Who said enterprises are done with backward-looking analytics?

Vishal Gupta, a partner at research firm Everest Group, said enterprises are done with a backward-looking point of view and want to be more forward-thinking.

What has changed about the data predictive engines use?

The data newer predictive engines rely on has expanded beyond neat, numerical records to include messy, unstructured sources of insight-rich interactions.

What replaced quarterly refreshes in these systems?

Real-time training allows AI to evolve continuously instead of waiting for quarterly refreshes.

Large Language ModelsAI Business & IndustryMIT Technology Review AI19h agoOct 6, 2026, 01:00 JST

Vishal Gupta: enterprises go forward-thinking as AI shifts

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3 Key Points

What happened
Everest Group partner Vishal Gupta says enterprises are done with a backward-looking point of view. The frontier in enterprise AI has moved from prediction to autonomous decision making.
Why it matters
Gupta's read is that the argument over whether predictive models beat statistical forecasts is settled. The remaining question is getting systems to act on their conclusions without drifting from business intent.
What to watch
The gap between leaders and laggards is widening, so the test is whether enterprises can keep autonomous systems aligned with intent. Gupta also says the word 'analytics' is giving way to AI.

WHO IT HITSEnterprise data and analytics teams at large organizations are the ones who will have to shift from quarterly forecast reviews to systems that act on their own conclusions. Executives setting AI strategy may feel the pressure most, given the widening gap between leaders and laggards.

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Context & Analysis

The article frames 2026 as a year when the debate over whether predictive models can beat statistical forecasts is already settled. What remains open is how to let predictive systems act on their own conclusions without drifting from business intent — a shift the piece describes as moving from prediction to autonomous decision making.

Two technical changes underpin that shift. Real-time training lets AI evolve continuously rather than waiting for quarterly refreshes, and newer predictive engines now draw on messy, unstructured sources of insight-rich interactions, not just neat numerical records. Gupta's comment that the word 'analytics' is giving way to AI reflects how broadly that label is being applied.

The stakes hinge on whether enterprises can close the gap the article says is widening between leaders and laggards. For teams whose work is built around periodic forecasts, the question is likely to be less about model accuracy and more about keeping autonomous systems pointed at the right business intent.

FAQ

Who said enterprises are done with backward-looking analytics?: Vishal Gupta, a partner at research firm Everest Group, said enterprises are done with a backward-looking point of view and want to be more forward-thinking.
What has changed about the data predictive engines use?: The data newer predictive engines rely on has expanded beyond neat, numerical records to include messy, unstructured sources of insight-rich interactions.
What replaced quarterly refreshes in these systems?: Real-time training allows AI to evolve continuously instead of waiting for quarterly refreshes.

MIT Technology Review AIRead Original Article

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