Stanford University’s 2026 AI Index offers two intriguing data points: 88% of organizations worldwide now use AI, and agent success rates on real-world computer tasks have climbed from about 12% to 66%. On the industry side, however, cases that truly enter production systems and connect investment with business returns remain relatively few. The growth in AI capability has not translated proportionally into enterprise productivity.
The deeper contradiction is that this wave of AI first transformed the information production layer—writing code, searching for information, and handling customer service conversations. These tasks mainly answer “what is known” and “how to express it.” The most valuable links in enterprise operations, however, deal with another class of questions: how much to produce, where to place inventory, how to schedule equipment, and in what order to fulfill orders. These high-frequency decision problems cannot be solved by generated content alone.
Decision AI is therefore drawing attention. It is not an entirely new type of model, but rather a renewed focus by industrial AI on deployment problems: large language models understand natural language, agents organize tasks, mathematical optimization and solvers handle complex constraints, and simulation and business systems validate and execute the results. As general-purpose model capabilities become standardized supply, vendors’ differentiation depends more on whether they understand production processes and whether they possess computable industry models.
From an enterprise perspective, the difficulty has long been stuck at the “last mile.” Optimization software is a typical professional tool: users must understand both the model and how it connects with business processes. Human language must be converted into computable content, separated by a complex layer of translation. Chen Yaoyu, product lead for Cardinal Operations’ Qingtian, noted that even faster-moving customers need considerable time for business staff to understand how optimization is used and how to embed it into existing systems.
AI is precisely compressing this translation layer, bringing computing power back to the fore. When a system must call back and forth among large models, solvers, simulation, and business systems, inference cost, latency, and resource scheduling directly affect whether it can be used continuously. This is why StarWar Cloud has kept focusing on enterprise AI deployment in GPU computing power platforms and computing scheduling: combining general-purpose models with specialized computing on a task-by-task basis, so every call is clearly accounted for and reliably supplied.
A port yard case illustrates