The experimental phase of generative AI will end, paving the way for truly autonomous systems that function rather than simply summarize in 2026.
In 2026, the focus will be less on model parameters and more on agency, energy efficiency, and the ability to navigate complex industrial environments. The next 12 months will represent a transition from chatbots to autonomous systems that execute workflows with minimal supervision. Organizations need to rethink their infrastructure, governance, and talent management.
An autonomous AI system takes the wheel
Hanen García, chief architect of communications at Red Hat, argues that while 2025 was defined by experimentation, next year marks a “definitive tipping point toward agent AI, autonomous software entities that can reason, plan, and execute complex workflows without continuous human intervention.”
Telecommunications and heavy industry are the testing grounds. Garcia charts a trajectory toward autonomous network operations (ANO) that moves beyond simple automation to self-configuring and self-healing systems. The business goal is to reverse commoditization and reduce operating expenses by “prioritizing intelligence over pure infrastructure.”
Technically, service providers are deploying multi-agent systems (MAS). These allow different agents to collaborate on multi-step tasks and handle complex interactions autonomously, rather than relying on a single model. However, with increased autonomy comes new threats.
Emmett King, founding partner at J12 Ventures, warns that “as AI agents gain the ability to perform tasks autonomously, hidden instructions embedded in images and workflows become potential attack vectors.” Therefore, security priorities must shift from protecting endpoints to “managing and auditing autonomous AI actions.”
As organizations scale these autonomous AI workloads, they run into a physical wall: power.
King argues that the availability of energy, not access to models, determines which startups scale. “Right now, the computing shortage is dependent on the capacity of the electricity grid,” King said, suggesting that energy policy will become Europe’s de facto AI policy.
KPIs need to adapt. Cloudera CTO Sergio Gago predicts that businesses will prioritize energy efficiency as a key metric. “New competitiveness will not come from the biggest model, but from the most intelligent and efficient use of resources.”
Horizontal co-pilots that lack domain expertise or proprietary data fail the ROI test when buyers measure actual productivity. The “clearest enterprise ROI” comes from manufacturing, logistics, and advanced engineering, where AI is integrated into high-value workflows rather than consumer-facing interfaces.
AI will kill static apps in 2026
Software consumption is also changing. Chris Royles, Cloudera’s field CTO for EMEA, suggests that the traditional concept of an “app” is becoming fluid. “In 2026, AI will begin to fundamentally change the way we think about apps, how they function, and how they are built.”
Users will soon request temporary modules generated by code and prompts, effectively replacing specialized applications. “Once that functionality is done with its purpose, it is closed down,” Royles explains, noting that these “single-use” apps can be built and rebuilt in seconds.
Tight governance is needed here. Organizations need visibility into the reasoning processes used to create these modules to ensure that errors are safely fixed.
Data storage faces similar calculations, especially as AI becomes more autonomous. Wim Stoop, director of product marketing at Cloudera, believes the days of “digital hoarding” are coming to an end as storage capacity reaches its limits.
“Rather than being stored indefinitely, AI-generated data will become disposable, created and updated on demand,” Stoop predicts. Verified, human-generated data increases in value while synthetic content is discarded.
Specialist AI governance agents fill in the gaps. These “digital colleagues” continuously monitor and protect data, allowing humans to “take control of governance” rather than enforcing individual rules. For example, security agents can automatically adjust permissions without human intervention whenever new data enters the environment.
Sovereignty and the human element
Sovereignty remains a pressing concern for European IT. According to Red Hat survey data, 92% of IT and AI leaders in EMEA believe that enterprise open source software is essential to achieving sovereignty. Providers leverage existing data center footprints to deliver sovereign AI solutions and ensure that data remains within a specific jurisdiction to meet compliance demands.
Emmet King, founding partner at J12 Ventures, added that advances in open source are enabling more actors to run frontier-scale workloads, shifting competitive advantage from owning the model to “controlling the training pipeline and energy supply.”
Workforce integration is becoming personal. Nick Brush, co-founder of Persona, argues that tools that ignore human nuances such as tone, temperament, and personality will soon become obsolete. Blasi predicts that by 2026, “AI will alert you to half of workplace conflicts before managers even know they exist.”
These systems will focus on “communication, influence, trust, motivation, and conflict resolution,” Blasi suggested, adding that personality science will be the “operating system” for the next generation of autonomous AI, providing an evidence-based understanding of human personality rather than general recommendations.
Gone are the days of “thin wrappers.” Buyers are now exposing tools that measure real productivity and are built on hype rather than proprietary data. Competitive advantage for companies comes not from renting access to models, but from controlling the training pipeline and the energy supply that powers the models.
See also: BBVA embeds AI into banking workflows with ChatGPT Enterprise
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