According to itsupplychain.com, Gartner forecasts that global enterprise spend on supply chain management (SCM) software with agentic AI capabilities will grow from less than $2 billion in 2025 to $53 billion by 2030. This represents a compound annual growth rate exceeding 100% over the five-year period — underscoring a rapid, enterprise-wide shift toward AI agents embedded directly into core SCM workflows.
Rise of Simple and Advanced AI Agents
The forecast distinguishes between AI assistants — now widely available — and AI agents, which are gaining traction as the next evolutionary layer. Simple AI agents perform discrete supply chain tasks such as order status updates, exception flagging in freight tracking, or automated replenishment triggers. As Balaji Abbabatulla, VP Analyst in Gartner’s Supply Chain practice, explains:
“Simple AI agents are capable of executing discrete supply chain tasks, increasingly enabling organizations to automate routine workflows and freeing up bandwidth of humans to complete more complex tasks.” — Balaji Abbabatulla, VP Analyst, Gartner’s Supply Chain practice
Gartner anticipates that within the next 12–18 months, organizations will begin quantifying business value from these simple agents — prompting investment in clusters of coordinated agents capable of orchestrating multi-step workflows, either autonomously or with human oversight.
Shifting Procurement and Adoption Benchmarks
Procurement criteria for SCM software are evolving rapidly. Per Amarendra, Director, Research in Gartner’s Emerging Market Dynamics practice:
“Procurement criteria are also evolving, with AI assistant features becoming a mandatory requirement for SCM software selection, and AI agents a common requirement.” — Amarendra, Director, Research, Gartner’s Emerging Market Dynamics practice
This signals a structural change in vendor evaluation: AI capability is no longer a differentiator but a baseline expectation. Gartner projects that by 2030, 60% of enterprises using SCM software will have adopted agentic AI features, up from just 5% in 2025.
Deployment Lag and Operational Readiness
Despite accelerating vendor innovation, enterprise adoption will lag due to gaps beyond the technology layer. Abbabatulla emphasizes that successful scaling requires parallel advancement in four foundational areas:
- Data management maturity (e.g., unified master data, real-time event streaming)
- Operations management discipline (e.g., standardized process definitions, exception-handling protocols)
- Workforce AI-readiness (e.g., training in AI-assisted decision validation, prompt engineering for agent orchestration)
- Network-centricity (e.g., interoperability across tier-1 and tier-2 suppliers, shared data models)
He adds: “While SCM tech providers will be delivering AI agents of various denominations to retain their competitive position in a rapidly evolving software market, supply chain data management, operations management, AI-readiness of the workforce, and network-centricity need to evolve to enable deployment of AI-driven supply chain at scale.”
Practitioner Implications for Supply Chain Professionals
For supply chain professionals, this forecast translates into concrete near-term priorities. First, procurement teams must update RFPs to explicitly require AI agent architecture documentation — including agent autonomy levels, human-in-the-loop configuration options, and cross-platform orchestration support. Second, operations leaders should pilot agent clusters in high-frequency, low-risk workflows (e.g., daily demand signal reconciliation, carrier performance scorecarding) before scaling to mission-critical decisions like dynamic safety stock recalibration. Third, change management efforts must extend beyond training to include governance frameworks — defining escalation paths when agents propose actions outside predefined confidence thresholds. Finally, partnerships with platform vendors must be evaluated not only on AI feature sets but on documented success in multi-agent, multi-vendor environments — a key determinant of long-term scalability.
Source: itsupplychain.com
Compiled from international media by the SCI.AI editorial team.
