BEYOND THE BUZZ: AI
How to Prevent AI Agents from Becoming a Liability in Procurement By CHRIS VESSEY , VP of Innovation & Customer Value, ORO Labs
AI data governance essentials • Clear processes • Aligned roles • Standardized data models In regulated categories—like financial services procurement, pharmaceutical supply chains, government contracting, and cross-border trade—that gap carries legal and operational exposure that accrues quietly until an audit, a failed contract, a regulatory review, or a supplier dispute makes it visible. leaders need fast answers: what decision was made and who has authority to intervene. Most multi-agent environments deployed today cannot produce those answers reliably. should not expect agentic technology to build it for them. When an agent misroutes a high-value purchase order or applies outdated compliance logic mid-workflow, procurement Procurement leaders who want lasting results need to audit current workflow fragmentation before approving new agent deployments, map which decisions require human sign-o before any agent touches them, define escalation protocols for every automated decision point, and pressure vendors specifically on integration depth, including how agents access supplier data, receive process context, and hand o when judgment is required. Build the Command Structure Before Expanding the Fleet
Procurement leaders today are authorizing agentic deployments faster than their organizations can eectively govern them. The problem isn’t the agents themselves. It’s that most enterprises are not deploying them into coherent procurement architecture. Instead, they are layering them onto
fragmented workflows spread across disconnected systems, approval chains, and supplier processes. Each agent handles its piece of the process, but nobody is coordinating what happens between them: where one hands o to the next, who owns the exceptions, and what happens when something breaks. Without an embedded governing layer coordinating how those agents operate, each deployment adds more operational complexity instead of reducing it. The organizations seeing measurable returns from agentic procurement are the ones building governance and orchestration into the workflow before scaling automation broadly. Deploying Agents Without Oversight Will Cost You Building governance before scaling is what separates a controlled deployment from an unorchestrated one. Every agent added to an uncontrolled environment creates new oversight requirements, new failure modes, and new dependencies a human still has to manage somewhere downstream. Gartner’s research confirms that clear processes, aligned roles, and standardized data models are foundational to eective AI decision governance, and that organizations without that foundation
Resilience Hinges on Operational Context By PETER BUDWEISER , General Manager of Supply Chain, Celonis
investment in AI, many initiatives are falling short because they lack one critical component: operational context. AI can make eective decisions only if it understands how the business actually operates. Without visibility into bill-of- materials constraints, production line capacity limits, QA compliance gates, and unexpected port disruptions across the supply chain, AI risks generating
recommendations that are disconnected from operational reality. In other words, artificial intelligence without context is operating with major blind spots. The companies seeing the greatest resilience gains are not simply deploying more AI; they are grounding AI in operational understanding. With the right context, supply chain organizations can move from reactive firefighting to proactive orchestration.
As geopolitical instability, supplier shortages, shifting regulations, and economic
uncertainty continue to
intensify, resilience has evolved from a competitive advantage into a business necessity. Yet despite significant
32 Inbound Logistics • July 2026
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