如何将预测分析带入智能体 AI 时代
Bringing predictive analytics to the agentic AI era
企业 AI 现在不再争论预测模型能否超越统计预测，关键是如何让预测系统自主决策且不偏离业务目标。实时训练和扩展的数据源使 AI 从被动后见之明转向务实先见之明，将预测分析提升至新高度。AI 正让“分析”一词逐渐被“智能体”取代，一切都在成为 AI。
In 2026, the question for enterprise AI is no longer whether predictive models can outperform statistical forecasts—that argument is settled. The big question now is how to enable predictive systems to act on their own conclusions without drifting from business intent. The frontier has moved from prediction to autonomous decision making, and the gap between leaders and laggards is widening accordingly.
“Enterprises are done with a backward-looking point of view; they want to be more forward-thinking,” says Vishal Gupta, partner at research firm Everest Group.
Intelligent analytics, powered by technologies like deep learning and generative AI, are making this possible. Real-time training allows AI to evolve continuously instead of waiting for quarterly refreshes. In addition, the data that newer predictive engines rely upon has expanded to encompass not just neat, numerical records but also messy, unstructured sources of insight-rich interactions. As a result, AI-powered analytics are moving enterprises from passive hindsight to pragmatic foresight.
AI takes predictive analytics—a broad discipline that includes predictive modeling, data prep, analysis workflows, interpretation of results, and decision-making applications—to new heights. “In many ways I think the word ‘analytics’ is giving way to AI,” says Gupta. “Everything is becoming AI.”
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来源: MIT Technology Review · AI · technologyreview.com