AI 수요 예측 : 가까운 미래에 기대할 수 있는 것

This IDC Perspective examines how AI is transforming demand forecasting from model-driven processes to platforms that incorporate planner input, behavioral data, and agent-based execution. Many organizations continue to rely on manual intervention due to limitations in traditional planning systems and data models. This document outlines how CIOs can address these challenges by focusing on data readiness, selecting platforms with embedded AI capabilities, and adopting phased, outcome-driven implementation approaches."AI-driven demand forecasting is not just about improving models; it is about enabling organizations to translate insights into action through integrated planning and execution," says John Bermudez, adjunct research advisor for IDC's IT Executive Programs (IEP).

Executive Snapshot

• Key takeaways
• Recommended actions

Situation Overview

• Learning systems: Closing the gap between models and reality
  • What CIOs should do now to enable learning systems
• Data readiness: Moving from accuracy to behavioral enrichment
  • What CIOs should do now to improve data readiness
• From statistical forecasting to causal insight
• From planning systems to agent-executed workflows
• Impact on planning processes and skills
• Vendor landscape: Emerging capabilities in execution

Advice for the Technology Buyer

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• Related research
• Synopsis