AI Redefining Enterprise Decision‐Making

International Data Corporation (IDC) reported that artificial intelligence is changing how enterprises make technology decisions. Findings were presented at the company’s Directions 2026 event. The study examines how organizations build, acquire, and deploy technology as AI adoption expands.

The Two Phases Of The AI Supercycle

IDC describes an AI spending cycle consisting of two phases: infrastructure buildout, followed by enterprise adoption. Early investment is focused on computing capacity, while later stages depend on integration into business processes. Meredith Whalen, Chief Product and Research Officer at IDC, said enterprises are still in early adoption stages despite increased spending.

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Economic Influence And The Rise Of Agentic Systems

IDC estimates AI could generate $22.5 trillion in global economic value by 2031. Growth is linked to productivity gains, new revenue models, and changes in business operations. The report also identifies a shift toward AI-driven purchasing processes, where automated systems influence decision-making and reduce reliance on manual input.

Beyond One-Size-Fits-All: Evolving AI Models

Enterprise AI is shifting toward multi-model and multi-agent systems. Organizations are adopting strategies to manage the selection, governance, and coordination of multiple AI tools. AI agents are increasingly used to automate processes, moving software from user-driven applications to systems that deliver outcomes with less manual interaction.

Strategic Adoption And Future Projections

IDC notes that value creation depends on how quickly companies move from testing to operational use. Workforce training and adoption of AI agents remain key factors. The report projects a transition by 2029 from training-focused models to large-scale inference integrated into enterprise systems.

Optimizing AI Investment And Measuring Value

IDC introduced the Agentic Business Value Maximisation Framework to help organizations assess AI use cases and measure outcomes. The framework focuses on prioritization and continuous evaluation. Around 42% of organizations report difficulty measuring AI performance and return on investment.

Conclusion

IDC data show continued expansion of AI adoption across enterprise operations. Execution and implementation remain key factors in determining outcomes. Organizations are expected to focus on deployment, measurement, and integration as AI use increases.