃𝗲𝗻 𝗔𝘂𝘁𝗼𝗺𝗮𝘁𝗶𝗼𝗻: The Blueprint of Excellence The true power of CI/CD is unleashed through architect-driven automation. This approach leverages the expertise of architects to design CI/CD pipelines that are not only efficient but also resilient and secure. By  planning the automation strategy, architects ensure that the pipeline is aligned with the project's goals, technology stack, and operational requirements. This strategic oversight is critical in optimizing the CI/CD process, minimizing bottlenecks, and ensuring that the automation tools and practices adopted are the best fit for the project. 𝗪𝗵𝘆 𝗖𝗜/𝗖𝗗 𝗠𝗮𝘁𝘁𝗲𝗿𝘀: Beyond Speed and Efficiency While CI/CD is often celebrated for its ability to speed up software delivery, its benefits extend far beyond just efficiency: - 𝗘𝗻𝗵𝗮𝗻𝗰𝗲𝗱 𝗤𝘂𝗮𝗹𝗶𝘁𝘆: Automated testing ensures that bugs are caught early, improving the overall quality of the software. - 𝗙𝗮𝘀𝘁𝗲𝗿 𝗙𝗲𝗲𝗱𝗯𝗮𝗰𝗸 𝗟𝗼𝗼𝗽: Continuous integration provides immediate feedback on code quality, allowing developers to make quick adjustments. - 𝗥𝗲𝗱𝘂𝗰𝗲𝗱 𝗥𝗶𝘀𝗸: Smaller, more frequent deployments reduce the risk associated with releasing new features or changes. - 𝗜𝗻𝗰𝗿𝗲𝗮𝘀𝗲𝗱 𝗣𝗿𝗼𝗱𝘂𝗰𝘁𝗶𝘃𝗶𝘁𝘆: Automation frees developers from manual tasks, allowing them to focus on creating value. The journey to implementing CI/CD may seem daunting, but the rewards are unparalleled. By embracing architect-driven automation, organizations can not only accelerate their software delivery but also enhance their ability to respond to market changes and customer needs swiftly. As we navigate the complexities of modern software development, let's champion the adoption of CI/CD practices. It's time to shift our mindset, innovate relentlessly, and drive towards a future where software development is more agile, resilient, and aligned with the ever-changing digital landscape.

The advent of robotics in gardening and agriculture is poised to revolutionize the industry, driving significant changes in various aspects. What do you think about this solution? Increased Efficiency and Productivity: Precision Farming: Robots equipped with sensors and AI can analyze soil conditions, plant health, and weather patterns to optimize resource allocation, leading to higher yields and reduced waste. 24/7 Operation: Unlike human workers, robots can operate around the clock, maximizing productivity and accelerating crop cycles. Minimized Labor Costs: Automation of repetitive tasks like weeding, harvesting, and planting can reduce reliance on manual labor, lowering operational costs. Enhanced Sustainability: Resource Optimization: Robots can precisely apply water, fertilizers, and pesticides, minimizing environmental impact and reducing costs. Reduced Chemical Use: AI-powered robots can identify and target specific pests and weeds, limiting the need for broad-spectrum chemical treatments. Sustainable Practices: Robots can facilitate sustainable farming practices like precision agriculture and organic farming, promoting long-term ecosystem health. Improved Food Quality and Safety: Consistent Quality: Robots can maintain consistent standards for harvesting and processing, ensuring uniform product quality. Reduced Contamination: Automated systems can minimize the risk of contamination from human error or biological factors. Traceability: Robotics can enable precise tracking of food products from farm to table, enhancing food safety and traceability. Challenges and Considerations: Initial Investment: The high cost of robotic systems may be a barrier for small-scale farmers. Technical Expertise: Operating and maintaining complex robotic systems requires specialized skills and training. Job Displacement: Automation may lead to job losses in certain sectors, necessitating workforce retraining and upskilling. Ethical Concerns: The use of AI and robotics in agriculture raises ethical questions about the role of technology in food production and potential environmental impacts. The Future of Agriculture: The integration of robotics in gardening and agriculture is likely to reshape the industry, leading to increased efficiency, sustainability, and food security. While challenges remain, the potential benefits of this technological revolution are immense. As technology continues to advance, we can expect to see even more innovative applications of robotics in the years to come. #Ai #innovation #technology

We’ve called efficiency the unsung hero of the energy transition in the past. While the energy transition will happen first through the transition of energy usages, like the shift with transport, from internal combustion engines to electric vehicles, or from fuel or gas boilers to heat pumps, we cannot ignore the utmost priority of the energy transition: efficiency. Efficiency is the greatest path to reduce our energy use, our impact on the world’s climate through CO2 emission reduction, and very importantly, the best way to make solid and practical savings. In its most historical form, energy efficiency is about better insulation, to reduce heating (or cooling) loss in buildings like family homes, warehouses, office high rises, and shopping malls. This is useful, but expensive and tedious to realize on existing installations. Digitizing home, buildings, industries and infrastructure brings similar benefits at a much lower cost and a much higher economic return. The combination of IoT, big data, software and AI can significantly reduce energy use and waste by detecting leaky valves, or automatically adjusting heating, lighting, processes and other systems to the number of people present at any given time, using real-time data analysis. It also allows owners to measure precisely progress, report automatically on their energy and sustainability parameters, and benefit from new services through smart grid interaction. And this is just the energy benefit. Automation and digital tools also optimize the processes, safety, reliability, and uptime leading to greater productivity and performance.

New research joint with Maxim Massenkoff: How is AI affecting the US labor market? In this research brief, we introduce a new measure of AI displacement risk to spot disruption, then test it against employment data. We find limited evidence AI has increased unemployment to date. Our measure, "observed exposure," compares the tasks LLMs are theoretically capable of to the tasks people actually use Claude for at work. We find that actual usage is far from reaching theoretical capability. This measure tracks with independent forecasts. Jobs with higher observed exposure to AI are projected by the BLS to grow more slowly over the next decade. We find limited evidence, however, that AI is playing a role in the broader labor market today. The top 25% of workers most exposed to AI automation have similar trends in unemployment rates to workers with no exposure at all. Hiring of younger workers in the most exposed occupations appears to have slowed faster than for non-exposed roles, but our estimates are imprecise and other non-AI factors may be playing a role. This research is a first step. Our goal is to establish an approach for measuring how AI is affecting employment, and to build on these analyses periodically as more data becomes available.

AI adoption is accelerating faster than the energy systems built to support it. Data centers are already among the most power-intensive assets on the grid and are seeing demand rise at rates that legacy infrastructure, static operating models, and fragmented regional grids were simply not designed to handle. The consequence is predictable: higher costs, growing emissions, and mounting pressure on utilities and operators trying to maintain reliability while integrating renewables. I’ve spent much of my career working at the intersection of technology, energy policy, and industrial systems, and this challenge is proving to be one of the defining infrastructure questions of the decade. It’s increasingly clear that the sector needs new ways to manage load, forecast demand, and coordinate resources across highly variable conditions. This week, I had the opportunity to hear from senior leaders at Hanwha Qcells about a model they are developing that aims to address these pressures. What stood out to me was the architectural shift behind the technology: using AI, interoperable language, and digital twins to unify diverse equipment, link operations to real-time grid signals, and automate many of the repetitive, checklist-style decisions that currently consume operator time. This broader concept of treating data centers as intelligent, grid-aware assets aligns with conversations happening across industry and government. The framework they described integrates clean generation, storage, and control software into a single adaptive system. The goal is straightforward but ambitious: reduce wasted energy, cut emissions, and improve resilience as AI demand grows. Their lofty projections (20–30% cost reductions, up to 35% emissions cuts, faster response times through agentic operations) reflect why approaches like this are gaining momentum. What interests me most is how these ideas fit into the larger trend: the shift toward an “Intelligent Age” where digital growth and energy management are inseparable... remember when VPPs were unheard of? Solutions that improve transparency, interoperability, and operational flexibility will be essential, and not just for data centers, but for manufacturing, transportation, and other power-intensive sectors facing similar constraints. As we look ahead, the real opportunity is in building systems that scale, adapt, and operate with far greater situational awareness. The conversation with Qcells underscored how quickly this space is evolving and why collaboration across utilities, technology developers, operators, and policymakers will be critical in the years ahead. Article link: https://bit.ly/4qggMLd #Hanwha | #HanwhaQcells | #Microsoft | #AI | #DataCenters | #EnergyManagement | #GridModernization | #CleanEnergy | #Innovation

By 2030, 170 million new jobs will be created and 92 million will disappear. (World Economic Forum: Future of Jobs Report) Most people see those numbers and think about loss. But when I look at them, I see something different: A once-in-a-generation redesign of what work is. Not because humans are being pushed aside  but because AI is changing how we create value. And if you look closely, you can already see the shift happening. Over the past few years, I’ve watched entirely new roles appear inside teams I’ve coached and partnered with. Roles that didn’t exist, weren’t taught in school, and had no playbook to follow: Prompt Engineer teaching AI how to think • AI Risk & Governance Specialist, protecting safety and fairness • Decision Engineer, blending human judgment with AI execution • AI Ethicist, guarding what must remain human • AI Operations / ML Ops, keeping models stable in the real world • Head of AI, shaping the strategy, culture, and adoption • AI Translator, turning business problems into AI workflows • Model Validator, ensuring models stay accurate and unbiased Three years ago these sounded experimental. Now they’re becoming essential. 🔄 The real shift isn’t “jobs disappearing” it’s that jobs are changing shape. AI is taking over the parts of work that drain people: • the repetition • the manual steps • the data chasing • the admin • the coordination • the first drafts And as that happens, something interesting emerges: Humans finally get to focus on the parts of work that are actually human. • strategy • creativity • judgment • ethics • communication • leadership • meaning Every leader I’ve worked with eventually realizes this: AI doesn’t threaten the work, it transforms what the work is worth. If you want to stay relevant in this new era, the advantage isn’t “knowing AI.” It’s knowing how to work with it. Here’s where to start: 1. Learn the language. Not to become an engineer  but to understand how LLMs, agents, and AI systems think. 2. Choose your path. Technical or strategic. Both matter more than ever. 3. Build something small. A workflow. A prototype. A simple automation. Hands-on learning hits differently. 4. Strengthen the human skills. The ones AI can’t replicate: judgment, communication, ethics, design thinking. 5. Stay adaptable. In a world moving this fast, curiosity becomes a career strategy. **The future of work isn’t about competing with AI. It’s about learning how to partner with it  so humans can rise to the work that truly needs us.** Because at the end of the day, the future of work is still a story about people. 🔁 Repost to help someone navigate this shift ➡️ Follow Gabriel Millien for human-centered insights on AI, LLMs, and the future of work

Welding has always been at the core of fabrication and manufacturing, yet it’s one of the most challenging processes to scale. Why? TIG welding, known for its precision and quality, is labor-intensive, time-consuming, and requires a high level of skill. But in today’s landscape, fabricators and manufacturers face growing pressure to deliver faster, at a lower cost, and with fewer skilled welders available. This is where welding automation is making an impact. Take TIG welding, for example. Traditional methods are limited by manual processes, but advancements like the SWR-TIPTIG system are showing how automation can improve both productivity and quality while addressing labor shortages. Here’s what’s changing: 1️⃣ Speed and precision coexist: Historically, TIG welding prioritized precision over speed. But with systems like TIPTIG, welders can achieve up to 2.6x faster deposition rates without sacrificing quality. 2️⃣ Expanding accessibility: Automation reduces the reliance on highly specialized welders, allowing a broader range of operators to achieve high-quality results. This isn’t about replacing welders—it’s about enabling them to work smarter. 3️⃣ Worker safety and ergonomics: Automation minimizes exposure to hazardous fumes, radiant heat, and repetitive physical strain. As a result, the operator is less fatigued and more focused on managing the process, not the physical task. 4️⃣ Reducing costs beyond the weld: Automation cuts down on post-weld cleanup with minimal spatter and increases overall throughput by integrating seamlessly into production workflows. These advancements are particularly critical in industries where weld integrity is non-negotiable, such as aerospace, oil & gas, and pharmaceuticals. Applications that involve exotic materials like stainless steel and Inconel demand precision that automation can now consistently deliver. The broader takeaway? Automation is no longer just a productivity tool—it’s a strategic decision for staying competitive. As project timelines tighten and customers demand higher quality at lower costs, adopting solutions that align with these expectations is essential. Those who embrace these innovations are not just improving processes—they’re shaping the future of fabrication. Let’s continue the conversation about where automation is taking us and how we can solve the challenges ahead. What do you see as the biggest hurdle to automation in your industry?

This headline captures a growing reality: China’s rapid automation drive is reshaping global industrial competition. The charts below the headline tell the real story — China now installs more industrial robots each year than the rest of the world combined, and its robot density (robots per 10,000 workers) has surged past advanced economies like Germany, the US, and Japan. This transformation isn’t just about scale. It reflects a deep structural shift — from labor-cost advantage to productivity and precision dominance. Chinese factories, powered by robotics and AI, are fast becoming the global benchmark for efficiency, threatening to erode the technological and manufacturing edge long held by Western economies. For multinational executives, the “fear” stems less from politics and more from competitiveness: China’s mix of automation, vertical integration, and government-backed industrial strategy is creating a self-reinforcing ecosystem — one that could define the next industrial era. Sources: on graph

When you look at the data, AI is clearly not diminishing human labor, but redefining and enhancing it across the board. PwC's new 2025 Global AI Jobs Barometer draws on nearly a billion job ads and thousands of financial reports to show that AI is boosting productivity, increasing wages, and evolving roles, even those most susceptible to automation. The report is well worth a look. Here are some of the standout findings: 📈 AI-exposed industries see 3x faster productivity growth. Industries most able to use AI achieved a 27% growth in revenue per employee between 2018–2024, compared to just 8.5% in the least exposed sectors. 💰 AI boosts wages—especially for those with skills. Workers with AI skills earn, on average, 56% more than their peers in the same roles without such skills. This wage premium has grown from 25% just a year ago, signaling rising demand and perceived value for AI capabilities. 📊 Wage growth outpaces in AI-heavy sectors. Wages grew 2x faster in industries most exposed to AI (16.7%) compared to the least exposed (7.9%) from 2018–2024. Contrary to fears, even highly automatable jobs are seeing wage gains, suggesting AI is augmenting rather than replacing human value. 🚺 Women dominate AI-exposed roles—creating both promise and risk. In every country studied, women hold a greater share of AI-exposed jobs than men, with superior scope for augmentation as well as automation. 🧠 AI accelerates a “skills earthquake.” The skills required in AI-exposed jobs are changing 66% faster than in less exposed roles—more than 2.5x the pace of change last year. This is especially dramatic in automatable jobs, suggesting roles are evolving toward higher complexity and value. 🎓 Degrees matter less in an AI-driven job market. Degree requirements have declined more steeply for AI-exposed jobs, as companies prioritize up-to-date skills over formal credentials. This may reflect the “democratization of expertise,” where AI helps workers acquire and apply expert knowledge rapidly. 🧑💻 Automatable jobs are being upskilled, not eliminated. Despite being most vulnerable to automation, automatable roles are experiencing faster wage growth and greater skills disruption than augmentable ones. These jobs are being reshaped toward more complex, judgment-based tasks that demand higher capabilities. 🏭 AI job demand surges across all sectors—even traditional ones. The share of job postings requiring AI skills is growing in every industry, including low-tech sectors like agriculture and construction. 🧑🤝🧑 CEOs see AI as a people-powered value engine. 70% of global CEOs expect AI to transform value creation in their companies, and 82% say it hasn’t reduced headcount. Workers agree: 70% of GenAI users report more creativity, learning, and quality in their work, showing AI is enhancing—not eroding—human potential.