Nadine Soyez
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Turn AI into measurable results fast | From strategy to adoption with practical execution frameworks for business leaders | Top 12 LinkedIn ‘AI at Work’ Voice to follow Europe | 15+ yrs digital transformation
Belgium
8K followers
500+ connections
About
88% of organisations are experimenting with AI. Only a few get measurable results.
I help close that gap.
AI becomes valuable when it is put into practice and solves real business problems.
The real challenge is not the technology itself. It is translating AI into measurable business performance and making it work reliably within existing operations — with aligned teams, the right structures, and a clear approach that replaces scattered AI use with intentional, scalable practice.
That is where I focus my work.
I help business leaders who are done with AI experiments or isolated pilots that go nowhere. My focus is execution: turning AI ambition into measurable business outcomes through the right use cases, structures, and teams that actually adopt them.
This means identifying where AI creates actual business value, turning use cases into a roadmap and implementation plan, and establishing the structures required for adoption, scalability, and long-term reliability.
I work at the intersection of business, technology, and people, focusing on what works in the current context rather than what looks good on a slide. My clients value my practical, execution-driven approach that delivers results quickly.
Over 15 years in digital transformation, I watched the same pattern repeat itself with every new technology wave — and AI is no different. Early enthusiasm, scattered initiatives, pressure to show impact, and a growing gap between what was promised and what was delivered. I decided to focus entirely on that gap.
I combine technological depth with a strong understanding of business processes. Two “languages “ that often operate separately and don’t understand each other, but need to be aligned for AI to deliver measurable outcomes.
Today, I’m LinkedIn Top 12 “AI at Work” Voice to follow in Europe. I worked with Baloise, BASF, Bundeswehr, City of Dresden, Deutsche Bahn, Caritas, Deutsche Bundesbank, SMA, Stadtwerke München, SThree and many other companies. I regularly speak at major conferences.
👉 How I can help:
→ AI Execution Sprint:4–6 weeks to know exactly where AI creates value, what it takes to build, and a clear plan to implement and scale.
→ AI in Practice Workshop: One day to move your team from inconsistent AI use to shared alignment, practical skills, and first AI assistants built for based on their work.
Based in Belgium, office in Frankfurt, working across Europe, delivering my services in German, English, Dutch.
Reach out and make AI work:
✉ DM on LinkedIn or email to: nadine.soyez@designingaiheroes.com
Articles by Nadine
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What shadow AI is actually telling you. Learn to read the signals.
What shadow AI is actually telling you. Learn to read the signals.
The tools your teams already use without asking are your clearest signal of where AI would actually help Right now, in…
4 Comments -
The difference between AI activity and AI impact. A week-by-week breakdown from the field.
The difference between AI activity and AI impact. A week-by-week breakdown from the field.
When I sit down with leaders ten weeks into their AI initiative, the picture is almost always the same. Pilots are…
6 Comments -
AI adoption that lasts: honest lessons from practice
AI adoption that lasts: honest lessons from practice
An AI strategy rarely fails when it is drafted. It fails in the months after launch, when the people meant to use it…
18 Comments -
Your AI Pilot Worked. Can You Prove It? — The Step That Turns Pilots Into Business Cases
Your AI Pilot Worked. Can You Prove It? — The Step That Turns Pilots Into Business Cases
A successful AI pilot means nothing if no one can demonstrate what actually changed in the business. The pilot has…
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The difference between an AI plan and an AI roadmap that scales
The difference between an AI plan and an AI roadmap that scales
The question every AI roadmap should answer before kickoff The AI roadmap looked convincing on paper. Quarterly…
17 Comments -
Before you build any AI pilot, do this. Most teams skip 2 critical steps. Then wonder what went wrong.
Before you build any AI pilot, do this. Most teams skip 2 critical steps. Then wonder what went wrong.
This is part 2 of my newsletter series about „The Execution Framework", we cover Use Case Design and prototype. Most…
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Most AI Strategies Fail Before They Start. Here Is Why.
Most AI Strategies Fail Before They Start. Here Is Why.
Stage 1 + 2 of The 7-Step Framework That Turns AI Strategy Into Results: Diagnose where AI will actually move the…
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Congratulations. Your Employees Love AI. Now What?
Congratulations. Your Employees Love AI. Now What?
Last week at the AI Governance and Data Governance Europe Conference in London, a CEO told me he had discovered several…
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The Orchestra Has No Conductor - Why AI activity is not the same as AI results, and what leaders can practically do about it
The Orchestra Has No Conductor - Why AI activity is not the same as AI results, and what leaders can practically do about it
AI activity without coordination is not progress. It is noise at scale.
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The Pilot That Never Lands - 3 root causes why this happens and what successful AI projects do differently
The Pilot That Never Lands - 3 root causes why this happens and what successful AI projects do differently
This is the most common pattern I encounter in my work: organisations that are running AI pilots but not building AI…
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Activity
8K followers
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Nadine Soyez shared thisMost leaders see shadow AI as a risk to shut down. I see it as free source of research. Every unapproved tool in use shows you what people actually do rather than what they say, exactly where their work is painful and where AI genuinely helps. In my client work, that signal is often the most useful part in the AI use-case evaluation. But only if you surface it properly first. This week's newsletter breaks down how: - Run an open conversation, not an audit - Map the need under each tool, not just the tool name - Sort what you find into sanction, replace, and stop - Convert the strongest patterns into real use cases Surface it first. Govern it second. How do you approach shadow AI in your organisation? Immediately forbid it or evaluate it?What shadow AI is actually telling you. Learn to read the signals.What shadow AI is actually telling you. Learn to read the signals.
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Nadine Soyez shared thisWhat does it actually take to move from AI ambition to AI execution? I interviewed someone who knows best. I sat down with Paul Smith (MBA CITP FBCS) CIO at Amnesty International, for a new episode of the Designing AI Heroes Podcast. In our conversation, he shared his insights from "From 'we should do AI' to actually doing it". Paul is a globally recognised, multi-award-winning CIO (Global CIO100, World CIO200, UK CIO100). So you can expect an insightful talk and much experience from practice. Here's what we got into: → The first step that broke the "we should" loop → How AI is used in daily practice → Examples where AI delivered measurable impact → What surprised him most, good and bad → How leadership teams react when AI comes up → What he sees in his leadership team → His advice to a peer CIO calling him today If your organisation knows it should act on AI but doesn't know where to start in a practical way, this is for you. Link to listen on Spotify and Apple Podcasts in the comments. Where is your organisation still stuck in the "we should" stage?
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Nadine Soyez shared this45% of executives say AI is delivering ROI. Only 27% of middle managers agree. That gap isn't about belief. It's about who's doing the work. A recent HBR article confirms what I see in client work across Europe: senior leaders and middle managers aren't living in the same reality. The numbers are stark: 56% say their organisation is adopting AI faster than competitors. Only 28% of managers agree. 76% of executives believe employees are enthusiastic about AI. Only 31% actually are. (BCG/Columbia) Middle managers are 64% more likely to call themselves "cautious." 43% of workers think AI will make their job more frustrating. 53% worry about job security. (Mercer) Executives are rewarded for vision. Middle managers are rewarded for execution. They live with it when AI outputs are wrong, when workflows break, and when people have to redo work. When you hand them an AI mandate without reducing their load first, you're asking them to build the plane while flying it. Ignore this gap, and your AI pilots stay pilots. Managers protect their teams by slowing adoption, and you never scale. What actually helps: → Diagnose before you prescribe → Co-create the playbook with managers → Reduce admin load before adding AI work on top → Measure AI readiness and confidence, not just usage → Treat cautious feedback as data, not resistance AI only creates value when it's put into practice. And practice happens a lot in the middle. Source: HBR, Korst, Puntoni & Tambe (April 2026). Link in comments. Where is this divide showing up in your organisation?
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Nadine Soyez shared thisThe gap between 'we're working on AI' and 'AI is working for us' is exactly ten weeks, if you structure them right. When I sit down with leaders ten weeks into their AI initiative, the picture is almost always the same. Pilots are running across a couple of teams. An AI tool has been rolled out. There is a portfolio of use cases and maybe a roadmap for a more or less strategic plan. The activity and engagement are real and genuine. What's almost never on that list: one use case that has actually changed how the work gets done. That gap, between visible activity and real impact, is the story of the first ten weeks in most AI initiatives. In this week's newsletter, I break down - from my work and experience - what ten weeks of AI execution actually produce, week by week: → Two weeks of foundation - don't skip this → The decisions that separate doers from planners → Where the prototype sits during the pilot → What week ten does NOT produce The organisations I work with are usually honest about this: week ten looks like activity, not impact. What does yours look like?The difference between AI activity and AI impact. A week-by-week breakdown from the field.The difference between AI activity and AI impact. A week-by-week breakdown from the field.
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Nadine Soyez shared thisWe cut reporting time by 60%. The team checked every number anyway. Last month, I worked with a client on a reporting workflow for their operations team. The AI generated shift summaries, flagged quality deviations, and prepared performance updates. Technically, it worked. But the team was checking every number and verifying every paragraph. What we changed wasn't the model or the prompts. We adjusted three elements in the workflow design. 𝟭. 𝗠𝗮𝗸𝗲 𝘁𝗵𝗲 𝗿𝗲𝗮𝘀𝗼𝗻𝗶𝗻𝗴 𝘃𝗶𝘀𝗶𝗯𝗹𝗲 The system now shows which data sources and time periods each insight came from. No more black box. People trust outputs they can trace. When the reasoning is hidden, doubt fills the gap. 𝟮. 𝗔𝗱𝗱 𝗮 𝗰𝗼𝗻𝗳𝗶𝗱𝗲𝗻𝗰𝗲 𝘀𝗶𝗴𝗻𝗮𝗹 Not every section carries the same reliability. A simple indicator flags whether a section is solid, needs a quick check, or requires closer review. It tells operators and planners where to focus their attention, instead of making them second-guess everything. 𝟯. 𝗕𝘂𝗶𝗹𝗱 𝗶𝗻 𝗮 𝗳𝗲𝗲𝗱𝗯𝗮𝗰𝗸 𝗹𝗼𝗼𝗽 Team members can now flag outputs in one click: correct, partial, or incorrect. That feedback improves the retrieval logic and prompts over time. It gives the team a sense of control. They're shaping the system, not just using it. Within a short amount of time, spot-checking dropped significantly. The team started trusting the outputs enough to act on them directly. Trust in AI rarely comes down to the model itself. It comes down to how the workflow around it is designed. Where in your organisation do AI systems still feel like a black box?
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Nadine Soyez reposted thisNadine Soyez reposted thisMost participants who had a 20-minute discussion with AI chatbots about health, careers or relationships followed its advice. However, 2-3 weeks later, participants receiving advice from AI showed no sustained well-being. These findings reveal that LLMs exert substantial influence over real-world personal decisions without delivering measurable psychological benefits. https:// arxiv.org/abs/2511.15352
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Nadine Soyez shared thisBefore you explore another AI tool, answer these 4 questions. The problem isn't a shortage of use cases. Every team has a list. The challenge is that most teams can't prioritise, assign ownership, or align everybody on goals, success and metrics. So AI stays a pilot forever. This is what I work on first with my clients: 𝗦𝘁𝗲𝗽 𝟭: Map the process you want to change Document how the work actually happens today. ↳ Where are the handoffs? ↳ Where does time disappear? 𝗦𝘁𝗲𝗽 𝟮: Capture your current KPIs Pick 2-3 metrics that matter for this process. ↳ Time per task ↳ Error rate or rework volume ↳ Cost or headcount involved 𝗦𝘁𝗲𝗽 𝟯: Align stakeholders on what "better" means Write this down before anything gets built. ↳ Define clear goals that everybody understands ↳ Align everybody on what success looks like 𝗦𝘁𝗲𝗽 𝟰: Set your measurement system Decide now how you'll track change from the beginning. ↳ Set metrics ↳ Learn the signals when to continue, adapt, or stop. This takes a few days, not weeks. But it's the difference between a pilot that dies quietly and one that earns a budget. What's the one step your team usually skips?
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Nadine Soyez shared this5 signals I see that AI adoption will fail. AI Adoption fails when people are expected to change how they work with AI, and nothing is in place to support them. This is the final issue of my newsletter series on the AI execution framework: Enablement, Change and Communication. By the time leadership teams reach this step, they treat enablement, change and communication as an add-on to the rollout rather than a discipline in its own right. Which is why so many AI initiatives quietly lose momentum just after launch. In final issue I cover: • 5 signals adoption is stalling early • Why enablement ≠ training • Why change isn't a one-time event • Why communication ≠ an announcement • The mistake most leadership teams make • Concrete moves to close the gap this quarter Change is the slowest part of any AI initiative because it's about people, not systems. The organisations that hold adoption over time design it deliberately. The rest watch it fade. Which of the 5 signals resonates with you the most?AI adoption that lasts: honest lessons from practiceAI adoption that lasts: honest lessons from practice
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Nadine Soyez shared thisWalk into a company that's actually good with AI, and the first surprise is how little AI gets mentioned. No one's debating which model to use. That conversation happened weeks ago. After having 50+ conversations with leaders in my AI Practice Hub Community last month, here's what the good ones get right: 𝗧𝗵𝗲 𝗯𝗼𝘁𝘁𝗹𝗲𝗻𝗲𝗰𝗸 𝗶𝘀 𝗿𝗮𝗿𝗲𝗹𝘆 𝘁𝗵𝗲 𝗺𝗼𝗱𝗲𝗹. It's the workflow. Who owns the output? Who handles edge cases? Good companies put 80% of their effort here. Most put 80% into picking tools. 𝗧𝗵𝗿𝗲𝗲 𝗽𝗿𝗼𝘃𝗲𝗻 𝘂𝘀𝗲 𝗰𝗮𝘀𝗲𝘀 𝗯𝗲𝗮𝘁 𝘁𝗵𝗶𝗿𝘁𝘆 𝗽𝗼𝘁𝗲𝗻𝘁𝗶𝗮𝗹 𝗼𝗻𝗲𝘀. A long backlog isn't a strategy. Pick a few, test fast, cut what doesn't work. 𝗚𝗼𝘃𝗲𝗿𝗻𝗮𝗻𝗰𝗲 𝗶𝘀 𝗱𝗮𝘆-𝗼𝗻𝗲 𝘄𝗼𝗿𝗸. Set decision rights and risk limits before the pilot starts. If you wait, you'll rebuild everything when compliance shows up. Getting this right early doesn't slow you down. It helps you scale faster. 𝗣𝗿𝗼𝘁𝗼𝘁𝘆𝗽𝗲𝘀 𝗯𝗲𝗮𝘁 𝗣𝗼𝘄𝗲𝗿𝗣𝗼𝗶𝗻𝘁𝘀. Six weeks in and nothing's running? The initiative is stalling. Even a rough prototype forces real decisions. 𝗛𝘂𝗺𝗮𝗻-𝗶𝗻-𝘁𝗵𝗲-𝗹𝗼𝗼𝗽 𝗶𝘀𝗻'𝘁 𝗮 𝗰𝗼𝗺𝗽𝗿𝗼𝗺𝗶𝘀𝗲. 𝗜𝘁'𝘀 𝘁𝗵𝗲 𝗱𝗲𝘀𝗶𝗴𝗻. The good ones build review steps and escalation paths in from day one. Not as a backup plan. As a clear choice about where human judgment belongs. 𝗦𝗰𝗮𝗹𝗶𝗻𝗴 𝗶𝘀 𝘀𝗲𝗾𝘂𝗲𝗻𝗰𝗶𝗻𝗴. Start with quick wins to build trust, then move to bigger bets. A quick win looks like automating one manual reporting step in four weeks. Companies with pilot lists are common. Companies with pilot sequences are not. The pattern across all of this: they're not trying to become "AI companies." They're becoming companies where AI quietly works. Which of these do you see missing most often in your organisation?
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Nadine Soyez liked thisNadine Soyez liked thisExcited to join the lineup at AISecCon in Toronto in just one week. A lot of enterprise teams are realizing the hard part of AI is managing the growing operational complexity around systems that inevitably make mistakes. And in the case of agents, take destructive actions. I’ll be sharing some of my latest work and thinking behind Trust Engineering and how “trust infrastructure” can help organizations scale enterprise AI systems. Some of the themes I’ll be covering: 🔹How teams preserve accountability while working alongside AI systems 🔹Designing for resilience, recoverability, and safe failure handling in systems that will produce imperfect outputs 🔹Observability and guardrails to handle the explosion of edge cases with enterprise AI systems If you’ll be in the area, let me know. I’d love to connect!
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Nadine Soyez liked thisNadine Soyez liked thisThe successful AI projects you wish for all have the same DNA. There’s a big difference between AI that creates value and AI that becomes an expensive distraction. A certain kind of DNA. It's not technical brilliance or the newest model or the biggest budget. Just a few fundamentals that teams get right. This is how I’d break it down: V - Validate 🔍 ↳ Start with the problem, not the tool. ↳ AI only matters if it improves something the business already cares about. A - Assign 👤 ↳ Every AI initiative needs a clear owner. ↳ Not a committee or just “the business”. One named, accountable person. L - Link 📈 ↳ Tie AI to measurable outcomes. ↳ Faster decisions, lower risk, better customer experience, higher productivity. U - Understand ⚠️ ↳ Every AI use case comes with trade offs. ↳ Privacy, bias, security, trust, reputation. Know the risks before you scale. E - Embed 🏢 ↳ AI creates value when it becomes part of everyday work. ↳ The real challenge is adoption, not the pilot. Getting value from AI means quietly focusing on the fundamentals, not chasing hype. Technology matters, but execution matters more. Consider all these areas and give you and your team a better chance of success. What do you think most organisations are still getting wrong about AI? ♻️ Repost to help someone get value from AI. 🔔 Follow Clare Kitching for insights on unlocking value with data & AI. 💎 Get more from me with my free newsletter here: https://lnkd.in/ghBtk6jR
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Nadine Soyez reacted on thisNadine Soyez reacted on thisIt is the journey, not the destination. As part of becoming a better Swiss 🇨🇭, I signed up for a new adventure this year: the Rigi Marsch. Theoretically, the trek is 48 km. In practice, we walked 52.5 km, with around 1,700 m of altitude gain. On km 15, the rain started, accompanied by violent gusts of wind. Just before the final steep climb, I had soaked gloves, a freezing back, and shoes that felt like portable muddy ponds. The last 45 minutes, the wind fired thousands of icicles into my face, and I could barely keep my balance on the ice. Did I mention that the whole hike happens at night? I am often asked why I take up those challenges — Spaghetti Tour in 2022 (11 peaks over 4000 m in 5 days), Kilimanjaro in 2023, Dufourspitze in 2024, or the Rigi Marsch this year. I was never fully prepared physically, nor am I a reasonable athlete. I barely make time for a couple of strength training sessions a week. I started hiking (in fact, moving) less than 7 years ago. But that restless mind will always push me beyond my limits. Over the last two months, I walked more than 320 km and climbed around 15,000 vertical meters, preparing for the Rigi Marsch. Without it in the calendar, I would probably have negotiated myself out of every single training session. My weekends and my professional life follow a very similar pattern: a bit of adventure, a lot of curiosity, and absolutely no interest in standing still. My life, my way.
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Nadine Soyez liked thisNadine Soyez liked thisThe organizations most eager to replace managers with AI may be the ones that need better managers the most. That sounds backward, but it is the real tension in this conversation. If a manager's role is mostly chasing updates, routing information, summarizing meetings, creating dashboards, and reminding people what they already agreed to do, then yes, AI is going to make that version of management look expensive very quickly. But that does not prove management is obsolete. It proves we have allowed too many management roles to become buried under administrative work. The real test is what remains after AI removes the coordination layer. Someone still has to translate strategy into the daily decisions people make. Someone still has to coach junior employees before they develop bad judgment at scale. Someone still has to know why a customer exception exists, why one team does not trust another, why a process failed the last time it was attempted, and why the obvious answer in the dashboard might be the wrong answer in the organization. That work is harder to measure, which is exactly why it is easy to undervalue. This is where companies can make a very expensive mistake. Cutting bloated layers is not the same as hollowing out the middle. One removes drag. The other removes context. A lot of organizations probably do need fewer managers doing coordination work. But they may need more leaders who can create clarity, build trust, develop talent, and turn executive priorities into execution without losing the human reality of the business. AI should make management less administrative. It should not make leadership disappear. The smarter question is not how many managers AI can replace. It is what kind of management work should disappear so the people left in those roles can finally do the work that actually creates value. That is the difference between redesigning the organization and simply cutting into the muscle.
Experience
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Senior Project Manager & Consultant
Fasihi GmbH
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Education
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Steinbeis School of International Business and Entrepreneurship (SIBE)
MBA General Management
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Postgraduate studies Master of Business Administration, Master Thesis in business strategy development, international management courses abroad in Sao Paolo, Brazil
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Duale Hochschule Baden-Württemberg
Diplom/Bachelor Business Administration
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Studies in cooperation with Fraport AG, Frankfurt am Main, Germany
Licenses & Certifications
Skills
Publications
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Digitale Medien im Unternehmen
Springer Verlage Heidelberg
Other authors -
Courses
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Prompting für generative KI mit dem Virtual Team Heroes Framework
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Languages
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French
Native or bilingual proficiency
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English
Full professional proficiency
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German
Native or bilingual proficiency
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Dutch
Limited working proficiency
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TEASER PROMPT: "Imagine you are designing a high-impact executive leadership program in partnership with Vlerick Business School. Together, you are launching a new, elite “Top Gun” education track exclusively for senior engineering leaders: (first time) VPs of Engineering, platform leads, CTOs, and high potential technical executives in fast-scaling tech companies and software-enabled enterprises. The program is called Engineering Leadership. This is not a theoretical, academic course. It is bold, frontrunning, and deeply practical. Guest professors are “top gun” technical leaders from Belgium’s top 20 tech scale-ups and unicorns, practitioners who have built, scaled, and transformed real engineering organizations, building future proof enterprises. The 10 sessions are structured around crucial key complexities & responsibilities. essential pillars. Participants learn how to lead through profound technological change, scalable & performant architectures, and build engineering cultures that evolve as the company grows. The ambition: to equip our best engineering leaders with the strategic, technical, and organizational superpowers required to lead at scale in an era of exponential innovation." HERE'S YOUR DEEPLY PRACTICAL ENGINEERING LEADERSHIP PROGRAM, YOU WANT ME TO REFINE THE TOPICS & SPEAKERS? "No. It's perfect" PM me or more info via https://lnkd.in/e3XjKz22 🎪 🧑🎓
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