Bringing predictive analytics to the agentic AI era

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.

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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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This content was produced by Insights, MIT Technology Review’s custom content arm, not its editorial staff. It was researched and written by humans, with any AI tools that may have been used limited to production processes under human oversight.

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