Why soft skills matter in the AI age: a parent's advice

AI: Beyond the Fear — Building Skills That Matter Last week, I shared a thought: AI won’t replace people, but people using AI will replace those who don’t. That sparked a lot of conversations, and it made me reflect: what skills really matter if we want to grow with AI instead of fearing it? Here’s what I see as the key areas: 1️⃣ AI & Data Literacy – Learning how to work with AI tools, interpret insights, and ask the right questions. It’s becoming a new business language. 2️⃣ Cloud Computing – Almost every modern system runs on AWS, Azure, or GCP. Understanding this is no longer optional. 3️⃣ Cybersecurity – As we automate and digitize more, protecting data and trust becomes even more critical. 4️⃣ Automation & DevOps – Businesses need speed and reliability. Knowing how to streamline workflows is a huge differentiator. 5️⃣ Critical Thinking & Problem Solving – AI can give answers, but only humans can connect dots, challenge assumptions, and innovate. 6️⃣ Emotional Intelligence & Leadership – Inspiring teams, building trust, and guiding people through change—this is where humans shine. 7️⃣ Sustainability & Green Skills – The future isn’t just digital, it’s sustainable. Green knowledge is becoming a growth area across industries. On a personal note, I’m focusing on strengthening AI & data literacy, exploring AWS AI/ML services, and extending my work in automation & DevOps towards MLOps. These feel like natural next steps to build on my current experience. Because the real question isn’t “Will AI replace me?” It’s “Am I preparing myself to work smarter with AI?” Let’s build, adapt, and lead. #AI #FutureOfWork #Upskilling #LearningAndDevelopment #CareerGrowth

Every conversation I have lately circles back to the same question: what skills are needed in the age of #AI? 🌤️ According to World Economic Forum - Future Jobs Report - 2025, here are some essential skills to thrive in the AI-powered future: 1. Prompt Engineering & Critical Thinking – asking the right questions matters more than ever. 2. Data Literacy – not becoming a data scientist, but being comfortable with insights & patterns. 3. Adaptability – roles will shift; being agile will be a career superpower. 4. Ethics & Judgment – knowing when not to use AI is as important as when to use it. 5. Creativity – pairing human imagination with AI scale. 6. Communication – translating complex tech into business outcomes. 7. Emotional Intelligence – what AI can’t replicate is human connection. 8. Continuous Learning – the tech will never stop evolving. 9. Strategic Foresight – seeing where AI is heading and positioning yourself ahead of the curve. 10. AI Collaboration – treating AI as a co-worker, not a tool. - "Co-Intelligence" by Ethan Mollick! 😊 That’s why I’m excited for #AI World in Las Vegas (Oct 13–16) — a chance to learn, connect, and see these ideas in action. If you’re serious about preparing for what’s next, this is where the conversations are happening #AI #FutureOfWork #SkillsForTheFuture #Leadership #Oracle

"Too many executives are greenlighting projects not because they solve a defined business problem, but because “we need an AI initiative.” - MIT Report The hardest part of GenAI isn't the demo. It's qualifying the right use case. Working with enterprises in US, Europe and India, I've observed that 90% of failed POCs stem from poor discovery. Here's the qualification framework that works better: 1. Problem-First, Not Technology-First (Working backwards in Amazon parlance) "We want to implement AI" isn't a business requirement. Always dig deeper: "What manual process is burning 20+ hours/week?" "Which errors are costing real money?" "Which workflows have clear input/output patterns?" "How big is the problem?" (Quantifying) 2. Data Readiness Assessment A good start could be: "If you needed to train someone to do this task tomorrow, what would you show them?" If they can't answer clearly, their data isn't AI-ready. 3. Success Metrics Definition Define success criteria before touching the tech: 40% time reduction in document processing. 95% accuracy in classification tasks. $200K annual savings in operational costs. 4. Change Management Reality Check "Who will be the biggest skeptic of this solution?" If you have not thought about user adoption, the technical solution does not matter. Companies that nail discovery succeed faster than those rushing to flashy demos. What's your biggest challenge in AI solution qualification? #GenAI #ArtificialIntelligence #PreSales #AITransformation #MachineLearning #EnterpriseAI #AWS #Anthropic #SalesStrategy #TechnologySales #DigitalTransformation #TechLeadership #Innovation #Fortune #MIT

And we're back! One last vacation thought 😉 The new bottleneck in tech isn't technology - it's talent and team structure. Here's why 👇 For the first time, the most advanced tech is available to everyone simultaneously. A startup can use the same foundational AI models as a Fortune 500 company. The cloud provides near-infinite, on-demand compute. The true competitive advantage has shifted from technology access to people and process. 🧠 Skills are the new currency: The shift from "Coder" to "Engineer" to "AI Trainer / Policy Manager" is profound. It’s a move from writing instructions to curating data and guiding intelligence. A formal upskilling program is no longer a perk; it’s a core business strategy. 🚀 Team structure dictates speed: The single biggest driver of velocity is moving from large, project-based teams to small, autonomous, outcome-driven teams. If you change nothing else, changing your team topology to align with the value stream will have the most immediate impact. This leads to the real question: in a world where AI can write code, what is your company's new competitive moat? The answer is Semantics and Governance. 💡 Your Data, Semantically Understood: Your real IP is your proprietary data, but raw data is useless. This means turning unstructured chaos into business intelligence by systematically extracting structured metadata from your documents, images, and conversations. The future moat is a rich, semantic understanding of this data (a Knowledge Graph that allows AI to generate unique insights for your business). It's the one thing competitors can't copy. ⛓️ Your Governance, as an Enabler: Your governance model is your corporate immune system. In the past, it was a slow, bureaucratic function. In the future, it's an intelligent, automated system of guardrails that allows your teams to move incredibly fast, safely. The ability to trust your AI and deploy with confidence is a massive competitive advantage. What I've created here isn't just a comparison table; it's a map. The task now for every leader is to use it to find where you are, decide where you need to go, and invest in the teams and systems that will get you there. What do you think is the biggest bottleneck in organizations today? #TechLeadership #DigitalTransformation #AI #DataStrategy #FutureOfWork #Metadata #TeamTopology

🚀 Elon Musk just launched Macrohard. Yes, that’s the actual name. And yes—it’s aimed straight at Microsoft. Here’s what’s inside this week’s One More Thing in AI: 1️⃣ Macrohard vs Microsoft Musk’s AI-powered software agents are gunning for Word, Excel, and PowerPoint. Backed by his Colossus supercomputer. 2️⃣ How to Join the 5% Winning with AI (MIT research) Why most companies fail at AI—and what the disciplined few do differently. 3️⃣ Better GPT-5 Prompts Think of GPT-5 as an operating system for building. Learn how to steer it for workflows, coding, and prototypes. 4️⃣ AI’s Hidden Security Nightmare The “lethal trifecta” already causing data leaks at Microsoft, GitHub, and Google—and what founders must do now. 👉 Read the full edition here: https://lnkd.in/dTNKGETd The AI wars are heating up. And Musk just raised the stakes. #ai #elon #gpt5

Feeling bombarded by AI news? Wondering what actually matters for your business? 🤔 It's September 10, 2025, and here at Aidgentic we're putting together a daily roundup of breaking news and signals that actually matter for founders today: ➡️ Oracle's stock soared due to increased demand for its AI cloud services, as businesses scramble to secure the computing power they need for AI. This highlights the growing importance of AI infrastructure. (https://lnkd.in/eiej-BbK) ➡️ Aston Martin and Cognizant are exploring how AI can boost performance, from car design to marketing. They're even using AI to create personalized messages from drivers in 35 languages! This shows how AI can be used across all parts of a business. (https://lnkd.in/enCdNVwe) ➡️ College students are changing their majors to prepare for an AI-powered future. Some are even adding AI certificates to their studies. This shows that the next generation of workers understands the importance of AI skills. (https://lnkd.in/eF_hNtV8) The Aidgentic Takeaway: The lesson for founders is that AI is not just a tech trend. It's a fundamental shift in how we work and live. Use this to your advantage! Find ways to use AI to improve your skills and empower your team. This is how you build a moat against big tech and create a thriving business in the age of AI. 🚀

In today’s rapidly evolving technological landscape, Generative AI (GenAI) is reshaping industries, enabling unprecedented innovation and automation. Whether you’re a data scientist, AI enthusiast, or business leader, understanding the critical stages involved in developing powerful GenAI solutions is essential for success. This insightful infographic breaks down 12 key stages that guide the entire GenAI solution development process—from Problem Definition, through Data Collection & Preprocessing, Model Selection, Training & Fine-Tuning, all the way to Scaling & Expansion. Each step plays a vital role in ensuring the solution is robust, ethical, efficient, and aligned with business objectives. Some key highlights include: ✔️ The importance of Prompt Engineering for guiding model outputs effectively. ✔️ Conducting thorough Ethical and Bias Reviews to promote responsible AI use. ✔️ Implementing continuous Monitoring & Feedback Loops to enhance performance in real time. ✔️ Leveraging Optimization & Compression Techniques to improve efficiency. 💡 Whether you’re developing an AI-powered product, optimizing business processes, or making strategic decisions, this framework provides a comprehensive roadmap to help you stay ahead of the curve. Let’s embrace the future of AI, build solutions that matter, and drive meaningful impact! 🌍 #ArtificialIntelligence #GenAI #MachineLearning #DataScience #DeepLearning #AIInnovation #DigitalTransformation #TechLeadership #CareerSwitch #Microsoft #IBM #FutureOfWork #AIProjects #BusinessIntelligence #ResponsibleAI #PromptEngineering #ContinuousImprovement #TechTrends #AIForGood

💰 MLOps is Not a Cost Center — It’s a Growth Enabler Many organizations hesitate to invest in MLOps because they see it as “extra engineering overhead.” But the reality is the opposite: ⚡ Without MLOps: • Models stay stuck in notebooks. • Experiments never reach production. • ROI from AI projects stays at 0%. ⚡ With MLOps: • Time-to-market for ML solutions shrinks dramatically. • AI projects scale beyond PoCs into production-grade systems. • Models adapt faster to new data → better business outcomes. 📊 The business impact: MLOps isn’t about building fancier pipelines. It’s about turning AI into measurable value—from customer personalization to fraud detection to operational efficiency. 💡 Takeaway: MLOps isn’t “nice to have.” It’s the bridge between AI investment and AI impact. 👉 Question: Does your organization measure the ROI of MLOps, or is it still treated as a technical checkbox? #MLOps #AI #MachineLearning #DevOps #DataOps #BusinessValue #AIEngineering #ArtificialIntelligence #DigitalTransformation #CloudComputing #FutureOfAI #Leadership

🚀 3 Ways AI Will Transform Technical Support in the Next 5 Years As a Technical Architect with a passion for cloud systems and support automation, I’ve been exploring how Artificial Intelligence—especially through my academic journey—can reshape the future of enterprise support. Here are three game-changing shifts I see coming: 1️⃣ Hyper-Automation of Support Workflows AI will streamline ticket classification, routing, and resolution using NLP and predictive analytics. Think faster responses, smarter triage, and bots that actually understand context. 🔗 Forbes Technology Council highlights how AI tools will empower professionals to dive into use cases more swiftly. 2️⃣ Smarter Knowledge Management Machine learning will surface relevant documentation instantly, analyze past tickets for patterns, and continuously update knowledge bases—making support teams more agile and consistent. 🔗 Metapress outlines how AI learns from every issue to improve support accuracy and speed. 3️⃣ Personalized, Predictive Support Experiences From proactive maintenance alerts to sentiment-aware escalation paths, AI will help support teams anticipate needs and tailor solutions for every user. 🔗 Forbes predicts over 40% of core IT spending in Global 2000 firms will go toward AI initiatives by 2025. Inspired by insights from Forbes and Metapress2—and my own journey into AI through academic training. I’m excited to keep learning and applying these concepts to real-world challenges. If you're working on AI in support or cloud architecture, I’d love to connect and exchange ideas! #AI #TechnicalSupport #CloudArchitecture #Automation #MachineLearning #EnterpriseTech #DigitalTransformation