Experimental AI concludes as autonomous systems rise

The experimental phase of generative AI is coming to an end, making way for truly autonomous systems in 2026 that act rather than merely abstract.

2026 will lose focus on model standards and will be about agency, energy efficiency, and the ability to navigate complex industrial environments. The next 12 months represent a move away from chatbots toward autonomous systems that execute workflows with minimal supervision; Forcing organizations to rethink infrastructure, governance and talent management.

Autonomous AI systems are taking the lead

Although 2025 is determined by experience, next year represents “a critical pivot toward agentic AI, autonomous software entities capable of thinking, planning, and executing complex courses of action without constant human intervention,” says Haneen Garcia, senior communications engineer at Red Hat.

Communications and heavy industry are the testing ground. Garcia points to the path toward autonomous network operations (ANO), moving from simple automation to self-configuring and self-repairing systems. The business goal is to reverse the trend of commoditization by “prioritizing intelligence over pure infrastructure” and reducing operating expenses.

Technologically, service providers are deploying multi-agent systems (MAS). Instead of relying on a single model, these elements allow different agents to collaborate on multi-step tasks and handle complex interactions independently. However, increased autonomy poses new threats.

“As AI agents gain the ability to perform tasks autonomously, hidden instructions embedded in images and workflows become potential attack vectors,” warns Emmett King, co-founder of J12 Ventures. Therefore, security priorities must shift from endpoint protection to “managing and reviewing independent AI actions.”

As organizations expand autonomous AI workloads, they are hitting a physical wall: power.

King says it is the availability of energy, not access to form, that will determine the size of startups. “Compute scarcity is now a function of grid capacity,” says King, noting that energy policy will become the de facto AI policy in Europe.

KPIs must adapt. Sergio Gago, CTO at Cloudera, expects companies to prioritize energy efficiency as a key metric. “The new competitive advantage will not come from the largest models, but from smarter and more efficient use of resources.”

Horizontal co-pilots who lack industry experience or proprietary data will fail ROI tests where buyers measure true productivity. The “clearest enterprise ROI” will arise from advanced manufacturing, logistics, and engineering sectors where AI is integrated into high-value workflows rather than consumer-facing interfaces.

AI ends fixed application in 2026

Software consumption is also changing. Chris Royals, EMEA Field CTO at Cloudera, points out that the traditional concept of an “application” has become fluid. “In 2026, AI will begin to fundamentally change the way we think about applications, how they work and how we build them.”

Users will soon require temporary modules generated by code and prompts, effectively replacing custom applications. “Once that functionality has served its purpose, it shuts down,” Royals explains, noting that these “disposable” applications can be built and rebuilt in seconds.

Strict judgment is required here; Organizations need to see the heuristics used to create these modules to ensure that errors are safely debugged.

Data storage faces similar calculations, especially as artificial intelligence becomes more autonomous. Wim Staub, product marketing manager at Cloudera, believes the era of “digital storage” is over as storage capacity has reached its limit.

“AI-generated data will become disposable, created and updated on demand rather than stored indefinitely,” Staub predicts. The value of verified, human-generated data will rise as artificial content is eliminated.

Specialized AI governance agents will make up the shortfall. These “digital colleagues” will continuously monitor and secure data, allowing humans to “manage governance” rather than impose individual rules. For example, a security agent can automatically adjust access permissions when new data enters the environment without human intervention.

Sovereignty and the human element

Sovereignty remains a pressing concern for European IT. Red Hat survey data indicates that 92 percent of IT and AI leaders in EMEA view open source enterprise software as vital to achieving sovereignty. Service providers will leverage existing data center footprints to deliver sovereign AI solutions, ensuring data remains within specific jurisdictions to meet compliance requirements.

Emmett King, co-founder of J12 Ventures, adds that competitive advantage is moving from owning models to “controlling training pipelines and power supplies,” with open source developments allowing more players to manage workloads at border scale.

Workforce integration has become personal. Tools that ignore human nuances — accent, mood, personality — will soon feel outdated, says Nick Blasi, co-founder of Personos. By 2026, Blasi predicts, “half of workplace conflicts will be flagged by AI before managers even know they exist.”

Blasi suggests that these systems focus on “communication, influence, trust, motivation, and conflict resolution,” adding that personality science will become the “operating system” for the next generation of autonomous AI, providing a solid understanding of human individuality rather than general recommendations.

The era of the “thin envelope” is over. Buyers are now measuring true productivity, uncovering instruments based on hype rather than proprietary data. For the organization, competitive advantage will no longer come from renting access to the model, but from controlling the training pipelines and the power supplies that power them.

See also: BBVA integrates AI into banking workflows with ChatGPT Enterprise

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