Beyond the Buzz: Deploying AI in Your Supply Chain
AI Quick Tips
“You’ve got to start with consistent data and clear operating rules. And you can’t take people out of it. Artificial intelligence should support operators, not replace them. Keep it transparent, set guardrails, and treat it like a decision tool.”
–Keith Moore
CEO
AutoScheduler.AI
“Focus on governance, human-in-the-loop decision models, transparent KPIs, and continuous model validation, not just faster automation.”
–George Maksimenko
CEO
Adexin
“Treat AI as decision support, not a substitute for human judgment, and pair it with strong data governance, transparency, and regular human review.”
–Nishith Rastogi
Founder and CETO (Chief Executive and Technology Officer)
Locus.sh
“AI features are increasingly bundled into platforms and turned on by default. Ask key questions about data portability and how AI recommendations are explained to avoid inconsistencies or black box outputs becoming a liability.”
–Ben Hussey
Co-CEO
Katana Cloud Inventory
“Supply chains require context, experience, and judgment. Use AI as support, not a substitute, and keep humans accountable for outcomes. In some scenarios, like customs filings, it’s also required by law to keep humans in the loop.”
–Matthew Fotouhi
Chief Innovation Officer
MAGAYA
1. Clean, standardized data
2. Optimized end-to-end workflows
3. Visibility so teams trust the output
Source: Deepti Sharma
Chief Technology Officer
TrueCommerce
AI hurdles to Overcome
1. Poor data (incomplete, biased, or outdated)
2. Change management gaps
3. Lack of cross-functional integration
4. Unclear accountability
5. Disconnected systems
6. Undefined ROI
Go deeper:
Inspectorio State of
Supply Chain 2026 report
Preparing for Agentic AI
“Supply chain planning leaders should prepare for an agentic AI future, but they need to separate meaningful capability from market noise. The priority today is not full autonomy, but building the operational discipline, architectural flexibility, and decision frameworks that allow agentic AI to scale as the technology matures.”
–Jan Snoeckx
Senior Director, Analyst – Supply Chain Strategy and Planning
Gartner
“Create a knowledge document with everything that you know—what is important knowledge for your specific role—so that it can be given to an agent, and there are no context gaps when they are making decisions on your behalf.”
–Troy Lester
Co-Founder and CRO
Warp
Reality Check for AI Agents in Manufacturing Supply Chains
By Ross Meyercord, CEO, Propel Software
Agentic AI watches, assesses, and acts within the boundaries set using your business rules, your system configurations. It solves problems across workflows before they cascade into business disruptions that kill margins and strain customer relationships.
There are four key areas where agentic AI can positively impact manufacturers’ supply chain operations: predictive risk management, continuous supply chain monitoring, connected data infrastructure, and operational governance.
Why Fragmented Data Cripples AI Effectiveness
Agentic AI creates an uncomfortable reckoning. To optimize supply chain decisions, agents need simultaneous visibility into engineering specifications, quality history, supplier scorecards, customer commitments, and cost structures. If that data lives in five systems that don’t communicate, the agent’s effectiveness is diminished.
Competitive advantage goes to manufacturers whose data flows through connected infrastructure. If understanding the business impact of a supplier change requires manual work across multiple systems, you’re not ready for agentic AI.
The Foundation Agents Need to Operate Safely: Governance
Without proper infrastructure, agents make decisions based on patterns that miss critical business context. An AI agent analyzing supplier quality data might notice a compliance field gets completed less than half the time with no consequences. It concludes the field isn’t important and stops requiring it. But what if that field captures FDA-mandated traceability data that simply hasn’t been audited yet?
Successful implementations pair intelligent agents with platforms that enforce business rules and compliance requirements. Before deploying agents, establish governance protocols. Can you control which agents access sensitive supplier agreements? Do you have automated audit trails documenting agent decisions?
AI Outlook
The next few years will bring purpose-built agentic solutions that communicate through standardized protocols. A design change will automatically trigger agents across new product introduction, demand planning, supply chain, and procurement in a coordinated sequence.
Manufacturers must recognize that connected intelligence requires connected infrastructure. Companies chasing AI without addressing fragmented systems will find themselves managing unreliable automation that creates as many problems as it solves.
How to Prevent AI Agents from Becoming a Liability in Procurement
By Chris Vessey, VP of Innovation & Customer Value, ORO Labs
Procurement leaders today are authorizing agentic deployments faster than their organizations can effectively 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 off 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 effective AI decision governance, and that organizations without that foundation 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 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.
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.
Build the Command Structure Before Expanding the Fleet
Procurement leaders who want lasting results need to audit current workflow fragmentation before approving new agent deployments, map which decisions require human sign-off 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 off when judgment is required.
Resilience Hinges on Operational Context
By Peter Budweiser, General Manager of Supply Chain, Celonis
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 investment in AI, many initiatives are falling short because they lack one critical component: operational context.
AI can make effective 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.