Impact of Technology on Workforce

The expansion of robots and automation is poised to significantly transform the job market and has complex implications for inequality. What do you think? Impact on Jobs: 1. Job Displacement: Robots and automation are likely to replace repetitive, manual, and routine jobs (e.g., manufacturing, logistics, and data entry). Some middle-skill jobs may also be at risk as automation technologies become more sophisticated. 2. Job Creation: New roles will emerge in robotics maintenance, programming, AI development, and other tech-focused fields. Demand for human-centric jobs, such as healthcare, education, and creative industries, may increase as these areas are harder to automate. 3. Job Evolution: Many jobs will change in scope, requiring workers to collaborate with robots or leverage automation tools for productivity. Impact on Inequality: 1. Widening Skill Gap: Workers with higher education and tech-savvy skills are more likely to benefit, while those in low-skill jobs may struggle to adapt. This divergence could exacerbate income inequality if reskilling programs are not widespread. 2. Geographic Disparities: Advanced economies with resources to invest in automation could benefit more than developing countries, increasing global inequality. 3. Ownership of Technology: Concentration of robot and AI ownership among corporations and wealthy individuals might widen wealth disparities unless equitable policies (e.g., profit sharing, taxes) are implemented. Mitigating Inequality: 1. Education and Reskilling: Governments and companies need to invest in upskilling and reskilling workers to prepare them for the jobs of the future. 2. Universal Basic Income (UBI): UBI or similar safety nets could help address income gaps caused by job displacement. 3. Fair Policies: Regulations around labor, taxation, and profit sharing could ensure that the economic benefits of automation are distributed more equitably. 4. Support for Vulnerable Sectors: Strengthening social welfare systems and providing targeted support for industries and workers most at risk. Video: @discover_our_planet_ #Innovation #Technology #Inequality

If you're losing brilliant women at the final stages of hiring - this might be why... Let me talk you through a recent example where a company had a disproportionately high number of women dropping out at late interview and offer stage for their tech roles: They were offering great salaries. Flexible working. A decent benefits package. So what was going wrong? We took a look at the data. Out of 2 billion data points, a few things stood out: → Diversity is non-negotiable. Women in tech rank it 31% higher than the average candidate. If they don’t see representation in leadership, they won’t apply → Flexible hybrid work wins, because structure matters. Demand for remote-only roles is 11% below average, while core hours and in-office collaboration rank higher → Family-friendly policies trump flashy perks. Fertility leave (+41%), job sharing (+33%), and parental leave (+19%) are the real differentiators But then we dug deeper; and that's where it got really interesting: → Women in data roles showed a higher demand for in-office work - mentorship and access to resources mattered → Women in engineering & development wanted mission-driven work and career progression above all else → Women in product roles prioritised culture and flexibility more than any other group The company checked their employer brand. Their careers page talked about “great culture” and “exciting opportunities.” But it said nothing about what actually mattered to the people they were trying to hire. They weren’t losing candidates because of the salary or the benefits. They were losing them because they don't know what their target talent groups actually want. The companies getting this right aren’t guessing. They’re using data to shape their employer brand - so they attract the right people, with the right message. Download our women in tech report to access more of these insights: https://lnkd.in/enYcGpeW And tell me if you've turned down a job offer for similar reasons? #WomenInTech #Hiring #EmployerBranding #FutureOfWork #DiversityMatters

𝐓𝐡𝐞 𝐒𝐀𝐏 𝐣𝐨𝐛𝐬 𝐭𝐡𝐚𝐭 𝐰𝐢𝐥𝐥 𝐛𝐞 𝐤𝐢𝐥𝐥𝐞𝐝, 𝐚𝐧𝐝 𝐒𝐀𝐏 𝐣𝐨𝐛𝐬 𝐭𝐡𝐚𝐭 𝐰𝐢𝐥𝐥 𝐭𝐡𝐫𝐢𝐯𝐞 𝐛𝐞𝐜𝐚𝐮𝐬𝐞 𝐨𝐟 𝐀𝐈. AI will have a profound impact on the workforce at both SAP itself and SAP consulting companies. Some jobs will likely become obsolete as AI proves to be faster, more efficient, and cost-effective. Other roles will not only survive but flourish, as AI enhances their scope and enables new levels of innovation and efficiency. 𝐑𝐨𝐥𝐞𝐬 𝐦𝐨𝐬𝐭 𝐚𝐭 𝐫𝐢𝐬𝐤 𝐨𝐟 𝐛𝐞𝐢𝐧𝐠 𝐧𝐞𝐠𝐚𝐭𝐢𝐯𝐞𝐥𝐲 𝐢𝐦𝐩𝐚𝐜𝐭𝐞𝐝 𝐢𝐧𝐜𝐥𝐮𝐝𝐞: 𝐌𝐚𝐧𝐮𝐚𝐥 𝐒𝐀𝐏 𝐓𝐞𝐬𝐭𝐢𝐧𝐠 𝐂𝐨𝐧𝐬𝐮𝐥𝐭𝐚𝐧𝐭𝐬 ❌ AI-powered test automation will eliminate most manual SAP testing. Who survives? Test engineers skilled in AI-assisted test automation. 𝐁𝐚𝐬𝐢𝐜 𝐀𝐁𝐀𝐏 𝐃𝐞𝐯𝐞𝐥𝐨𝐩𝐞𝐫𝐬 (𝐂𝐮𝐬𝐭𝐨𝐦 𝐄𝐧𝐡𝐚𝐧𝐜𝐞𝐦𝐞𝐧𝐭𝐬 𝐟𝐨𝐫 𝐄𝐂𝐂/𝐒/𝟒𝐇𝐀𝐍𝐀) ❌ AI can generate and optimize standard ABAP code automatically. Who survives? SAP BTP developers (working on event-driven architectures, AI-powered extensions). 𝐋𝐨𝐰-𝐋𝐞𝐯𝐞𝐥 𝐒𝐀𝐏 𝐒𝐮𝐩𝐩𝐨𝐫𝐭 (𝐋𝟏 & 𝐋𝟐 𝐇𝐞𝐥𝐩𝐝𝐞𝐬𝐤) ❌ AI chatbots & predictive issue resolution will replace many support tickets. Who survives? AI-powered SAP support strategists. 𝐁𝐚𝐬𝐢𝐜 𝐂𝐨𝐧𝐭𝐞𝐧𝐭 𝐂𝐫𝐞𝐚𝐭𝐨𝐫𝐬 & 𝐂𝐨𝐩𝐲𝐰𝐫𝐢𝐭𝐞𝐫𝐬 𝐰𝐢𝐭𝐡𝐨𝐮𝐭 𝐜𝐮𝐬𝐭𝐨𝐦𝐞𝐫 𝐜𝐨𝐧𝐭𝐚𝐜𝐭 ❌ AI tools (like ChatGPT, Jasper, and SAP AI Copilots) can generate marketing copy, blog articles, and product descriptions in seconds. Who survives? AI-enhanced content strategists who focus on brand differentiation, thought leadership & SAP-specific narratives. 𝐒𝐀𝐏 𝐉𝐨𝐛𝐬 𝐓𝐡𝐚𝐭 𝐖𝐢𝐥𝐥 𝐓𝐡𝐫𝐢𝐯𝐞 𝐚𝐧𝐝 𝐆𝐚𝐢𝐧 𝐈𝐦𝐩𝐨𝐫𝐭𝐚𝐧𝐜𝐞: 𝐓𝐫𝐚𝐧𝐬𝐟𝐨𝐫𝐦𝐚𝐭𝐢𝐨𝐧 𝐂𝐨𝐧𝐬𝐮𝐥𝐭𝐚𝐧𝐭𝐬 🚀 Why? AI-driven business processes require strategic alignment & implementation. Future-proof skills: AI-powered business process optimization, SAP AI integration, SAP AI ethics. 𝐒𝐀𝐏 𝐂𝐥𝐨𝐮𝐝 𝐀𝐫𝐜𝐡𝐢𝐭𝐞𝐜𝐭𝐬 & 𝐄𝐑𝐏 𝐒𝐭𝐫𝐚𝐭𝐞𝐠𝐢𝐬𝐭𝐬 🚀 Why? AI-driven SAP solutions are moving to cloud-native & hybrid environments. Future-proof skills: SAP BTP, AI-enhanced workflow automation 𝐏𝐫𝐨𝐜𝐞𝐬𝐬 𝐀𝐮𝐭𝐨𝐦𝐚𝐭𝐢𝐨𝐧 & 𝐇𝐲𝐩𝐞𝐫𝐚𝐮𝐭𝐨𝐦𝐚𝐭𝐢𝐨𝐧 𝐄𝐱𝐩𝐞𝐫𝐭𝐬 🚀 Why? AI-driven RPA, intelligent workflows & autonomous supply chains will reshape SAP implementations. Future-proof skills: SAP Intelligent RPA, AI-driven BPM, process mining. 𝐖𝐡𝐚𝐭 𝐭𝐨 𝐃𝐨 𝐍𝐞𝐱𝐭? ✔ Learn AI-driven SAP tools (SAP Joule, Datasphere, AI Core, SAP AI API development). ✔ Shift from execution (configuration & support) to AI-powered strategy & process optimization. ✔ Develop hybrid skills (AI, cloud-native SAP, data analytics, cybersecurity). AI isn’t replacing SAP experts or eliminating consultant jobs—it’s shaping a new generation of 𝐀𝐈-𝐞𝐦𝐩𝐨𝐰𝐞𝐫𝐞𝐝 𝐄𝐑𝐏 𝐬𝐩𝐞𝐜𝐢𝐚𝐥𝐢𝐬𝐭𝐬. Those who adapt to this shift early will be leading the disruption, not just surviving it. Do you agree? #sap #ai #technology #jobs

New! We analyzed a billion job postings globally, and the results may surprise you: job numbers and wages are rising. Let’s dive in. For the second year running, the 2025 Global AI Jobs Barometer from PwC shows that productivity and wages are not just rising, they’re accelerating, even in roles that are most amenable to automation. Our research spans six continents and includes data from 24 countries and territories. 💭 100% of industries are expanding their usage of AI (even industries less obviously exposed to AI such as mining and construction) 📊 Since 2022 when awareness of AI's power surged, productivity growth in industries best positioned to adopt AI has nearly quadrupled (while falling slightly in industries least exposed to AI) 3️⃣ Industries most able to use AI have 3x higher growth in revenue generated by each employee  🪙 Workers with AI skills command a 56% wage premium (up from 25% last year) ⚒️ Skills sought by employers are changing 66% faster in occupations most exposed to AI (like financial analyst) versus least exposed (like physical therapist) – up from 25% last year . AI continues to act as an amplifier of human expertise — not a replacement for it, despite what the headlines might suggest. The prime example being that job growth is occurring even in roles where "automation" is playing the biggest role (like customer service and software engineering). Job cuts and doomerism make headlines, but job creation takes longer to materialize and to be recognized. It’s the difference between weather and climate, and why we call this report a "barometer". As the shifting sands of the past two years begin to settle into clearer patterns, there’s never been a better time to dive in, get hands-on, and lead your teams through this transformation. Link to the full report below.

Bloomberg just published the conversation I had with their team about how we're using AI and robotics to transform manufacturing, and they captured something important that often gets lost in these discussions.   When people hear "AI in manufacturing," they often picture robots replacing workers. That's not what we're building.   At the Hyundai Motor Group Innovation Center Singapore (HMGICS), we are exploring what some call a "dark factory" due to its high level of automation. The goal isn't eliminating human jobs. It's elevating human work.   We don't need more people tightening bolts repetitively. We need more engineers designing systems, more technicians maintaining intelligent equipment, more problem-solvers optimizing production. AI and robotics handle the repetitive tasks. Humans handle judgment, creativity, and continuous improvement.   As I mentioned in the conversation, "We are a tech company that happens to be in the automotive business." That shift, from purely mechanical manufacturing to software-defined production, changes everything about how we serve customers.   We can produce ten different models on the same line at HMGICS and switch between ICE, hybrid, and EV in real-time based on what markets want. We can respond quickly because our manufacturing systems are intelligent enough to adapt.   That flexibility, powered by AI, is what lets us deliver the right vehicle to the right customer at the right time, not force customers to accept what we happen to be producing.   We're scaling this approach from Singapore to Hyundai Motor Group Metaplant America (HMGMA) and beyond. Sixty percent of HMGICS innovations are already deployed in Georgia. This isn't pilot-stage experimentation, it's industrial transformation in practice.   Thanks to Angie Lau and the Bloomberg team for the conversation and for helping tell this story. In an age of extremes, the companies that thrive will be those that use technology to maximize human potential, not replace it. It's a great time to be with Hyundai Motor Company!

As GenAI becomes more ubiquitous, research alarmingly shows that women are using these tools at lower rates than men across nearly all regions, sectors, and occupations.   A recent paper from researchers at Harvard Business School, Berkeley, and Stanford synthesizes data from 18 studies covering more than 140k individuals worldwide.   Their findings:   • Women are approximately 22% less likely than men to use GenAI tools • Even when controlling for occupation, age, field of study, and location, the gender gap remains • Web traffic analysis shows women represent only 42% of ChatGPT users and 31% of Claude users   Factors Contributing the to Gap:   - Lack of AI Literacy: Multiple studies showed women reporting significantly lower familiarity with and knowledge about generative AI tools as the largest gender gap driver. - Lack of Training & Confidence: Women have lower confidence in their ability to effectively use AI tools and more likely to report needing training before they can benefit from generative AI.   - Ethical Concerns & Fears of Judgement: Women are more likely to perceive AI usage as unethical or equivalent to cheating, particularly in educational or assignment contexts. They’re also more concerned about being judged unfairly for using these tools.   The Potential Impacts: - Widening Pay & Opportunity Gap: Considerably lower AI adoption by women creates further risk of them falling behind their male counterparts, ultimately widening the gender gap in pay and job opportunities. - Self-Reinforcing Bias: AI systems trained primarily on male-generated data may evolve to serve women's needs poorly, creating a feedback loop that widens existing gender disparities in technology development and adoption.   As educators and AI literacy advocates, we face an urgent responsibility to close this gap and simply improving access is not enough. We need targeted AI literacy training programs, organizations committed to developing more ethical GenAI, and safe and supportive communities like our Women in AI + Education to help bridge this expanding digital divide.   Link to the full study in the comments. And a link also to learn more or join our Women in AI + Education Community. AI for Education #Equity #GenAI #Ailiteracy #womeninAI

Artificial intelligence is often framed as a story about jobs disappearing. That is the wrong message. The foundational shift is improved productivity. When new technologies arrive they rarely eliminate work altogether. They change how work is done, create new roles, and expand what people and organisations are capable of producing. The printing press did it. Electricity did it. The internet did it. AI will be no different. Yesterday I spoke with ABC’s Kirsten Aiken about how artificial intelligence is already reshaping the economu and why the scale of the opportunity will depend on how we choose to deploy it. If AI is used purely as a cost reduction tool, the conversation will stay trapped in a fear narrative around labour displacement. If it is deployed as a productivity engine, it becomes something very different. It allows people to work at higher levels of abstraction, automate repetitive tasks, and focus human capability on communication, creativity, decision making and complex problem solving. That is how new industries and new jobs emerge. Australia has a major opportunity in front of it. Building the infrastructure that powers artificial intelligence is not only about technology. It is about enabling the next wave of productivity growth across the entire economy. AI infrastructure, renewable energy, and digital capability are becoming key to global competitiveness. The question is not whether AI will change work. It will. The question is whether we position ourselves to capture the productivity gains that come with it. #ai #abc #thebusiness Link to the full interview here: https://lnkd.in/g36AujVX

Your best AI users are twice as likely to quit. Employees gaining the most productivity from AI are simultaneously experiencing 88% higher burnout rates—and they're developing better relationships with AI than with their human coworkers. "This is one of the biggest warning signals I've ever seen in any of the research I've conducted on AI over the last 12 years," Kelly Monahan, Ph.D. told our Charter Forum group last week. "There's something fundamentally broken in the way we are relating to each other within our organizations." Here's what's driving this crisis: 🔴 Pressure for infinite output: High performers are being pushed to go higher than ever, and have the drive and traits to match. But ... 🔴 Human connection eliminated: The focus on "more output" leaves no time for the relationships that make work sustainable and meaningful. 🔴 Middle managers hit hardest: Those using AI heavily while managing teams face the worst burnout of all—exactly the people you need to build the trust needed to scale AI adoption. The problem isn't the technology. It's that too often we're measuring AI adoption as a performance metric and focusing on efficiency instead of opportunity. More, not better. Cutting costs, not growth. Meanwhile, 68% of workers report struggling with the pace and volume of work according to Microsoft. We're in the era of "do more with less" and the infinite workday—and our best people are breaking. Your heavy AI users aren't just your highest performers. They're your canaries in the coal mine. Are you seeing burnout among your AI adopters? 👉 Read on: https://lnkd.in/g-jMnatU

Most companies wait until they have an urgent problem before addressing workforce capability. But the ones building competitive advantage are investing in readiness before the gap becomes a crisis. Here are four areas where organizations need to focus: 𝟭. 𝗥𝗲𝘀𝗸𝗶𝗹𝗹𝗶𝗻𝗴 𝗳𝗼𝗿 𝗿𝗼𝗹𝗲𝘀 𝘁𝗵𝗮𝘁 𝗱𝗶𝗱𝗻'𝘁 𝗲𝘅𝗶𝘀𝘁 𝗳𝗶𝘃𝗲 𝘆𝗲𝗮𝗿𝘀 𝗮𝗴𝗼 Automation specialists, data scientists, and AI integration roles require new training pathways. Companies that build apprenticeship programs and internal development tracks get ahead of skills bottlenecks before they slow growth. 𝟮. 𝗣𝗿𝗲𝗽𝗮𝗿𝗶𝗻𝗴 𝘁𝗲𝗮𝗺𝘀 𝘁𝗼 𝘄𝗼𝗿𝗸 𝗮𝗹𝗼𝗻𝗴𝘀𝗶𝗱𝗲 𝗔𝗜 It's not enough to deploy AI tools. Teams need to understand how to integrate AI into their workflows, manage AI-driven processes, and improve performance through human-AI collaboration. 𝟯. 𝗜𝗱𝗲𝗻𝘁𝗶𝗳𝘆𝗶𝗻𝗴 𝘀𝗸𝗶𝗹𝗹 𝗴𝗮𝗽𝘀 𝗯𝗲𝗳𝗼𝗿𝗲 𝘁𝗵𝗲𝘆 𝗮𝗳𝗳𝗲𝗰𝘁 𝗼𝗽𝗲𝗿𝗮𝘁𝗶𝗼𝗻𝘀 Skills assessments show what people can actually do, not just what their job titles suggest. Companies that map capabilities across their workforce can redeploy talent strategically and keep people engaged in roles where they can grow. 𝟰. 𝗖𝗿𝗲𝗮𝘁𝗶𝗻𝗴 𝗽𝗮𝘁𝗵𝘄𝗮𝘆𝘀 𝗶𝗻𝘁𝗼 𝗿𝗼𝗹𝗲𝘀 𝘄𝗵𝗲𝗿𝗲 𝗽𝗲𝗼𝗽𝗹𝗲 𝗰𝗮𝗻 𝘀𝘂𝗰𝗰𝗲𝗲𝗱 Whether it's technical training, role-specific development, or management skills, companies need structured programs that prepare people for the work that's coming, not just the work that exists today. The retirement wave is gathering speed. Skills-based hiring is becoming the norm. Growth isn't waiting. What's your approach to workforce readiness right now?