Dirk .

Throwback Friday: "Technology is easy. People are hard." That closing line from Peter Van Laer’s keynote at CEO Summit 2025, last Friday, stuck with me. AI is not the goal; it is a lever. The real test is how leaders and organizations choose to adopt it. Technology accelerates. Culture, ethics and collaboration decide whether we are truly future-proof. It was inspiring to hear how BDO Belgium is becoming an AI-enabled organization, step by step, with people at the center. At Akkodis Benelux we share that mindset: scale AI from pilots to production while building trust, adaptability and human connection. Three mindsets leaders need now: • Soulful thinking: anchor choices in purpose and impact. • Critical thinking: separate signal from noise and challenge assumptions. • Integrative thinking: connect disciplines to turn scattered insights into results. Thank you Jan De Schepper, for another excellent edition! So, how are you making AI work for people in your organization? #CEOSummit2025 #TechnologyAndHumanity #Leadership #AI #Innovation #PeopleFirst #Akkodis #Benelux

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It s quite telling that such an hamless study than the one published by a minor MIT research group this week, which found that 95% of enterprise pilots fail to impact P&L, is gaining so much attention. Even the FT covered it with a comment about the current fragilities in capital markets. Actually there is nothing new under the sun with this MIT study. The sample is narrow (less than 300 companies). It is no more than a very simple questionaire about project management. The main tenet (« fail fast and cream-skim your portfolio of projects ») is one of the oldest in AI. The actual telling of the episode is elsewhere : we entered this summer in a white space in terms of equity narrative for LLM builders. GPT 5 release showed that scale is not the main driver of value in LLM any more. And OpenAI made a spectacular U-turn in its equity story, relying on services as much as assets and even open-sourcing its assets. Some PERs are obviously over-inflated (above 500 for some companies…) and you will see a lot of calculations everywhere trying to reconcile profits with the CAPEX efforts made in terms of GPU acquisition (all the calculations provide results above a factor of 10). The actual piece to look at this summer is the one from Nvidia research that described the reason why small langage models are the future of agentic architectures. Here it is 👇. It is actually telling about the future business models in AI. The one who will capture the value in AI are the ones who actually know how to build effective small models, meaning transforming ongoing workflows and processes. https://lnkd.in/ed_-7Tdq

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The new Gartner Hype Cycle for AI is out, and it’s no surprise what’s landed in the trough of disillusionment… Generative AI. What felt like yesterday’s darling is now facing a reality check. Sky-high expectations around GenAI’s transformational capabilities, which for many companies, the actual business value has been underwhelming. Here’s why.… Without solid technical, data, and organizational foundations, guided by a focused enterprise-wide strategy, GenAI remains little more than an expensive content creation tool. This year’s Gartner report makes one thing clear... scaling AI isn’t about chasing the next AI model or breakthrough. It’s about building the right foundation first. ☑️ AI Governance and Risk Management: Covers Responsible AI and TRiSM, ensuring systems are ethical, transparent, secure, and compliant. It’s about building trust in AI, managing risks, and protecting sensitive data across the lifecycle. ☑️ AI-Ready Data: Structured, high-quality, context-rich data that AI systems can understand and use. This goes beyond “clean data”, we’re talking ontologies, knowledge graphs, etc. that enable understanding. “Most organizations lack the data, analytics and software foundations to move individual AI projects to production at scale.” – Gartner These aren’t nice-to-haves. They’re mandatory. Only then should organizations explore the technologies shaping the next wave: 🔷 AI Agents: Autonomous systems beyond simple chatbots. True autonomy remains a major hurdle for most organizations. 🔷 Multimodal AI: Systems that process text, image, audio, and video simultaneously, unlocking richer, contextual understanding. 🔷 TRiSM: Frameworks ensuring AI systems are secure, compliant, and trustworthy. Critical for enterprise adoption. These technologies are advancing rapidly, but they’re surrounded by hype (sound familiar?). The key is approaching them like an innovator...  start with specific, targeted use cases and a clear hypothesis, adjusting as you go. That’s how you turn speculative promise into practical value. So where should companies focus their energy today? Not on chasing trends, but on building the capacity to drive purposeful innovation at scale: 1️⃣ Enterprise-wide AI strategy: Align teams, tech, and priorities under a unified vision 2️⃣ Targeted strategic use cases: Focus on 2–3 high-impact processes where data is central and cross-functional collaboration is essential. 3️⃣ Supportive ecosystems: Build not just the tech stack, but the enablement layer, training, tooling, and community, to scale use cases horizontally. 4️⃣ Continuous innovation: Stay curious. Experiment with emerging trends and identify paths of least resistance to adoption. AI adoption wasn’t simple before ChatGPT, and its launch didn’t change that. The fundamentals still matter. The hype cycle just reminds us where to look. Gartner Report:  https://lnkd.in/g7vKc9Vr #AI #Gartner #HypeCycle #Innovation

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AI is only as good as the data you feed it. But what truly makes it powerful is the context we keep enriching it with and the purpose we choose to build it on. That’s why I found this perspective on Systemic AI so powerful. It moves beyond experiments and pilots to a framework that helps enterprises truly lead with AI and drive impact at scale. MathCo leaders have this unique framework of Systemic AI and as a Board Member of the company I have seen the traction it is getting with global enterprises and it’s being translated into real value for these businesses. Give this a read - “Systemic AI” : https://lnkd.in/e7fsYJAG #SystemicAI #OwnYourIntelligence #EnterpriseAI #AgenticAI Sayandeb Banerjee Aditya Kumbakonam Anuj Krishna Chandni Bose

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𝐍𝐨 𝐚𝐫𝐜𝐡𝐢𝐭𝐞𝐜𝐭, 𝐧𝐨 𝐜𝐢𝐭𝐲. Yet, every week, 𝐛𝐢𝐥𝐥𝐢𝐨𝐧-𝐝𝐨𝐥𝐥𝐚𝐫 𝐟𝐢𝐫𝐦𝐬 try to build AI systems with no architect at all. They call it “strategy.” What’s actually missing is 𝐚𝐫𝐜𝐡𝐢𝐭𝐞𝐜𝐭𝐮𝐫𝐞. Everyone talks about 𝐀𝐈 𝐚𝐦𝐛𝐢𝐭𝐢𝐨𝐧. Almost no one talks about 𝐀𝐈 𝐚𝐫𝐜𝐡𝐢𝐭𝐞𝐜𝐭𝐮𝐫𝐞. 👉 𝟗𝟓 % 𝐨𝐟 𝐀𝐈 𝐩𝐫𝐨𝐠𝐫𝐚𝐦𝐦𝐞𝐬 𝐬𝐭𝐚𝐥𝐥 𝑛𝑜𝑡 𝑏𝑒𝑐𝑎𝑢𝑠𝑒 𝑜𝑓 𝑚𝑜𝑑𝑒𝑙𝑠, 𝑏𝑢𝑡 𝑏𝑒𝑐𝑎𝑢𝑠𝑒 𝑜𝑓 𝑚𝑖𝑠𝑠𝑖𝑛𝑔 𝑎𝑟𝑐ℎ𝑖𝑡𝑒𝑐𝑡𝑢𝑟𝑒. The first five steps look great on paper - and that’s exactly where most organisations stop. 1️⃣ 𝐋𝐞𝐚𝐝 𝐟𝐫𝐨𝐦 𝐭𝐡𝐞 𝐜𝐞𝐧𝐭𝐫𝐞, 𝐚𝐜𝐭 𝐟𝐫𝐨𝐦 𝐭𝐡𝐞 𝐞𝐝𝐠𝐞. AI impact grows when the CEO owns ambition, but value emerges where business and tech meet. 2️⃣ 𝐃𝐞𝐬𝐢𝐠𝐧 𝐟𝐨𝐫 𝐫𝐡𝐲𝐭𝐡𝐦, 𝐧𝐨𝐭 𝐩𝐫𝐨𝐣𝐞𝐜𝐭𝐬. Weekly AI reviews beat quarterly innovation updates. Always. 3️⃣ 𝐁𝐮𝐢𝐥𝐝 𝐣𝐨𝐢𝐧𝐭 𝐨𝐰𝐧𝐞𝐫𝐬𝐡𝐢𝐩. Each function must own a measurable AI outcome - that’s when transformation becomes real. 4️⃣ 𝐌𝐚𝐤𝐞 𝐭𝐞𝐜𝐡𝐧𝐨𝐥𝐨𝐠𝐲 𝐜𝐨𝐦𝐩𝐨𝐬𝐚𝐛𝐥𝐞. Reusable components. Common data foundations. That’s how scale becomes sustainable. 5️⃣ 𝐈𝐧𝐯𝐞𝐬𝐭 𝐢𝐧 𝐩𝐞𝐨𝐩𝐥𝐞, 𝐧𝐨𝐭 𝐩𝐥𝐚𝐭𝐟𝐨𝐫𝐦𝐬. Upskilling outlasts tooling. Always has, always will. 𝐈 𝐥𝐞𝐚𝐫𝐧𝐞𝐝 𝐭𝐡𝐢𝐬 𝐭𝐡𝐞 𝐡𝐚𝐫𝐝 𝐰𝐚𝐲 - 𝐞𝐯𝐞𝐧 𝐰𝐨𝐫𝐥𝐝-𝐜𝐥𝐚𝐬𝐬 𝐟𝐢𝐫𝐦𝐬 𝐬𝐭𝐚𝐥𝐥 𝐫𝐢𝐠𝐡𝐭 𝐡𝐞𝐫𝐞. Not for lack of will, but for lack of 𝐚𝐫𝐜𝐡𝐢𝐭𝐞𝐜𝐭𝐮𝐫𝐞. 6️⃣ 𝐆𝐨𝐯𝐞𝐫𝐧𝐚𝐧𝐜𝐞 𝐚𝐬 𝐨𝐩𝐞𝐫𝐚𝐭𝐢𝐧𝐠 𝐬𝐲𝐬𝐭𝐞𝐦. Not committees - living guardrails. Trust, compliance, and agility moving in one rhythm. 7️⃣ 𝐕𝐚𝐥𝐮𝐞 𝐫𝐞𝐚𝐥𝐢𝐬𝐚𝐭𝐢𝐨𝐧 𝐚𝐬 𝐝𝐢𝐬𝐜𝐢𝐩𝐥𝐢𝐧𝐞. A dedicated 𝐕𝐚𝐥𝐮𝐞 𝐎𝐟𝐟𝐢𝐜𝐞 linking AI to measurable outcomes - tracking ROI in real time. 𝐃𝐢𝐬𝐜𝐢𝐩𝐥𝐢𝐧𝐞 𝐬𝐭𝐚𝐫𝐭𝐬 𝐭𝐡𝐞 𝐣𝐨𝐮𝐫𝐧𝐞𝐲. 𝐆𝐨𝐯𝐞𝐫𝐧𝐚𝐧𝐜𝐞 𝐚𝐧𝐝 𝐯𝐚𝐥𝐮𝐞 𝐚𝐫𝐜𝐡𝐢𝐭𝐞𝐜𝐭𝐮𝐫𝐞 𝐬𝐜𝐚𝐥𝐞 𝐢𝐭. AI doesn’t fail because of technology. It fails without an architect. 𝐖𝐡𝐞𝐫𝐞’𝐬 𝐲𝐨𝐮𝐫 𝐨𝐫𝐠𝐚𝐧𝐢𝐬𝐚𝐭𝐢𝐨𝐧 𝐭𝐨𝐝𝐚𝐲 - 𝐬𝐭𝐞𝐩 𝟓 𝐨𝐫 𝐬𝐭𝐞𝐩 𝟕? #Leadership #AI #Governance #ValueCreation #DigitalTransformation #AIFactory

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