Bringing predictive analytics to the agentic AI era

将预测分析引入代理式 AI 时代

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.

到了 2026 年，企业 AI 面临的问题不再是预测模型能否胜过统计预测——这一争论早已尘埃落定。现在的核心问题是，如何让预测系统在不偏离商业意图的前提下，根据自身的结论采取行动。技术前沿已经从“预测”转向了“自主决策”，而领先者与落后者之间的差距也随之拉大。

“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.

研究公司 Everest Group 的合伙人 Vishal Gupta 表示：“企业已经厌倦了回顾性的视角；他们希望更具前瞻性。”

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.

由深度学习和生成式 AI 等技术驱动的智能分析正在使这一切成为可能。实时训练使 AI 能够持续进化，而无需等待季度更新。此外，新型预测引擎所依赖的数据范围已经扩大，不仅涵盖了整洁的数字记录，还包括了混乱、非结构化但蕴含丰富洞察的交互信息。因此，AI 驱动的分析正在推动企业从被动的“事后回顾”转向务实的“前瞻预见”。

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.”

AI 将预测分析——这一涵盖预测建模、数据准备、分析工作流、结果解读和决策应用等广泛领域的学科——提升到了新的高度。Gupta 认为：“在许多方面，我认为‘分析’一词正在让位于 AI。一切都在变成 AI。”