Tech

Bringing predictive analytics to the agentic AI era

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Based on a feed excerpt; full article content has not been acquired. Source evidence retrieved 05/10/2026, 14:05:24 UTC.

Briefings

In 2026, enterprise AI shifted focus from static prediction to autonomous decision-making, driven by real-time training and expanded data sources including unstructured interactions. Research firm Everest Group partner Vishal Gupta notes that while predictive models now outperform statistical forecasts, the critical challenge is enabling systems to act independently without drifting from business intent. The report highlights a widening gap between leaders and laggards in adopting these capabilities.

Key points

• In 2026, the enterprise AI frontier shifted from static prediction to autonomous decision-making, where systems must act independently without drifting from business intent.
• Vishal Gupta from Everest Group states that enterprises are moving away from a backward-looking perspective toward forward-thinking strategies powered by real-time AI training.

Context and limitations

The report covers the transition from prediction to autonomous decision-making in 2026, but specific implementation timelines, adoption rates by industry sector, and quantitative metrics on the widening gap between leaders and laggards are not provided.

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