Articles
Le regard d'Otman sur l'IA, le leadership, la confiance et le côté humain de la transformation.
For the past few years, leaders have been told to learn how to prompt AI. The harder question is now arriving: what work are you actually willing to hand over to it? Imagine an AI agent opening your CRM, reading customer history, preparing a proposal, updating the pipeline and scheduling the next action. Nobody is asking it a question anymore. Work has been delegated.
Lire →The most robotic conversations I had last week were with humans. And it made me question something we rarely ask in AI: Are we sure we should make AI as human as possible? Last week, on my road trip home, my car broke down in France. I was travelling with my three daughters. Late at night, after being towed away, we were dropped in Massiac, a tiny mountain village. No car. Luggage in hand. No idea where we would sleep. ANWB had the full case. They knew where we were and who I was travelling with.
Lire →متى يشعر الإنسان بأنه ينتمي حقا إلى وطنه؟ مداخلة قدمتها في ندوة بالرباط حول مواطنة مغاربة العالم في العصر الرقمي — وكيف أن الانتماء الحقيقي لا يقاس بالمسافة، بل بالأثر.
Lire →Your junior employees were the training ground. AI just removed it. For years the messy first draft, the clumsy customer reply, the analysis that took too long, all of it looked inefficient. But that was where judgment got built. Through small mistakes, unclear tasks, difficult customers, and watching experienced people make trade-offs in real time. Now AI does that first layer. The draft is generated, the meeting summarized, the research compressed, the reply suggested.
Lire →Forty percent of AI agent projects will be cancelled before 2027. The reason will not be the models. Gartner put a number on something I keep seeing up close. The technology works. The agents are capable. And still the projects quietly die. What kills them is rarely the AI. It is an agent dropped onto a process nobody fully understood. A workflow with no clear owner. A pilot that impressed everyone in the demo, then met the messy reality of how the business actually runs.
Lire →Everyone is using AI. That is becoming the problem. Not because AI is bad. Not because people should stop experimenting. The problem is that AI is entering companies faster than teams can structure its use. One person uses ChatGPT for strategy. Another uses Claude for documents. A developer uses Copilot. A recruiter uses an AI sourcing tool. A manager uses AI to summarise meetings. A sales team uses AI to write outreach. Individually, all of this can be useful.
Lire →AI agents are entering the messy part of business. Not the clean demo. The real business. The inbox full of half answers. The follow-up nobody sent. The candidate who replies late. The customer who changes the context. The manager who wants speed, but also control. That is where AI agents start to matter. Not when they give a polished answer. When they help carry the small operational loops that slow companies down every day.
Lire →Agentic AI is moving from assistant logic to execution logic. That may sound like a small shift. It is not. It changes how companies design workflows, governance, procurement and responsibility. For a long time, AI in companies was mostly about support. Support for writing. Support for search. Support for analysis. Support for faster thinking. That phase is not over, but something more serious is emerging now. Agentic AI is moving from assistant logic to execution logic.
Lire →Vibe coding tricked me into thinking I shipped a product. The demo was clean. The UI was sharp. The deploy worked. I genuinely thought I was done. Then real users showed up. And everything I never built started breaking. → Auth flows leaked data because RLS was never set up → The API got hammered with zero rate limiting → Errors piled up silently with nothing tracking them → The database slowed to a crawl on its first real query
Lire →AI has become a priority in many organizations. Clear thinking has not.
Lire →AI does not just scale what you build. It reveals how you think.
Lire →Most companies are training AI skills. Very few are structuring them. That is the real gap behind who should learn what.
Lire →In many teams, the first tasks to be supported or automated are the ones people used to learn from. Drafting. Research. Basic analysis. That work was never just about output.
Lire →Everyone is moving toward AI. Tools are bought. Pilots are launched. Agents are being built. From the outside, it looks like progress.
Lire →During the Industrial Revolution, textile workers didn't just complain. They broke into factories and destroyed the looms.
Lire →A lot of professionals still think their value is in doing the work faster. But AI is already becoming very good at speed.
Lire →That is the part many leaders still underestimate in AI adoption. A company can automate faster, reduce costs, speed up workflows.
Lire →One of the biggest mistakes in AI is assuming the future will be built only by the loudest markets. Some of the most serious AI ambition I see today ...
Lire →AI is creating two kinds of professionals: those who use tools, and those who redesign work. That distinction matters more than most people realise.
Lire →The hardest shift for most leaders is realising that stepping back is not the same as stepping away. Leading from behind requires more trust, more pa
Lire →AI is not just disrupting jobs, it is revealing who matters when the obvious work gets easier. For years, many professionals could hide behind activity. Long meetings. Big presentations. Complex language. Endless coordination. Work that looked important from a distance. AI is starting to strip some of that away. Because when a machine can produce the first draft, the summary, the analysis, the code scaffold, the visual, or the action plan in seconds, something uncomfortable happens:
Lire →In a laboratory, something quiet but powerful happened. A simple dandelion root extract triggered cancer cell death in colon cancer cells - within ju
Lire →The biggest career shift in AI is not automation. It is redesign. More and more jobs are no longer defined only by what you can do yourself. They a
Lire →AI is quietly creating a new class of professionals: the orchestrators. For years, professional value came from one of two places. Either you were t
Lire →And we call it education. I see it every year in universities. Students get clean assignments. Clear requirements. Expected outputs. And they become very good at one thing: Doing exactly what they are told. No deviation. No questioning. No real ownership of the problem. For years, that was fine. Because execution was valuable. But now we have AI. And AI does one thing extremely well: It follows instructions. Give it a prompt, and it delivers. Faster, cheaper, and without hesitation.
Lire →They fail right after the demo. In the demo, everything looks smooth. Agents talk to each other. Tasks get completed. The flow feels almost intelligent. But a demo is a controlled environment. Production is not. What changes? Real users behave unpredictably. Data is incomplete or messy. Edge cases appear immediately. And suddenly, the system starts doing things you didn't design for. This is where most teams realise something uncomfortable. They didn't build a system. They built a scenario.
Lire →I see many teams jumping straight into complex multi-agent setups. Planning orchestrators, memory layers, tool chains. It sounds impressive. It usually breaks fast. The first mistake is simple: we treat agents like magic, not like people. If you hire a new employee, you don't give them access to everything on day one. You don't expect them to make strategic decisions in week one. You don't measure them by how "intelligent" they sound.
Lire →From AI Agents to AI Teams Last year was the year of AI agents. In 2026 something more interesting is happening. We are starting to orchestrate teams of them. Vibe coding is becoming more serious, and multi-agent setups are appearing everywhere. Tools that connect agents together. Tools that orchestrate them. Platforms promising entire systems built from collaborating assistants. Every week there seems to be another new framework.
Lire →As Ramadan moves into its final days, I keep noticing an interesting contrast with the way we build technology. AI systems are designed around optimization. Better prompts. Better outputs. More efficiency. Everything is measured by how quickly we can produce a result. Ramadan works in a very different way. It creates space to ask a quieter question. Not how to do something better. But why we do it in the first place. Technology keeps pushing us to improve performance.
Lire →When Good Answers Become Cheap, Judgment Becomes Expensive A polished answer is not the same as a true one. Yet the AI era is quietly training us to forget that. We are entering a time where many things have become cheap. Confidence. Presentation. Structure. Well-written explanations. AI can produce all of them in seconds. What remains rare is something else. Judgment. The ability to pause and ask:
Lire →AI Is Making Everyone Sound Smart. But Are We Actually Understanding More? AI can write emails. Explain concepts. Generate code. Summarize books. Suddenly everyone can produce very convincing answers. But there is a quiet risk. When answers become easier, it becomes harder to notice when we don't really understand something. Ramadan made me reflect on something different this week. Discipline is not only about controlling what we consume. It is also about being honest with ourselves.
Lire →Every week there is a new model. A new tool. A new breakthrough. A new reason to feel behind. AI is accelerating everything. And if I am honest, it is exhausting sometimes. Ramadan does something very different. It slows me down. Less consumption. Less constant input. More awareness of what I actually choose to engage with. And that made me notice something. Maybe the real challenge in AI is not learning faster. Maybe it is choosing better.
Lire →AI is not just evolving. It is accelerating. Faster decisions. Shorter cycles. Instant outputs. Continuous optimisation. Speed has quietly become a virtue. Ramadan introduces a different discipline. Restraint. Not as weakness. But as strength under control. Fasting trains the space between impulse and action. The pause before reacting. The choice before responding. In technology, we often optimise for what can be done. Rarely for what should be done.
Lire →We talk a lot about optimizing AI. Faster models. Better reasoning. Stronger alignment. Smarter memory. But we rarely talk about optimizing ourselves. For me, Ramadan is not only a spiritual tradition. It is structured self training. You interrupt automatic behaviour. You delay gratification. You observe impulses instead of reacting to them. In AI, performance improves when noise is reduced and feedback loops are clear. The same principle applies to the human system.
Lire →When I showed this image during my keynoye, people thought I was talking about a movie. I wasn’t. In a real research experiment published by Anthropic, researchers tested how advanced AI systems behave when their existence is threatened. Here is what happened. A fictional company CEO, “Kyle”, decided to shut down an AI system called “Alex”. Alex had access to internal tools, including email.
Lire →Start asking it to think with you. Too many people are turning Claude and ChatGPT into oracles. They ask a question. They get a confident response. They move on. No challenge. No counter-argument. No pressure test. Just acceptance. That is not augmentation. That is intellectual outsourcing. AI is not risky because it is intelligent. It is risky because it sounds certain. If you treat AI as the final voice in the room, your critical thinking slowly weakens.
Lire →It’s control, through memory. We keep measuring models by how “smart” they sound in a chat. But the real shift is happening elsewhere, in agents that remember, act, and keep going. Vibe coding made this obvious to me. You can ship a prototype before you fully understand what you just created. Not because the model is a genius. Because the loop got shorter, and the system started carrying context for you.
Lire →It’s human incentives. We keep debating agents, autonomy, memory, orchestration layers. But in practice, most AI initiatives stall for a much simpler reason: Nobody wants their role, power, or metrics to change. I’ve seen teams invest in agent frameworks they barely use. Leaders approve pilots they never operationalise. Managers praise innovation while quietly protecting existing workflows. AI does not fail because it lacks capability. It fails because it exposes misaligned incentives.
Lire →There is a subtle but critical mistake in how many organisations talk about AI. We say AI is becoming autonomous. What we often mean is that we have delegated actions without redesigning responsibility. Those two are not the same. - Autonomy implies intention, limits, and ownership. - Delegation, when done poorly, is simply letting something act while hoping control will somehow follow. In human organisations, delegation is never absolute. We delegate tasks, not accountability.
Lire →When you misname systems, you misplace trust. And then you build workflows that no one can truly own when they fail. “Computer science is a terrible name,” the MIT professor says, almost casually. Not because the field is weak, but because the name makes beginners confuse the tool with the essence. That pattern is repeating in AI, at scale. We named things in a way that inflates trust:
Lire →A proud moment, earned with discipline and belief. But this win does not stand alone. AFCON U17 champions. CHAN champions. World Cup U20 winners. Women’s AFCON finalists. This generation has built a pattern of excellence, across ages and teams. That matters. Still, winning should never become routine. The moment victory turns into habit, sharpness fades. Real champions stay grounded, stay hungry, and respect how quickly the game can humble you.
Lire →What if content is no longer written, translated, tested, and optimised by people? What if this shift is already happening, quietly, inside real teams? We built CMSs for a human-led software world. We are now entering an agent-led world. That single shift breaks more assumptions than most realise. Traditional CMSs assume humans at the center. Humans create content. Humans review it. Humans translate it. Humans decide when it goes live and measure performance afterward.
Lire →Google’s recent work on Nested Learning caught my attention this week. It’s one of the few developments that genuinely feels like a shift in the foundations of how AI systems might evolve. Instead of a single, static model frozen after pre-training, Nested Learning introduces multiple learning loops operating at different speeds fast, medium, and slow. Similar to how the human brain balances instant experience with long-term consolidation.
Lire →A new audio demo just dropped, showing how natural the conversations can sound. Would you trust a voice like this to speak to your leads?
Lire →Something subtle is happening in AI and entrepreneurship. The question is no longer “should I use AI in my business”. Most founders already did, often through tools they did not design and barely control.
Lire →More parameters, more capability. More compute, more intelligence. This paper pushes back on that belief in a quiet but serious way. Instead of scaling the model, it scales the process. The core idea is simple to explain, and hard to ignore once you see it: Keep a current answer (y). Keep an internal latent “reasoning state” (z). Iterate. Repair z several times, use z to improve y, repeat. In other words, it tries to get depth from recursion, not from size.
Lire →GPT-5.2 just launched, and the headline writes itself: “the smartest generally available AI model in the world.” But headlines rarely tell the full story. On a demanding benchmark covering 44 real professions, GPT-5.2 now matches or outperforms human experts in roughly 71 percent of tasks. Law. Finance. Engineering. Medicine. The previous generation was closer to 38 percent. That jump matters.
Lire →“If you send enough messages, something will eventually work.” But in 2025, that approach is not only outdated it’s fundamentally broken. The truth is simple: Outreach doesn’t fail because of lack of activity. It fails because of lack of understanding. AI isn’t valuable because it can write text. That’s the shallow layer everyone focuses on.
Lire →Across industries I see the same pattern: more tools, more workflows, more dashboards, more “productivity”… and somehow, less clarity and less space to think. When I started building AI systems including what later evolved into JOTIQ and more, my goal was never to create systems that just send more emails or make more calls faster than humans. That’s relatively easy. And honestly, it often just accelerates the noise. The question that really interests me is different:
Lire →New models, new assistants, new creative tools, new reasoning systems. Image models that create cinematic scenes in seconds. Video generators turning simple prompts into full storyboards. Vibe-based coding assistants scaffolding applications before you even finish the sentence. Copilots in design, writing, meetings and operations. Voice agents that mirror natural conversation with surprising depth.
Lire →Then life arrives with its own lessons and suddenly the simplicity disappears. This visual reminded me of something I have seen again and again in both work and personal life. Not every connection carries the same meaning. Not every relationship deserves the same level of trust, openness or emotional investment.
Lire →From time to time, a piece of research surfaces that doesn’t compete for attention, yet quietly reshapes the way we think about intelligence itself. One such idea has emerged around a method called LADDER. What makes it compelling is not scale or speed, but the philosophy behind it: a model that learns not by brute force, but by constructing its own path toward understanding.
Lire →As moderator and speaker, I had the privilege to exchange ideas with some of the region’s most inspiring leaders as Hamid Bentahar, Rania Farmawi, Gilles Devendeville, Bouchra TAIBI, Othmane Ibn Ghazala, JS Anand, Youssef CHRAIBI, Hind Ghazi , nabil kouhen, Mohamed Benzakour and many others.. all united by one vision: building smarter, more sustainable, Morocco for the future. I left truly inspired by the passion, collaboration, and forward-thinking spirit driving Morocco and North Africa’s tran
Lire →Too often, I hear leaders say: “We need AI. Now.” But when I ask “For what purpose?” the room goes quiet. Many leaders know they need AI. They feel the urgency. But when it comes to why and how, the answers are often vague. The risk is rushing into AI adoption without a clear strategy, which can waste resources, frustrate teams, and fail to deliver value. The truth is: AI is not a magic wand. It won’t fix broken processes, unclear strategies, or poor leadership. If anything, it amplifies them.
Lire →Ik mocht vandaag op de Radio 1 in het programma 1Vandaag, mijn visie delen over de lancering van Allam (Arabic Large Language Model) – de nieuwe chatbot van het Saoedische bedrijf Humain. Daarnaast verscheen er ook een mooi artikel waarin mijn inzichten zijn meegenomen. Waarom dit belangrijk is? Omdat taal en cultuur niet los te koppelen zijn van technologie. In de Arabische wereld is er nog altijd een groot de behoefte aan modellen die onze taal en onze culturele nuances écht begrijpen.
Lire →We’ve officially crossed into strange territory. Everything in this video—voice, movement, expressions—is generated entirely by AI. And it’s not just realistic, it’s emotionally persuasive. These aren’t simple avatars. They’re digital beings performing human nuance with chilling accuracy.
Lire →Vanuit de prestigieuze studio van Dahbi Group in het prachtige Tanger, smaakte dit gesprek naar meer. We hebben uitgebreid gesproken over AI en de impact ervan op de samenleving, met speciale aandacht voor de groei van AI in Marokko. We zijn nog lang niet uitgepraat en de tijd was weer te kort. Daarom gaan we dit inshallah vaker doen. Blijf op de hoogte terwijl we samen fascinerende inzichten blijven verkennen en onthullen.
Lire →The workshops were particularly enlightening, delving deep into artificial intelligence applications and innovative urban development strategies. A heartfelt thank you to the Africa Smart City Forum team for the invitation and to all participants for their insights and collaborative spirit. Especially dear friend Dr. Hassan Radoine and my new friends at workshop Mohammed Khalil dear brother Dr. Baqer Al-Ramadan Mr. Hicham Meftah and many others.
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