Most AI pilot failures are organizational, not technical. This framework surfaces the five decision infrastructure gaps that account for the majority of manufacturing AI deployments that succeed in pilot and stall before production. Go through each section with the team that owns deployment — not the team that ran the pilot.

10–12 Ready for production deployment

7–9 High-risk deployment — address gaps before scaling

Below 7 Fix organizational infrastructure before turning the AI on

Decision Ownership

The most common gap and the least discussed. AI recommendations without named owners get reviewed in weekly meetings and quietly set aside.

Every AI recommendation type has a named owner who is accountable for reviewing and responding within a defined timeframe — not the team in general, a specific person.

Response SLAs are documented for each recommendation category — for example: stockout alert reviewed within 4 hours; supplier delay signal acknowledged within 24 hours; budget breach alert escalated within 2 business days.

An escalation path exists for overrides — who reviews the decision when the primary owner disagrees with the recommendation, and how is the override documented?

Workflow Integration

Friction always wins over insight. If acting on a recommendation requires switching context or logging into a separate system, it will be ignored.

AI recommendations surface in the channel where the decision already happens — the Teams channel, the mobile approval flow, the shift handover brief — not in a separate dashboard that requires a new login.

The decision-maker can approve or override without opening another system. If acting requires more than one context switch, the recommendation will be deferred.

Each recommendation includes the context needed to act — the signal, the data behind it, the proposed action, and the cost of inaction if the decision is delayed. Not just a flag.

Governance & Auditability

Experienced operators do not adopt recommendations they cannot interrogate. Trust requires a visible audit trail — not blind automation.

Every recommendation is logged with a timestamp, the assigned decision owner, the action taken (approved, overridden, deferred), and the outcome — creating a record the team can interrogate.

Every override is recorded with a reason. This builds a dataset for model improvement, makes override patterns visible to leadership, and establishes accountability without blame.

Data Foundation

The pilot used curated data. Production doesn't. Without a unified data layer, AI models degrade in production — and teams stop trusting the output.

ERP, MES, CMMS, and WMS data has been normalized into a unified schema designed for production data volume — not cleaned and curated for the pilot environment.

Data connectors are built to handle production-grade data quality: missing fields, inconsistent formats, delayed feeds, and concurrent writes — without manual intervention.

Success Metrics

"The pilot was a success" is not a production metric. Without pre-defined operational thresholds, there is no basis for knowing whether the system is working — or failing quietly.

Production success metrics are defined in financial and operational terms before go-live: PO cycle time from alert to approved PO, OTIF rate, unplanned expedite spend as % of COGS, stockout frequency per month. These are numbers a COO and CFO can measure weekly.

A measurement cadence (weekly or monthly) is in place to track these metrics against the pre-deployment baseline — with a defined threshold at which the system is declared underperforming and reviewed.

Your score

Count the checked items. If you're below 10, the gaps you identified are the reason most AI pilots stall between proof-of-concept and production. Each unchecked item is a specific organizational decision that needs to be made before go-live.

The article this checklist accompanies walks through each gap — what causes it, what it looks like in practice, and what the organizations that close it actually do differently.

Read the full article →

Running Dynamics 365 Business Central?

OpsGrid implements the execution infrastructure layer this checklist describes — decision routing, workflow-native recommendations, human-in-the-loop governance, and full audit trail — directly inside Business Central and Microsoft Teams. Currently in active beta with a focused group of mid-market manufacturers and distributors.

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The AI-to-Operations Checklist