Analyst Insight: AI, already relied on for analytics and forecasting, is becoming the decision-making engine for enterprise operations. At the center of this shift is agentic AI: Agents that are capable of reasoning, deciding, acting, and learning autonomously. This shift is imminent. According to IDC, by 2031, 60% of Global 2000 CEOs will use agentic AI for strategic decisions, driven by market volatility, innovation speed, and the need for faster, intelligence-driven choices at the boardroom level.

Across global supply chains, a transformation is underway, and several forces are accelerating this evolution. Decision intelligence, which connects data, analytics and automation to run decisions as a continuous digital process, has matured, proven its value in supply chain environments, and is supported by a rapidly expanding ecosystem. Advances in agentic AI are also enabling greater transparency and adoption, streamlining cross-functional workflows and coordinating decisions across planning, procurement, manufacturing, logistics and customer fulfillment.

Together, this is giving rise to agentic decision intelligence, and the application of autonomous agents to the full lifecycle of enterprise decision-making.

For years, supply chain leaders relied on manual analysis and static reports to make operational choices. While these tools provided visibility, humans still had to interpret data, make trade-offs, and execute decisions across disparate systems.

Now, agentic decision intelligence is changing the model. Enterprises can now connect insights to action across functions and close the gap between knowing and doing. Recent IDC research shows 88% of firms are advancing decision intelligence initiatives, and 40% see AI agents as critical to faster, more accountable business decisions. In the same study, companies leading in decision intelligence maturity reported 17 points higher customer satisfaction and 34 points greater operational efficiency than their peers.

This transition won’t be frictionless. As AI takes a more active role in decisions that directly affect revenue, costs and service levels, trust and transparency become critical. Leaders must ensure explainability and auditability in every AI-driven action.

Talent will also need to evolve. New roles, such as decision architects and decision analysts, are emerging to design, govern and refine these systems. Traditional silos will give way to flatter, cross-functional structures as intelligence becomes embedded into operations.

Finally, fragmented tools and siloed data create decision-making challenges. Unified platforms, however, can fully unlock the potential of agentic AI and decision intelligence, by rapidly simulating scenarios, automating decisions, and capturing learnings to turn insights into scalable, repeatable action.

Supply chains will evolve from human-led systems supported by machines to human-guided systems powered by AI agents that can sense disruption, simulate outcomes, and act faster than any manual process.   

In its research, IDC found that within 18 to 24 months, 11% of enterprises expect agents to handle routine decisions autonomously, while another 20% anticipate agents managing most decisions under human oversight.

A life sciences leader, discussing his team’s decision intelligence roadmap, put it simply: “This is one of the few technologies that fundamentally changes how we work, moving us from reports and emails to operating decisions in one place, with speed, scale and accountability.”

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Outlook: Agentic AI represents the next chapter for supply chains, where intelligence isn’t a dashboard or an algorithm, but a continuously learning network that runs the enterprise. For industry leaders, the question is no longer if, but how quickly they can harness this transformation to stay ahead.