Investment in supply chain software is entering a new phase as companies back AI systems that can execute, not just analyze, operational decisions. A new Gartner forecast points to a sharp rise in spending as enterprises begin funding agent-based tools designed to run multi-step workflows across planning and execution.
From Task Automation to Workflow Orchestration
According to Gartner, spending on supply chain management (SCM) software equipped with agentic AI capabilities is projected to rise from under $2 billion in 2025 to $53 billion by 2030. The forecast reflects both a rapid increase in vendor offerings and a growing willingness among enterprises to fund more advanced AI-driven capabilities across planning and execution layers.
At the core of this shift are simple AI agents that can execute narrowly defined tasks, ranging from order adjustments to inventory checks, without continuous human input. These agents are increasingly being deployed to handle routine operational work, allowing teams to focus on exception management and higher-value decision-making.
Balaji Abbabatulla, a VP analyst in Gartner’s supply chain practice, notes that early deployments are already demonstrating measurable value. As those results become clearer over the next 12 to 18 months, investment priorities are expected to move toward clusters of agents working together across multi-step processes, effectively coordinating workflows that previously required manual intervention across systems.
Adoption Lags Capability as Operating Models Adjust
The expansion is not limited to basic automation. Gartner’s outlook points to a broader shift toward advanced agentic systems capable of managing interconnected workflows, adapting decisions in real time, and operating with varying levels of human oversight.
By 2030, Gartner expects 60% of enterprises using SCM software to adopt agentic AI features, a sharp increase from just 5% in 2025. This trajectory signals a transition from experimentation and pilot programs to scaled deployment embedded within core supply chain processes.
However, adoption is expected to trail the pace of technological availability. Many organizations are still constrained by fragmented data environments, legacy systems, and operating models not designed for autonomous or semi-autonomous decision-making. According to trade reports and industry disclosures, these structural gaps, particularly around data standardization and process ownership, continue to slow the integration of AI into end-to-end supply chain workflows.
This mismatch is already shaping investment patterns. Rather than pursuing full-scale transformation, many companies are sequencing adoption, starting with contained use cases where AI agents can deliver clear returns before expanding into more complex orchestration scenarios.
Control Systems Will Shape the Pace of Value Capture
As agentic AI expands across supply chain workflows, the rate of return will increasingly depend on how organizations structure control layers around these systems. Early evidence from AI deployments in adjacent enterprise functions shows that companies capturing consistent value have formalized escalation paths, audit trails, and decision boundaries alongside automation. In supply chains, this points to a shift in focus from scaling agent count to codifying how decisions are approved, overridden, and traced across procurement, planning, and execution. Those that treat governance design as part of the build, not a downstream compliance step, are more likely to convert growing software spend into measurable operational and financial outcomes.
