Collection
AI operations and decision infrastructure for manufacturing and supply chain
Most operations teams already funded the signal layer: forecasting, anomaly detection, alerts. The gap is everything that happens after the alert fires.
"Dashboards explain yesterday. When no one owns the next move, insight just sits there." (Sanjay Brahmawar, CEO of QAD, Auto Supply Chain Prophets, Jan 2026)
Frequently asked questions
What is decision infrastructure in AI operations?
Decision infrastructure is the execution layer that sits between AI signals and operational action. It governs which decisions get surfaced, routes them to named owners, enforces approval before any system write, and maintains a full audit trail. Unlike decision intelligence tools that recommend, decision infrastructure ensures the decision happens, with accountability.
Why don't AI signals convert to operational action?
AI signals surface the right information but stop short of execution. The gap is governance: no clear owner, no approval path, no audit trail, and no way to write back to the ERP without a separate manual step. Decision infrastructure closes that gap by embedding ownership, approval logic, and execution into the signal itself.
How does OpsGrid implement decision infrastructure for operations?
OpsGrid connects to Dynamics 365 Business Central, monitors ERP signals continuously, and surfaces ranked operational decisions to named owners inside Microsoft Teams. Each decision card shows the underlying BC data, recommended action, and requires explicit approval before any write executes in BC. No action happens without a human sign-off. That's enforced architecturally, not by policy.
What is the operational AI execution gap?
The execution gap is the lag between when an ERP system detects an operational risk (a stockout signal, overdue PO, OTIF threshold breach) and when a qualified person makes and records a decision. For mid-market manufacturers, this gap averages 2–3 days and costs $800K–$2.4M annually at a $50M operation through missed OTIF, emergency freight, and excess inventory.
What we mean by AI operations
AI operations is not the same as deploying AI. Deploying AI typically means connecting a forecasting model to your data, surfacing anomalies, or generating recommendations. AI operations means the governance model that determines what happens when a recommendation is generated: who reviews it, who approves it, at what cost threshold, with what audit trail, and how the approved decision reaches the system of record without a manual step.
The distinction matters because most mid-market AI deployments stall at the signal layer. Alerts fire. Dashboards update. Recommendations appear in a portal. And then a person has to translate that into action: an email, a purchase order, a meeting, a spreadsheet update. That translation step is where decisions slow down, accountability diffuses, and the AI investment stops paying back.
Decision intelligence tells you what to do. Decision infrastructure governs how it happens, who approves it, and ensures it gets done. The articles in this cluster cover both the conceptual architecture and the practical implications for operations teams running on ERP systems in manufacturing and supply chain environments.
Where does your own operation's decision cycle stall?