What should leaders prepare for in 2026 when it comes to artificial intelligence (AI), after the explosion of LLM usage in 2024 and the rise of agents in 2025? At first glance, there are no signs of a new “monster” to tame… 2026 will be a year of technological maturation for companies and a return to the whiteboard for research labs. With this maturity comes the end of eight entrenched ideas that have shaped the corporate AI landscape for the past three years.

Misconception #1: The future belongs only to very large frontier models.

Nothing could be less certain. Yes, the battle between giant models is raging, perfectly illustrated by the recent “code red” triggered by Sam Altman following the enthusiastic reception of Gemini 3. But as early as 2023, Microsoft research teams had already published “All You Need Is a Textbook,” demonstrating—through code-generation experiments—that specialized models trained on carefully curated datasets could outperform general-purpose models with up to a hundred times more parameters.

Since then, examples have multiplied to the point that Gartner predicts that by 2028, small specialized models will hold 50% of the market. In 2026, leaders must explore the opportunities offered by smaller, more specialized models.

Misconception #2: The risk of hallucinations is a good reason to wait.

It is true that, according to Dataiku, 59% of executives encountered hallucination issues in 2025. But solutions exist. Models will always be capable of hallucinating, so the objective is to build full systems that ensure—though not zero risk—but reliability superior to current processes. To do this, enterprise data should be used either to fine-tune models or as a RAG-based knowledge source, with multiple models deployed in parallel to monitor drift and identify cases that require human oversight. Some companies are already using autonomous AI systems to interact directly with customers or streamline compliance processes in regulated industries such as healthcare and banking.

Misconception #3: No AI until all data is in the cloud.

More and more companies are choosing to deploy AI locally on their own servers, for regulatory reasons, codes of conduct, or in some countries due to limited access to cloud services. Open-source solutions exist to support this. This approach requires investments in more structured technical teams, but it can prove pragmatic and less costly. The entry into force of the AI Act in Europe will further strengthen this trend.

Misconception #4: Meta-agents will orchestrate swarms of agents—internal and external—to deliver massive productivity gains.

The scientific community is working tirelessly to stabilize agent-to-agent orchestration, and these solutions will eventually materialize. But in 2026, leaders should focus on deploying “deep” individual agents and refrain from heavy investments in self-orchestrated agent swarms just yet. One sign of ecosystem maturity was Anthropic’s recent donation to the Agentic AI Foundation of the MCP protocol for connecting agents to tools and external systems. Cognition, a research lab, warned us in a 2025 paper: “Don’t build Multi Agents.” I would add: “yet.”

Misconception #5: By “augmenting” employees, AI will have no impact on workforce size.

With the arrival of agents, the situation is slowly changing. In 2025, 30% of executives reported they expect to hire less over the next three years because of AI. Next year, leaders pushing ambitious AI programs will no longer be able to sidestep the topic.

Misconception #6: Quantum computing will always be something for the next decade…

The timeline for achieving true quantum advantage has stopped shifting further out. Roadmaps have now stabilized around building large-scale, error-tolerant quantum computers by 2030. And 2025 delivered the incremental but essential advances promised. IBM, for its part, announced a first real-world use case showing quantum advantage for 2026. Leaders can no longer ignore quantum technologies: they must start identifying opportunities in their industry and prepare experiments.

Misconception #7: Every company has already done a lot in cybersecurity.

Companies have indeed taken up the topic, but when executive committees brainstorm which use cases to prioritize, cybersecurity is still not emphasized enough. In 2025, 60% of companies experienced at least one AI-based attack, according to a recent BCG study. As attackers arm themselves with AI, defenders must do the same: automating cyber-defense will be essential in tomorrow’s cybersecurity arsenal.

Misconception #8: Artificial General Intelligence!

In 2024, Elon Musk claimed AI would surpass human intelligence by 2026. A prediction that today seems unlikely. Between Sam Altman—convinced he “knows how to build AGI”—and Yann LeCun—who believes we are still far from matching the intelligence of a cat—it is difficult to predict which vision will prevail.

Let us reread “The Bitter Lesson,” the 2019 paper by Rich Sutton, one of the founders of reinforcement learning. He reminds us that true breakthroughs rarely come from injecting human knowledge into models, but rather from systems that leverage ever-growing computational power to better grasp the world. Beyond human-curated data, the future of AI points toward systems that learn about the world through their own modes of perception.

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