Impact of Technology on Workforce

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

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

The manufacturing landscape is evolving rapidly, driven by AI, sustainability, and agility. My experience at RSWM Limited has shown that progress stems from blending technology with human insight. Beyond automation, success lies in intelligent collaboration. Agentic AI predicts maintenance, optimises supply chains, and boosts efficiency. Value emerges when teams innovate with these systems. Our shift to biofuels and zero-liquid-discharge operations illustrates how discipline transforms waste into value and enhances profitability. Sustainability is core to strategy. Circular models, recycled materials, and bio-fabrication set new standards. GreenStitch’s AI platform supports this by centralising data, automating ESG reporting, and tracking carbon footprints for informed decisions. Agility is vital amid trade shifts and climate disruptions. Market diversification and digital adoption foster resilience: the strength Indian manufacturing has shown across cycles. The future of manufacturing depends on intelligence, agility, and purpose. AI-enabled factories and digital supply chains are becoming standard practice while sustainability is embedded in operations rather than positioned as a CSR initiative. Leadership excels via effective technology integration: data-driven decisions, balanced profitability, responsive systems, and skilled teams. Concerns about AI replacing jobs ignore historical trends. Technology has always redefined roles rather than eliminated work. Supply chains are now AI-driven, equipment uses smart sensors, automated changeovers are standard, and predictive insights have replaced manual inspection. Customer engagement has moved from physical catalogues to digital portfolios, meeting global regulatory and market standards. Today’s manufacturing leaders must ask sharper questions, take informed risks, and build organisations that evolve continuously. Future factories will rely on engineering excellence, strategic clarity, and strong cultural alignment. #manufacturing #AI #agenticAI #technology #leadership #leadwithrajeev

With AI adoption raising alarms about automation and job loss, new data from the Principal Financial Group’s Well-Being Index (which surveys 1,000 U.S. business leaders) adds nuance to the conversation.    Among businesses with fewer than 10,000 employees that currently use or plan to use AI:     ⬆️ 60% say employee wages will increase.   📈 79% say staffing will increase or remain neutral.      Why it matters: While concerns about AI replacing workers persist, these findings suggest that, at least among smaller employers surveyed, AI is viewed as a tool to optimize the workforce, not reduce it. This aligns with broader survey data that points to staffing stability in 2025.     But the real pressure point? Specialized talent. 🔍 39% of businesses are concerned about a shortage of qualified employees, signaling that finding skilled labor, not automation, remains the bigger hurdle. As AI adoption accelerates, the conversation is shifting from job elimination to preparing talent for new ways of working. From AI literacy to rethinking HR strategies, organizations are facing important questions:  💡 How do we balance automation with human expertise?   💡 How do we equip the next generation for an AI-driven economy? Is AI a job disruptor or a workforce optimizer in your organization? How is your team navigating the shift? I’d love to hear your take. Check out the full index findings: https://lnkd.in/gPaZBXHU

An interesting new paper reveals a surprising consequence of generative AI: it's making labor markets less efficient at identifying top talent. This fascinating job market paper from Princeton and Dartmouth studied what happened when large language models disrupted traditional hiring signals. Before ChatGPT, employers valued customized job applications because the effort required to tailor them credibly signaled worker quality. Top workers invested time to demonstrate their fit—and it worked. Then LLMs made customization nearly costless. The results? Striking. Using data from Freelancer.com and a structural model of labor market signaling: - High-ability workers (top quintile) are now hired 19% less often - Low-ability workers (bottom quintile) are hired 14% more often - Employers can no longer distinguish signal from noise. When everyone can produce polished, tailored applications instantly, writing loses its informational value. The market becomes less meritocratic. Because it becomes harder to differentiate workers pay decreases. A great example of asymmetric information creates something akin to Akerlof's Market for Lemons. This has implications beyond freelancing, implying that recruiters need to be thinking about how to improve their application processes in a world where differentiation is more difficult A good-read for anyone thinking about AI's impact on labor markets and matching efficiency. Link to paper: https://lnkd.in/dJQn7i9m #AI #LaborEconomics #GenerativeAI #FutureOfWork

56% of women leave the tech industry 10-20 years into their careers - double the rate of men – just at the time when they should (in theory) be moving into leadership positions. We aren’t getting enough women into tech in the first place and we’re not keeping those we do have. Less senior women in tech means less female tech role models, which sends a message to more junior women that they’ll struggle to build a successful career in the industry. If we fail at retention, we’re much more likely to fail at recruitment too – neither are an option. The World Economic Forum predicts that 70% of the next decade's economic value will be driven by digitalisation, but we have a global tech talent shortage that threatens to seriously undermine that growth. An obvious solution would surely be to make the industry more appealing, welcoming, and supportive for the 50% of the population that feel disenfranchised by it. Instead, Miriam Partington reports for Sifted that “bro” culture continues to reign – one respondent from Sifted’s latest women in tech survey wrote “I see no future for myself at all in technology…I am repeatedly burnt out after years of this toxic masculine culture.” It’s a sentiment matched in a LinkedIn post I read earlier this week filled with comments from women in tech choosing, despite loving their work, to “bow out” of an industry they felt was stacked against them. It's 2024 and we are nowhere close to creating an industry where women feel safe, valued and appreciated. It's so frustrating and disappointing. Change must be intentional – it won’t just get better on its own. We have to be intentional about training, hiring, and promoting women in tech, which requires being intentional in creating cultures where women can achieve their full potential. I’d argue that it starts with gender pay equity, which is a priority for me at Kyriba right now. We have to signal support for women from the top – and compensation feels a pretty critical place to start. https://bit.ly/3WRXO1D

This year's most important talk on HR didn't come from a CHRO, Chief People Officer or a highly paid keynote speaker... I was struck by this section of Blaise Metreweli's first speech as Head of MI6 (the British Secret Intelligence Service): "Power itself is becoming more diffuse, more unpredictable, as control over these technologies is shifting from states to corporations and sometimes to individuals." This is not a national security issue alone. It is already playing out inside organisations, and HR sits closer to it than many realise. AI in recruitment, workforce planning, performance management and learning is no longer something that lives neatly within enterprise systems or formal governance. Increasingly, it is: ⋅ used directly by hiring managers ⋅ adopted by recruiters outside approved tools ⋅ accessed by employees to augment their own work, with or without organisational oversight The result is a quiet shift in where decisions are actually being made, and how much human judgment is being applied along the way. Like the technologies Metreweli referenced, AI in HR carries both promise and risk at the same time. The same tools that improve consistency and scale can also harden bias, remove context and obscure accountability if they are poorly understood or weakly governed. The lesson for HR and recruitment leaders is not to resist AI, nor to rush to deploy it. It is to recognise that our role is changing. We are no longer just implementing systems or writing policies. We are being asked to exercise judgment over technologies that distribute power more widely and more subtly than before. If HR does not take that responsibility seriously, others will fill the gap. And they may not be thinking about trust, fairness or long-term consequences. This is the work now: helping organisations use powerful tools without losing sight of human responsibility.

🚨 We’re not just losing women in tech — we’re losing innovation, and future leadership. BILLIONS of £££s. Thanks to my friend Rav Bumbra for highlighting The Lovelace Report —— which launched at the House of Commons by WeAreTechWomen and Oliver Wyman. 💡 Key insights from the report: • 40,000–60,000 women exit UK tech roles every year • 80% of women in tech are currently considering leaving • 90% want to lead, yet only 1 in 4 believe it’s achievable • Over 70% hold additional qualifications, yet only 14% feel they’re progressing • Replacement and retraining alone costs another £1.4–2.2 billion As someone who has dedicated years to making cybersecurity more inclusive, this report lands with weight — but also with clarity. It’s not women who need fixing. It’s the system. This isn’t a pipeline problem. It’s a systemic failure to retain and progress women in tech — which is costing the UK £2–3.5 billion a year. That number is staggering, but it represents more than financial loss — it reflects lost innovation, stalled careers, and cultures that aren’t serving the people they claim to include. The Lovelace Report lays out a clear and urgent blueprint for change. We must: ✅ Redesign career frameworks to be inclusive by default ✅ Tackle structural barriers to progression ✅ Build cultures where women thrive — not just survive 🔗 Read and share the report: https://lnkd.in/es-235TF Let’s ensure our daughters — and every woman entering tech today — finds not just opportunity, but longevity, leadership, and equity. 📢 Please pass this on to your teams, tech leaders, and HR partners. Progress only happens when we act together. #WomenInTech #TheLovelaceReport #InclusiveLeadership #TechForGood #Cybersecurity #RetentionCrisis #EquityInTech #INSecurityMovement #JaneFrankland

This International Women's Day, I want to share data that stopped me cold. Anthropic published research this week measuring which workers face the highest AI displacement risk. The answer is not who anyone expected. The most AI-exposed workers in the US are: → 16 percentage points more likely to be female → 4x more likely to hold a graduate degree → Earning 47% more than the least-exposed group We spent years building the narrative that automation threatens low-wage work. Factory floors. Delivery drivers. Retail cashiers. AI walked straight past them. It sat down at the desk of the woman who spent two decades forcing her way into finance, tech, and analytics. The most exposed jobs? Computer programmers. Financial analysts. Customer service managers. 75% task coverage. Already. Right now. These aren't entry-level roles that women "settled for." These are the careers women fought their way into — often against the odds, often without a roadmap. Here's the part that should make us angry. The job losses aren't visible yet. No unemployment spike. No headlines. Because it's not happening through layoffs. It's happening through closed doors. Young women aged 22–25 entering these fields are seeing hiring rates fall 14% since ChatGPT launched. Not fired. Never offered the job. That is how structural exclusion works. Silently. Before the data is undeniable. Before anyone has to answer for it. On International Women's Day, we celebrate how far women have come. But I want to ask a harder question: Who is protecting where they are now? The same industries that championed gender diversity in tech and finance — are they mapping which female-dominated roles are most AI-exposed? Are they funding transition programmes ahead of this curve, or waiting to react? If we only act once the data screams, we didn't protect progress. We delayed the fall. Infrastructure beats innovation. But infrastructure that ignores equity isn't infrastructure — it's a trap. What is your organisation doing TODAY to protect its most exposed workforce?