Navigating AI Transformation

AI holds great potential for the semiconductor industry and will kick-start the next round of innovation for faster, cheaper and more energy-efficient computation – that was my message today at SPIE Advanced Lithography + Patterning. I discussed the potential and the challenges that AI holds for our industry.   The potential is clearly huge. AI is rapidly integrated into applications, and high-performance compute is expected to underpin growth towards $1 trillion of semiconductor sales by 2030. The challenges are around the computing needs of AI models and related energy consumption. The compute workload of training a leading AI model has increased 16x every 2 years in recent years – much faster than the increase in computing power delivered by Moore’s law, which is about 2x every 2 years. The energy needed to train a leading model has not grown so steeply but still rose 10x every 2 years. This computing need has been met by building supercomputers and massive data centers. If you extrapolate these trends, training a leading AI model would need the entire world-wide electricity supply in about 10 years. That’s clearly not realistic, so the trend has to break, by training algorithms becoming more efficient and by chips becoming more efficient. In other words, the needs of AI will stimulate immense innovation in chip design and manufacturing – and the potential value of AI to our society will put urgency and funding behind that drive. As a consequence, chip makers are pulling all levers to accelerate semiconductor scaling. This includes lithographic “2D” scaling: shrinking the dimensions of transistors to pack more into a square millimeter. It will also include “3D” integration, with innovations like backside power delivery, transistor designs like gate-all-around, as well as stacking chips in the package, where holistic lithography will play a critical role to deliver performance requirements. ASML will support these trends through a comprehensive, holistic lithography portfolio. Our 0.33 NA/0.55 NA EUV lithography systems allow chip makers to shrink dimensions at the lowest possible cost on their critical layers, while tightly matched and highly productive DUV systems will continue to reduce cost. More than ever, metrology and inspections tools – whose data is fed into lithography control solutions that keep the patterning process operating within tight specs to deliver the highest possible production yields – will be essential to deliver 2D scaling and 3D integration processes. 3D integration requires wafer-to-wafer bonding, and we have demonstrated the capability to map the stresses and distortions that bonding creates and to compensate for them, reducing overlay errors for post-bonding patterning by 10x or more.   It was a pleasure catching up with the industry’s lithography and patterning experts in San Jose. I’m excited to see our collective innovation power having a go at these challenges. Together, we will push technology forward.

Adopting AI tools is easy. Reimagining how we work with them is the real transformation. Across many organizations, teams are being asked to “adopt AI” without the time, training or clarity they need to feel confident. When that happens, progress becomes fragmented—some people race ahead, others hesitate, and morale drops under the weight of confusion. Real AI transformation requires more than deploying technology. It demands deeper shifts that help people work differently and unlock value: → Change management to guide teams through new ways of working → Skilling to empower every employee to thrive in an AI-powered environment → Process understanding to ensure AI augments what matters most → Technology that’s usable, ethical and aligned with business goals As this Forbes article shares, the organizations that succeed will be the ones that treat AI adoption as a human journey, not just a technical one. When teams feel equipped, supported and included in shaping the path forward, that’s when AI truly delivers. What support are you giving your teams to learn and experiment with AI? https://lnkd.in/g2pXBtjm

AI Product Management vs AI for Product Management: Hacks and resources for you. Regardless the path you're on, you need to evolve your PM Craft. 'Evolve' being the keyword here. 𝗙𝗼𝗿 𝗔𝗜 𝗣𝗿𝗼𝗱𝘂𝗰𝘁 𝗠𝗮𝗻𝗮𝗴𝗲𝗺𝗲𝗻𝘁 (This is for the PMs working directly with AI products) – think Research PMs, Recommendations PMs, Platform PMs, and so on. You really need to get good at handling AI's unique quirks: ✨ The Probabilistic nature of AI: It's not always 0 or 1, and you've got to navigate that uncertainty. ✨ The Deep dependency on good quality data: Garbage in, garbage out. You're constantly thinking about data quality. ✨ Developing deep AI awareness: This is key but it's not about you getting too deep into technical concepts you won't need. My secret hack is to make it a habit to read research blogs from big tech companies. Google AI, Meta AI, OpenAI and attending technical conferences. Here are some: -Google AI Blog: https://ai.google/ -DeepMind's blog https://lnkd.in/g3mi8Xxy -Meta AI Blog: https://ai.meta.com/blog/ -OpenAI Research Blog: https://lnkd.in/gR_kPSkt -Microsoft AI Blog: https://lnkd.in/gYkW63yz -Amazon Science Blog: https://lnkd.in/gMJzQrGG You'll literally see what's going to be the next big product in the next two years. The original Transformers paper came out in 2017 – a PM on top of their craft could have foreseen Generative AI tools coming years ago. 𝗙𝗼𝗿 𝗔𝗜 𝗳𝗼𝗿 𝗣𝗿𝗼𝗱𝘂𝗰𝘁 𝗠𝗮𝗻𝗮𝗴𝗲𝗺𝗲𝗻𝘁 ✨ This is about leveraging AI tools to have more impact as a PM, no matter what sector you're in. It's all about adjusting your work style and experimenting to see what actually works for you. My hack here is simple but effective: train your brain to try new things. I block my calendar for 2-hour "experimentation slots." During that time, I'm creating my own tutorials, trying out new AI tools on my actual work, and following the right people. You know most of the tools by now, here are some that you might want to check out: -NotebookLM: new features getting added very often -ChatPRD: https://www.chatprd.ai/ -Productboard AI: https://lnkd.in/gm2mfeDY -ProdPad CoPilot: https://lnkd.in/gWrZZd7W -Quantilope: https://lnkd.in/g3TUJ_-9 -Dovetail: https://dovetail.com/ -Notion AI: https://lnkd.in/gfUb8yKg -Mixpanel: https://mixpanel.com/ Regardless of your seniority, being hands-on and experimenting with these tools goes a long way.

2026 will not reward organisations that experiment endlessly with technology. The next phase of transformation is not about more AI, but about better decisions at scale. As we look ahead, five shifts stand out: ✅ 𝗔𝗴𝗲𝗻𝘁𝗶𝗰 𝗔𝗜 𝗺𝗼𝘃𝗲𝘀 𝗶𝗻𝘁𝗼 𝗰𝗼𝗿𝗲 𝘄𝗼𝗿𝗸𝗳𝗹𝗼𝘄𝘀: Agentic AI shifts decisively from experimentation to execution. These systems plan, coordinate, and act across workflows, with humans setting direction and accountability. The impact is clearest in complex, exception-driven processes where traditional automation falls short. This shift is already delivering value. According to SAP’s Value of AI study with Oxford Economics, organisations expect an average 7% ROI (~US$2.8 million) from agentic AI over the next two years, with 85% seeing moderate to high potential to transform operations. ✅ 𝗖𝘂𝘀𝘁𝗼𝗺𝗲𝗿-𝘀𝗽𝗲𝗰𝗶𝗳𝗶𝗰 𝗔𝗜 𝗯𝗲𝗰𝗼𝗺𝗲𝘀 𝘁𝗵𝗲 𝗱𝗲𝗳𝗮𝘂𝗹𝘁: The strongest AI outcomes come from intelligence that understands an enterprise from the inside out i.e. its data, processes, policies, and decision patterns. This contextual grounding enables AI to influence core business decisions and strategic planning, a shift nearly half of enterprises expect to see in the near term. ✅ 𝗜𝗻𝘁𝗲𝗿𝗼𝗽𝗲𝗿𝗮𝗯𝗶𝗹𝗶𝘁𝘆 𝗯𝗲𝗰𝗼𝗺𝗲𝘀 𝘁𝗵𝗲 𝗯𝗮𝗰𝗸𝗯𝗼𝗻𝗲 𝗼𝗳 𝗲𝗻𝘁𝗲𝗿𝗽𝗿𝗶𝘀𝗲 𝗶𝗻𝘁𝗲𝗹𝗹𝗶𝗴𝗲𝗻𝗰𝗲: As AI becomes more autonomous, fragmented data landscapes quickly become the biggest constraint. Enterprises are prioritising interoperability across systems and environments so context flows seamlessly. Infrastructure is increasingly judged not by scale, but by its ability to support insight, coordination, and informed decision-making as AI moves into end-to-end process orchestration. ✅ 𝗦𝗸𝗶𝗹𝗹𝘀 𝗯𝗲𝗰𝗼𝗺𝗲 𝘁𝗵𝗲 𝗿𝗲𝗮𝗹 𝗱𝗶𝗳𝗳𝗲𝗿𝗲𝗻𝘁𝗶𝗮𝘁𝗼𝗿: As AI takes on more analytical and operational load, the value of human capability rises. Demand is growing for talent that blends domain expertise, data fluency, and AI understanding. Human roles are shifting toward judgment, creativity, oversight, and ethics. AI literacy is becoming essential across functions. Organisations that invest equally in people and technology are best positioned to translate intelligent systems into sustained business value. ✅ 𝗣𝗲𝗿𝗳𝗼𝗿𝗺𝗮𝗻𝗰𝗲 𝗿𝗲𝗽𝗹𝗮𝗰𝗲𝘀 𝗽𝗶𝗹𝗼𝘁𝘀 𝗮𝘀 𝘁𝗵𝗲 𝗺𝗲𝗮𝘀𝘂𝗿𝗲 𝗼𝗳 𝘀𝘂𝗰𝗰𝗲𝘀𝘀: AI maturity in 2026 is defined by outcomes, not experimentation. Enterprises are evaluating intelligence based on its ability to improve efficiency, resilience, decision quality, and customer experience. A strong majority expect AI to become central to business processes and decision-making by 2030. In 2026, adoption at scale not pilots becomes the true benchmark of success. The businesses that lead in 2026 will place intelligence where it matters most, design systems for trust, and apply technology with discipline and intent. That is how AI moves from promise to sustained performance.

Thoughts on how to build an AI-First Company (What the Best Are Actually Doing) Forget the hype. Here’s what separates AI-first companies from the rest and what you can learn from those operating at the edge of innovation: 1. Culture > Model The best AI orgs don’t wait for “alignment” or “roadmaps” to move. They empower builders at the edge. Execution beats planning. Meritocracy beats hierarchy. If someone has a good idea and can ship it, they win. 2. Small Teams, Big Impact Game-changing AI products are being built by teams of 10–15 people, not 1,000. Engineers, researchers, PMs, and GTM sit together, ship fast, and iterate in public. Org design is not about scale. It’s about speed. 3. Slack Is the Org Chart High-agency teams self-organize in real-time. Email is dead. Planning cycles are short. Communication is open by default. You either adapt or drown in noise. 4. Code Wins There’s no central committee telling you what’s allowed. If your team builds it and it works, it ships. Expect duplication. Expect mess. But expect momentum. 5. Safety Is a Feature Trust is the product. Great AI orgs bake in safety from the start; not as a compliance checkbox, but as product design. They focus on real risks: abuse, bias, misuse, prompt injection. Ignore this, and you’ll burn the brand. 6. Think Distribution, Not Just Models The biggest breakthroughs often come from how AI is surfaced to users; not how it’s trained. Sidebar placement, async workflows, and fast onboarding drive more value than 50B extra parameters. 7. Vibes Matter Yes, usage metrics matter. But so do narrative, community, and perception. The best orgs listen to Twitter, Reddit, and Discord as closely as their dashboards. Being AI-first means being user-first. Being an AI-first company isn’t about having the best model. It’s about having the right instincts: move fast, empower the edge, ship what works, build trust, and never stop learning. If you're still waiting for the perfect roadmap, you're already behind.

By 2030, 70% of the skills used in most jobs will completely change. Here's how top companies are preparing for the AI revolution (while others fall behind): 94% of companies with negative AI ROI invested less than 10% of their IT budget. Meanwhile, 71% of positive ROI cases came from organizations investing more than 10%. The message is clear: Half measures don't work. The biggest roadblocks companies face: • 51% struggle with governance & compliance • 47% worry about data security • 43% fear privacy issues • 41% lack AI expertise But there's a blueprint emerging from companies succeeding with AI agents. They all follow these 4 critical steps: 1. Establish a centralized AI hub • Cross-functional teams • Standardized processes • Knowledge sharing systems • Organizations with this see 37% higher success rates 2. Implement robust governance • Risk assessment protocols • Compliance monitoring • Clear accountability • Companies with strong governance are 2.5x more likely to report significant value 3. Commit to continuous learning • Regular model updates • Performance monitoring • Strong feedback loops • This leads to 42% improvement in AI model performance 4. Focus on human-AI collaboration • Comprehensive training • Role redefinition • Trust-building initiatives • Results: 26% higher productivity, 33% better employee satisfaction But here's what most miss: The future isn't just about having AI agents. It's about orchestrating thousands of them across your organization. In 3-5 years, you'll need: • Governance frameworks • Compliance systems • Retirement protocols • Control planes The companies that win won't just use AI as a tool. They'll become "agent native companies" where AI is an integral part of the workforce. The transformation is happening now. Will you lead it or follow? Follow me for more insights on building the future of work. 🚀 #AI #Leadership #FutureOfWork #Innovation

I asked the smartest people I know about AI... I’ve been reading everything I can get my hands on. Talking to AI founders, skeptics, operators, and dreamers. And having some very real conversations with people who’ve looked me in the eye and said: “This isn’t just a tool shift. It’s a leadership reckoning.” Oh boy. Another one eh? Alright. I get it. My job isn’t just to understand disruption. It’s to humanize it. Translate it. And make sure my teams are ready to grow through it and not get left behind. So I asked one of my most fav CEOs, turned investor - a sharp, no-BS mentor what he would do if he were running a company today. He didn’t flinch. He gave me a crisp, practical, people-centered roadmap. “Here’s how I’d lead AI transformation. Not someday. Now.” I’ve taken his words, built on them, and I’m sharing my approach here, not as a finished product, but as a living, evolving plan I’m adopting and sharing openly to refine with others. This plan I believe builds capability, confidence, and real business value: 1A. Educate the Top. Relentlessly. Every senior leader must go through an intensive AI bootcamp. No one gets to opt out. We can’t lead what we don’t understand. 1B. Catalog the problems worth solving. While leaders are learning, our best thinkers start documenting real challenges across the business. No shiny object chasing, just a working list of problems we need better answers for. 2. Find the right use cases. Map AI tools to real problems. Look for ways to increase efficiency, unlock growth, or reduce cost. And most importantly: communicate with optimism. AI isn’t replacing people, it’s teammate technology. Say that. Show that. 3. Build an AI Helpdesk. Recruit internal power users and curious learners to be your “AI Coaches.” Not just IT support - change agents. Make it peer-led and momentum-driven. 4. Choose projects with intention. We need quick wins to build energy and belief. But you need bigger bets that push the org forward. Balance short-term sprints with long-term missions. 5. Vet your tools like strategic hires. The AI landscape is noisy. Don’t just chase features. Choose partners who will evolve with you. Look for flexibility, reliability, and strong values alignment. 6. Build the ethics framework early. AI must come with governance. Be transparent. Be intentional. Put people at the center of every decision. 7. Reward experimentation. This is the messy middle. People will break things. Celebrate the ones who try. Make failing forward part of your culture DNA. 8. Scale with purpose. Don’t just track usage. Track value. Where are you saving time? Where is productivity up? Where is human potential being unlocked? This is not another one-and-done checklist. Its my AI compass. Because AI transformation isn’t just about tech adoption. It’s about trust, learning, transparency, and bringing your people with you. Help me make this plan better? What else should I be thinking about?

Over the past 10 weeks, I’ve interviewed 35 talent and learning leaders at Fortune 1000 companies for a report I’ll be releasing this fall. One of my favorite questions has been the very first one: 𝐖𝐡𝐚𝐭 𝐚𝐫𝐞 𝐲𝐨𝐮𝐫 𝐭𝐨𝐩 𝐭𝐡𝐫𝐞𝐞 𝐩𝐫𝐢𝐨𝐫𝐢𝐭𝐢𝐞𝐬 𝐫𝐢𝐠𝐡𝐭 𝐧𝐨𝐰?” With 105 priorities and counting, the responses vary widely given differences in industry, scope, and role (VP of Learning, talent, talent management, leadership development) but here is a slice of what has been shared so far: ➡️ AI and work transformation: Clarify what AI means for the workforce, its implications for roles, and how teams can adopt it to accelerate development and efficiency. ➡️ AI Coaching Pilot: Launch an AI-powered coaching pilot program across the organization to scale leadership development support. ➡️ Generative AI Upskilling: Upskill employees and leaders to effectively use generative AI in day-to-day work ➡️ Future of Work & Workforce Planning: Prepare for disruptions to job architecture by integrating human and digital workforces. Rethink responsibilities, structures, and collaboration models. ➡️ Change management: Embed change management capabilities at all levels, particularly around AI adoption. ➡️ New leadership Behaviors: Equip leaders with new capabilities to thrive in a changing environment, including adaptability, resilience, and the ability to lead in an AI-augmented workplace. ➡️ Skills and Career Paths - Creating paths by prioritized skills in our organization ➡️ Rethinking the Function: Redesign the talent and learning function to reflect disruption caused by AI ➡️ Change Leadership: Navigate a period of executive turnover and transition by stabilizing the leadership team, clarifying roles, and building confidence with functional business leaders. ➡️ Facilitating Connection: Partnering with our employee experience and workplace teams to use in-office team days for learning and connection ➡️ Linking Performance and Development: Redesign performance processes to connect directly to development, helping employees understand what growth means in practical and tangible terms. ➡️ Manager Development: Continue to strengthen manager capability and resources, ensuring managers are equipped to drive performance and support employee development ➡️ VP and SVP Development: Support and accelerate the growth of new vice presidents and senior vice presidents as they step into expanded leadership roles. ➡️ Building a Leadership Bench : Develop and execute a strategy for strengthening the leadership bench, with a focus on preparing our Top 200 leaders ➡️ AI/Learning : Using AI internally within the learning function and focusing on key skills in AI for client-facing practitioners ➡️ Academies For AI/Data Roles: Developing and rolling out an academy for our AI & Data Product Employees I’d love to hear your perspective: What stands out most to you about this list, or what themes are you seeing in this list?

I see many people struggling or confused when switching into AI. Don’t jump straight into frameworks like LangChain or LangGraph. Frameworks are accelerators, not starting points. Without foundations, you’ll end up building fragile demos instead of production-grade systems. Here’s a step-by-step path to transition your career into Generative AI: 1. Build Core Foundations --Python (APIs, JSON, virtual envs, packaging) --Git, Docker, Linux basics --Databases: Postgres + pgvector, or FAISS for embeddings 2. Learn Just Enough Math & Data --Vectors, cosine similarity, probability --Tokenization, chunking, normalization 3. Understand LLM Basics --How transformers work at a high level --Different types of models: base vs. instruct, hosted vs. local --Prompt engineering patterns (instruction, few-shot, tool-use) 4. Get Hands-on with RAG (without frameworks first) --Ingest → chunk → embed → store → retrieve → re-rank → generate --Add logging, caching, retries --Evaluate outputs with ground-truth sets 5. Learn Evaluation & Safety --Handle hallucination, PII, toxicity --Define and track metrics (accuracy, latency, cost) 6. Explore Reliability & MLOps --CI/CD for prompts/config --Observability, tracing, cost dashboards --Error handling and fallbacks 7. Then Explore Agents --Start simple: one-tool agents --Add planning and memory only when metrics prove value 8. Finally → Use Frameworks Wisely --Adopt LangChain, LangGraph, or LlamaIndex as orchestration layers --Keep your core logic framework-agnostic 9. Showcase Projects --Document QA system with metrics --Structured extraction pipeline with redaction --A small but reliable agent automating a real workflow 10. Be Interview-Ready --Explain RAG pipelines on a whiteboard --Compare models and providers --Justify design choices (chunking, caching, re-ranking) Learn the primitives first. Frameworks make you faster after you understand what’s under the hood. That’s how you build systems that last.

McKinsey has 40,000 employees and 25,000 AI agents. Now it is adjusting remuneration to AI. An entire industry is being disrupted by AI. And it is not the only one. Less than 2 years ago McKinsey had just 3,000 AI agents. Its CEO originally expected to reach one AI agent per employee by 2030. Now it might be months away. 𝗕𝘂𝘁 𝘄𝗵𝗮𝘁 𝗱𝗼 𝗮𝗴𝗲𝗻𝘁𝘀 𝗱𝗼 𝗶𝗻 𝗰𝗼𝗻𝘀𝘂𝗹𝘁𝗶𝗻𝗴? • Consulting is full of work that is structured, repeatable, research-heavy, and analysis-driven. Exactly the type AI can replace. • Agents can help consultants search internal knowledge, summarize documents, compare markets, draft first versions, structure analyses, test hypotheses, build models, prepare client materials, and accelerate the kind of linear problem-solving that used to consume large amounts of junior consultant time. This does not mean McKinsey no longer needs consultants. It means consulting is changing. If AI can produce the first draft, the benchmark, the synthesis, the model, or the analysis, humans have to become better at the parts AI cannot reliably do: • setting the right ambition • applying judgment • challenging answers • managing the client • connecting politics with strategy • turning analysis into decisions This is much bigger than automation. Consulting firms are now redesigning the economics of consulting around a new execution layer. 𝗟𝗲𝘁’𝘀 𝘁𝗮𝗸𝗲 𝗼𝗻𝗲 𝘀𝘁𝗲𝗽 𝗯𝗮𝗰𝗸. For decades, the consulting model was built around senior partners selling the work, large teams delivering it, and clients paying for expertise, time, and execution capacity. If now AI agents are doing an increasing part of this work, clients will ask why they should pay the same way for work that now takes less human effort. That means consulting firms need to adjust their business model: from selling hours and advice to selling outcomes. Savings, cost reduction, productivity improvement, revenue increase, real transformation. 𝗧𝗵𝗶𝘀 𝗶𝘀 𝘄𝗵𝗮𝘁 𝗠𝗰𝗞𝗶𝗻𝘀𝗲𝘆 𝗶𝘀 𝗰𝗵𝗮𝗻𝗴𝗶𝗻𝗴 𝗻𝗼𝘄: Partners will receive a smaller share of profits in cash and a larger share in equity. In practice, part of the money that would have been paid out immediately stays inside the firm. 𝗪𝗵𝘆? • Because consulting cash flows may become more volatile. If more projects are tied to savings or performance improvements, the firm may only get fully paid once the client actually delivers the result. • McKinsey needs more capital inside the business: to absorb delayed payments, take more outcome risk, and invest in the technology needed to deliver work differently. Consulting companies are adopting 𝗼𝘂𝘁𝗰𝗼𝗺𝗲-𝗯𝗮𝘀𝗲𝗱 𝗽𝗿𝗶𝗰𝗶𝗻𝗴. Any industry built on expensive expert work, repeatable analysis, and billable hours will face the same pressure: to move from selling activity to selling outcomes. Opinions: my own, Graphic source: CB Insights Subscribe to my newsletter: https://lnkd.in/dkqhnxdg