Flux de travail autonomes
Les logiciels évoluent rapidement, suffisamment pour que de nombreux systèmes existants commencent à se sentir comme un poids mort. Nous nous dirigeons vers une nouvelle phase de l’automatisation des entreprises où l’IA ne se contente pas de suggérer des actions ou de compléter automatiquement les courriels. Elle gère des flux de travail entiers. Des ventes aux opérations de back-end, l’IA commence à gérer l’ensemble du processus avec une supervision humaine quasi nulle. En 2026, ce changement deviendra évident. Vous verrez moins de fonctions d’IA « agréables à utiliser » et plus de systèmes autonomes de bout en bout effectuant un travail réel.
Il ne s’agit pas d’ajouter un chatbot ou d’intégrer un modèle linguistique dans votre CRM. Il s’agit d’une réarchitecture fondamentale. Les startups natives de l’IA construisent différemment. Leurs systèmes sont structurés dès le premier jour pour automatiser et fonctionner à grande échelle sans s’appuyer sur des processus centrés sur l’humain. Résultat : elles avancent plus vite, intègrent les clients plus rapidement et proposent des solutions plus légères et plus adaptatives que les entreprises en place ont du mal à égaler. Si vos systèmes actuels n’ont pas été conçus sur la base de l’IA, le rattrapage sera coûteux et lent. Et oui, il s’agit là d’un véritable risque commercial.
Le marché récompense déjà les entreprises qui ne se contentent pas d’utiliser l’IA, mais qui sont l’IA. Leur délai de mise sur le marché est réduit. Leurs marges sont plus étroites. Leur capacité d’adaptation est intégrée à leur infrastructure. Pendant ce temps, les entreprises qui considèrent l’IA comme un ajout seront obligées de se reconstruire entièrement si elles veulent être compétitives. L’attente ne réduira pas ce coût, elle ne fera que l’augmenter.
Selon Thomas Cuvelier, associé chez RTP Global, « 2026 sera l’année des flux de travail autonomes, où les entreprises passeront de « fonctions d’IA » à des travailleurs d’IA gérant des processus entiers. » Cette prédiction correspond exactement à ce que nous voyons se dérouler : les entreprises en place sont poussées à réviser les piles technologiques existantes tandis que les perturbateurs natifs de l’IA font un bond en avant avec des produits rationalisés qui s’adaptent.
Si vous êtes à la tête d’une entreprise, examinez attentivement votre pile technologique. Peut-elle prendre en charge des processus autonomes ? Si ce n’est pas le cas, planifiez dès maintenant votre transition. En effet, lorsque le changement se fera à grande échelle, vous voudrez être à l’avant-garde, et non pas réagir à la traîne.
Capacités émergentes en matière d’IA
We’re stepping into the next phase of AI, where the tech becomes smarter, safer, and more collaborative. You’re not just looking at tools that run instructions. You’re looking at systems that learn from their own failures, store critical information securely, and coordinate across multiple tasks and teams in real time. For executives, this isn’t just technical advancement, it’s competitive leverage.
Startups are already moving fast here. They’re building self-correcting workflows, AIs that detect when something goes wrong and fix it themselves, without needing human prompts. That cuts out delays and builds resilience right into the operating system. Then there’s secure memory. This isn’t just about storing data, it’s about doing it responsibly. AI systems are now being designed to retain sensitive organizational knowledge while meeting high standards for data privacy, security, and compliance. If you’re in a regulated industry, finance, healthcare, logistics, this is critical. It’s the kind of capability that moves AI adoption from useful to essential.
Multi-agent collaboration is another major piece. Think multiple AI agents, each optimized for a specific function, working in sync with each other and alongside humans to handle bigger, layered problems. That coordination is what makes AI scalable across complex environments. It turns isolated use cases into systems-level performance.
These developments aren’t experimental. They’re coming to market now. Thomas Cuvelier from RTP Global pointed to these areas, self-correcting workflows, secure memory, multi-agent systems, as the big technical frontiers for 2026. He’s tracking early-stage companies commercializing this tech. They’re not selling ideas, they’re deploying products.
If you’re leading a business unit, these are the technologies you should be prioritizing. They’re not hype-driven. They’re infrastructure. And if these capabilities aren’t on your roadmap, you should ask why. Because the companies that adopt them early will operate faster, safer, and with more intelligence built into every layer of the business.
Redesigning workforce organization
AI isn’t just changing software. It’s changing how companies organize people, and that change is overdue. Traditional hierarchical structures don’t function well in fast-moving, AI-augmented environments. What’s working now is a shift toward teams built around capability rather than reporting lines. Skills come first, not static roles.
Orla Daly, Chief Information Officer at Skillsoft, made this clear: “Organisations that flex their workforce based on priorities, and build teams based on skills versus reporting lines, will be better equipped to meet the demands of a fast-changing, AI-enabled workplace.” That’s where the edge is, structure that adapts to what the company needs now, not what was defined five years ago.
One model that’s gaining traction is the hub-and-spoke approach. A central AI governance team defines data policies, risk standards, and frameworks. Business units act as spokes, quickly deploying AI use cases with local autonomy. That gives the organization precision and speed. At scale, this creates consistency in how AI is used while still empowering teams to iterate and solve actual problems. It also gives leaders clear accountability on both risk and execution.
But agility without oversight is chaos. Daly emphasized that strong guardrails are just as important as experimentation. Companies need the confidence to try new things, but they also need a clear process to test, validate, and scale what works. That kind of operational discipline is how you separate transient tools from transformational systems.
As AI tools get distributed across functions, from HR and finance to marketing and legal, you can’t afford disconnection. Siloed teams will miss out. Cross-functional alignment, driven by clearly defined capabilities and shared goals, will drive both learning speed and product velocity. And it reduces waste.
If you want your workforce to keep up with your AI strategy, don’t wait for HR to propose a model. Build team structures that reflect adaptability, clarity of purpose, and trust in individual ownership. The teams that operate like that are already outperforming, quicker decisions, tighter execution, lower friction.
Faits marquants
- Autonomous workflows are the next competitive edge: AI is shifting from feature-based tools to fully autonomous systems that manage end-to-end processes with minimal human input. Leaders should prioritize re-architecting legacy systems now or risk falling behind AI-native competitors by 2026.
- Emerging AI capabilities are redefining enterprise infrastructure: Self-correcting workflows, secure memory, and multi-agent collaboration are becoming operational requirements, not just innovations. Executives should invest in startups or internal R&D advancing these areas to ensure readiness for high-scale, low-error environments.
- Organizational models must evolve to unlock AI’s full value: Traditional hierarchies are too rigid for AI-driven operations. Decision-makers should transition to skill-based, cross-functional team structures with centralized AI governance to accelerate agility, maintain compliance, and drive system-wide adoption.
