If 2024 was the year of AI experimentation and 2025 was the year of the "utility" revolution and physical infrastructure reality checks, 2026 will be the year of the pragmatic reset. The "AI honeymoon" is officially over. Boards are no longer asking if AI works; they are demanding proof of ROI, ironclad governance, and physical infrastructure that can actually scale.
At Proxet, we are seeing these trends converge to redefine how we build software and enable businesses over the next 12 months:
The 2026 pivot from experimentation to execution
1. The agentic reality: from copilots to digital workers
Agentic AI will continue to be the buzzword, but the focus will be on integrating these agents into the company workflows making them real workers whose outputs can be measured and mapped to an actual business outcome closing the loop on the ROI topic. Companies are moving beyond chatbots that wait for a prompt to Autonomous Multiagent Systems (MAS) that independently execute end-to-end business processes with human in the loop at critical checkpoints. This transition also marks the rise of "role-based" AI, where digital workers are no longer viewed as mere tools but as an integrated digital workforce. The focus is shifting from simple task automation to the orchestration of complex, independent workflows.
2. Digital provenance: protecting your business from synthetic noise
2026 is the year "I didn't know it was AI-generated" stops being a valid excuse. As the web becomes saturated with AI-generated content and third-party code, digital provenance — the ability to verify the origin and integrity of data — has become a survival requirement. Gartner predicts that by 2029, companies failing to invest in provenance tools (like Software Bills of Materials and digital watermarking) will face legal and “sanction risks running into the billions of dollars.”
Beyond legal liability, digital provenance is important because unverified data leads to "Model Collapse," where AI systems trained on their own synthetic output eventually degrade into useless noise. For a business, this isn't just about avoiding a fine; it’s about ensuring the data fueling your decisions hasn't been "poisoned" by the very automation meant to scale it.
3. The infrastructure reckoning: atoms over bits
The software revolution has hit a physical wall. While software has been evolving at lightning speed, the physical world — data centers, power grids, and cooling systems — simply hasn't kept pace. The cloud-only model is still effective for many, but for companies with predictable, high-volume workloads, it is increasingly becoming a source of spiraling costs and carbon footprints that are no longer sustainable. If we don't change how we build, businesses will face a "triple-threat" crisis: monthly AI bills that spiral out of control, painfully slow response times that frustrate users, and models that start making more mistakes because they don't have the "brain power" to think clearly.
The tech space is seeing a move to strategic hybrid architectures: cloud for training and elasticity, but on-premises and edge for privacy, consistency and immediacy. Until now, most AI energy was spent training models (teaching them). In 2026, the focus will be shifting to inference (running them). The most valuable tech leaders aren't just experts in code; they are also experts in megawatts. Securing energy-efficient inference-optimized chips and grid-stable data center space has become the ultimate competitive advantage.
4. Using active deception to outsmart AI threats
In 2026, the traditional model of "detect and respond" will no longer be enough to protect organizations’ data from the speed of automated AI attacks. Our society is entering the era of preemptive cybersecurity, where the focus has shifted from building higher walls to predicting where the next breach will occur.
Rather than waiting for an alert, organizations are now deploying programmatic deception. This involves saturating the digital environment with "Honey-infrastructure" — decoy assets that are indistinguishable from real production systems. Industry leaders like Zscaler have pioneered this active deception approach by deploying GenAI decoys, such as fake LLM APIs and decoy chatbots, which act as a minefield for hackers.
5. Physical AI: the shift from industrial automation to autonomy
While industrial robotics has been the backbone of manufacturing for decades, traditional systems were often built for highly structured environments where every movement was predetermined. In these "deterministic" setups, even a minor deviation — like a part shifted slightly out of place — could halt production. Today, the Physical AI revolution is evolving these machines into adaptive partners through a sophisticated Perceive-Plan-Act-Learn (PPAL) cycle.
The "one-robot, one-task" era is coming to an end. Companies are now deploying General-Purpose Robotics Foundation Models (like NVIDIA’s GR00T or Skild AI). Much like LLMs can write code or poetry, these models allow a single robot to switch from part-sorting to heavy-lifting just by downloading a new "skill library."
Conclusion
As we move into 2026, AI is undergoing a critical transition: it is moving from the "laboratory" of experimental pilots to the "engine room" of core business operations. The fascination with what AI could do has been replaced by a focused, disciplined demand for what it is doing — measured by operational uptime, verifiable trust, and sustainable scale.
The benefactors of the pragmatic reset will not be the ones with the largest models, but the ones with the most resilient architectures. Success in 2026 belongs to the leader who can seamlessly orchestrate a hybrid workforce of humans and autonomous agents, secure their data supply chain through digital provenance, and navigate the physical constraints of a power-hungry infrastructure.
At Proxet, we don't just predict the future; we design and build the infrastructure to enable it. Reach out to schedule a meeting, and let's discuss how to align your 2026 roadmap with AI and your strategic goals.