The Importance of Capacity Building in AI Development

McKinsey & Company’s “Singapore’s bold bets” piece is a good reminder: in the AI era, the real bet isn’t just on the tech — it is on people. Skills don’t last forever. In fact, their half-life is just a few years. That means constant reskilling and adaptability aren’t “nice to have” — they’re survival. We’ve seen this first-hand. AI programs stall when people aren’t equipped to use the tools. And the research backs it up: employees with AI literacy are nearly twice as likely to trust and adopt AI. Without trust, systems don’t scale. Without capability, transformation doesn’t stick. That’s why I see skilling as infrastructure, not an HR side project. At SMBC Group, our dual-engine approach reflects this: - Internally, modernising decision systems means embedding learning and capability-building at scale. - Externally, our exploration of agentic AI systems is about making technology human-readable and usable so more people across the enterprise can shape how work evolves   Singapore’s focus on people-first AI, bold governance, and talent investment makes it the right place to anchor this vision. The future won’t just be AI-enabled. It’ll be AI-ready. And readiness starts with people. #AgenticAI #FutureOfWork #Skilling #Singapore #AITransformation

Training ≠ Development. Especially if you’re trying to get your team AI ready. A lot of organizations are still treating AI readiness like a training issue only. Teach people the tool. Show them the prompt. Run the workshop. Check the box. But AI readiness is bigger than that. Training helps people do today’s job better. Development prepares people for how their role, judgment, decision making, and value will evolve tomorrow. If we only train people on AI tools, we may improve short term usage. If we develop people for an AI-enabled workplace, we build adaptability, critical thinking, confidence, and future readiness. That means the real conversation is not just: “Did we train them on AI?” It’s also: “Are we developing people to work differently because of AI?” “Are managers ready to lead in an AI-enabled environment?” “Are teams being equipped to rethink workflows, not just learn a tool?” That is where development comes in. Because the companies that do this well will not just have employees who can use AI. They will have people who can think with it, work with it, and grow with it. And that difference matters. Training supports adoption. Development supports transformation. How is your organization thinking about this right now: as a training need, a development opportunity, or both? ——— ✦ ——— 🌱 More on AI + Workforce Development → Janet Perez

Based on my conversations with CIOs and Business Leaders I am realizing that durable competitive advantage will not come from chasing the latest AI model or the most visible pilots. lt will emerge from building the foundations — data architecture, governance, workforce capability, and value-aligned operating models — that allow AI to compound over time. The technology is nearly a commodity. The organizational capacity to deploy it safely, measure it rigorously, and scale it strategically is not. That capacity is the real moat...!

The latest World Economic Forum “Chief People Officers Outlook” (Sept 2025) makes one thing clear — AI development must go hand in hand with people development. Among the top opportunities for AI in the next 6–12 months, the WEF highlights career development and upskilling. Among the top risks - employees not adapting fast enough and career stagnation from over-reliance on AI. There’s no contradiction here — it’s the same message from both sides: 🔹 AI transformation succeeds only when people grow alongside it. 🔹 Learning across silos, beyond our current roles, is no longer optional. And let’s be clear — upskilling doesn’t mean handing your work to AI. It means developing the skills to use AI wisely and meaningfully: 1️⃣ Learning agility — understanding where AI fits, applying judgment, and thinking critically about its output rather than accepting it blindly. 2️⃣ Relational intelligence — connecting across teams and functions to share insights, spot patterns, and identify where AI can truly add value. 3️⃣ Strategic problem solving — growing beyond routine tasks to tackle the creative, analytical, and human challenges that AI can’t. In short: upskilling isn’t about replacing effort with AI — it’s about expanding capability through it. How are you helping your teams build these capabilities today? #AI #Upskilling #FutureOfWork #Leadership #CareerDevelopment #LearningCulture #PeopleStrategy Full WEF report here: https://lnkd.in/ejkG_Cnf

🚀 Building AI-Ready Public Administrations: More Than Just Digital Skills As governments embrace AI to improve public services, one question looms large: Do our public institutions have the competencies to do this responsibly, effectively, and inclusively? From my work across 40+ countries, I’ve learned that AI readiness isn’t just about adopting new tools—it’s about building an ecosystem of skills across four key areas: 1️⃣ Policy & Legal – Understanding AI’s impact on rights, accountability, and governance, and embedding safeguards from the start. 2️⃣ Technical – Developing in-house expertise in model evaluation, data governance, and security to avoid vendor lock-in and ensure technical sovereignty. 3️⃣ Leadership – Cultivating strategic foresight, digital literacy, and change management so leaders align AI use with public value—not just efficiency. 4️⃣ Cross-Functional – Empowering AI governance advisors, algorithm auditors, data protection officers, and transformation leads to bridge policy, tech, and ethics. How to get there? Role-specific training that’s practical and embedded into career paths Reskilling programs to adapt to automation and prevent inequality Cross-sector collaboration with academia, private sector, and civil society Governance tools like AI registries, procurement standards, and regulatory sandboxes Promising examples already exist—from Ukraine’s CDTO Campus to Singapore’s SkillsFuture and North Macedonia’s locally trained VezilkaLLM. If we invest in the right capabilities today, we can build smarter systems and smarter institutions—ones that harness AI’s potential while upholding trust, transparency, and accountability. 💬 I’d love to hear: What’s one competency you think is most overlooked in preparing public administrations for AI? https://lnkd.in/gdsyavKr #AI #PublicAdministration #DigitalTransformation #AIReadiness #Governance #CapacityBuilding #Leadership #PublicSectorInnovation Bojana Bajić Igor Todoroski Prateek Sibal Gustavo Fonseca Ribeiro Ravi Gupta Nikola Neftenov Angel Draev

Governments everywhere are debating “AI sovereignty.” Build domestic models. Reduce reliance on foreign platforms. Regulate risk. All important. But none of it guarantees that AI will serve the public. In our latest #GlobalAIWatch post, Luca Cominassi and I reflect on lessons from the InnovateUS workshop on regulating algorithms — from Spain’s ALIA initiative to the EU AI Act and Italy’s national AI law. The throughline is that responsible AI adoption depends not only on regulation or technology alone, but on institutions capable of governing both. AI systems are increasingly mediating access to public benefits, legal rights, and essential services. That means accountability cannot sit with vendors. It must remain clearly located within public institutions. Ownership matters. Regulation matters. But capacity — the people, skills, authority, and oversight mechanisms within government — is what ultimately determines whether AI strengthens or weakens democracy. 📕 Read the key takeaways: https://lnkd.in/ecCinjTZ 📺 Watch the workshop with Mihir Kshirsagar: https://lnkd.in/eKGwteta Metagov, OECD.AI, Princeton Center for Information Technology Policy

AI Literacy or AI Elitism? The Hidden Divide in Workforce Readiness The OECD - OCDE report "Bridging the AI Skills Gap: Is Training Keeping Up?" (2025) investigates whether current training systems are adequately equipping workers with the skills needed for an AI-driven economy. It documents experiences from 21 OECD countries, including detailed analyses of training policies and the actual content of course catalogues in Australia, Germany, Singapore, and the United States. The report finds that although most countries are increasingly promoting upskilling and reskilling for AI, only a tiny share of courses (between 0.3% and 5.5% in the four sample countries) cover AI content, with a disproportionate focus on advanced skills rather than general AI literacy for the wider workforce. #KeyProblems identified include insufficient supply of AI training, over-reliance on financial incentives that are not always targeted to AI skills, and a lack of flexible, inclusive pathways—especially for vulnerable or less-skilled workers. Experiences from various countries reveal that while some are pioneering targeted AI literacy campaigns (e.g., Austria’s “Digital Everywhere” and Hungary’s gamified AI challenge), the majority remain focused on producing AI professionals. #Recommendations #for #policymakers are to expand both general and advanced AI training, better target incentives, increase non-financial support (like career guidance and public-private collaborations), develop more accessible and flexible training formats, lower entry barriers for participants, and embed AI skill-building within broader workforce development strategies. These steps are crucial to ensuring a people-centred, inclusive transition as AI reshapes the world of work. Source: https://lnkd.in/eMTMPQSy