Publication Announcement: 'Operational AI / Non-stop AI / Always-on Inference AI White Paper 2026 Edition - The Current State of AI OS / AI Ontology / Mission-Critical AI'

The Next Generation Social System Research and Development Organization (INGS) announced on March 25, 2026, the publication and outline of its 'Operational AI / Non-stop AI / Always-on Inference AI White Paper 2026 Edition - The Current State of AI OS / AI Ontology / Mission-Critical AI'.

■ Guidance from the White Paper Editorial Team

■ Key Message

The AI revolution is "just getting started."

At the core of all organizational activities, including businesses, is "Operational AI," which responds instantly to real-time changing data and situations to support optimal decision-making and actions. In 2026, AI is transforming into "Non-stop AI" – an "always-on intelligent infrastructure that continuously infers in real-time at the heart of enterprises, 24 hours a day, 365 days a year."

From an overarching perspective, AI primarily in language systems, such as LLMs and generative AI, is positioned as playing a partial role, mainly in human interaction. Generative AI learns from past data. Operational AI understands objectives, recognizes continuously changing data, infers, and makes decisions.

＜"Always-on Inference AI," which continuously performs situational awareness and decision-making by combining knowledge graphs and rule-based inference; "Non-Stop AI," which responds in real-time to data streams and business events; "Decision Ledger AI," which permanently records decision-making trails; and "Context AI"＞—all of these are transitioning towards integration and establishment as the "Operational AI" category.

Furthermore, as the "fusion of generative AI and these operational AIs" accelerates, the shift to an "inference economy" is reaching a critical phase, with market expansion from $79.8 billion in 2025 to $540.6 billion in 2033 (CAGR 26.1%) demonstrating this reality.

80-90% of enterprise AI spending occurs in the "inference phase." While AI inference costs have decreased by 1,000 times per token, total inference costs continue to rise. FinOps design and architecture selection that balances cost efficiency and performance has emerged as a central management challenge. The main battleground for AI costs is shifting from "training" to "continuous inference operation." Here, agent-based AI becomes the frontend for "Non-stop AI," making business automation, autonomous decision-making, and always-on inference the standard for enterprise operations. Furthermore, a "trinity configuration" of NVIDIA (infrastructure/chips) × Oracle (data platform/cloud) × Palantir (ontology/agent OS) is being established as the industry standard architecture for next-generation operational AI.

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■ Use Cases

In Finance/BFSI, for 24-hour real-time updates of AML, fraud detection, and risk models; in Manufacturing/Indu...