AI for Mobile Inspection in Infor EAM

AI integration for Infor EAM Mobile Inspection focuses on the field data capture surfaces where technicians interact with assets. The primary touchpoints are the Inspection forms, Asset registers, and Work Order modules accessed via the mobile app. AI agents can be triggered at key moments: after a photo is taken, when a measurement is logged, or upon form submission. This allows for immediate analysis of unstructured data (like images of corrosion or text notes) against historical asset records and compliance standards stored within Infor EAM's core tables.

A typical implementation wires an AI service—via a secure API call or webhook—between the mobile app's submission queue and the Infor EAM backend. For example, a technician uploads a photo of a pump coupling. An AI model analyzes the image for misalignment or wear, cross-references the asset's maintenance history from Infor, and, if an anomaly is detected, automatically drafts a corrective work order with suggested priority, required skills, and linked parts from the BOM. This shifts detection and initial triage from a later desk review to a real-time field action, compressing the 'find-to-fix' cycle from days to minutes.

Rollout requires a phased approach, starting with a single inspection type or asset class to validate model accuracy and user feedback. Governance is critical: all AI-generated recommendations should be logged in the Infor EAM audit trail with a confidence score and be configured for optional human review before auto-creating work orders. This ensures technicians remain in the loop while gaining an intelligent copilot. For teams using Infor OS, the platform's middleware and Coleman AI services can streamline building, deploying, and monitoring these agents within the existing Infor ecosystem.

The integration is built on a secure, event-driven pipeline that connects the Infor EAM mobile app, Coleman AI services within Infor OS, and core asset and work management modules. The typical data flow begins when a field technician completes a mobile inspection, submitting structured data (e.g., condition codes, readings) and unstructured data (e.g., photos, free-text notes) via the Infor EAM Mobile API. This payload is routed to a dedicated AI processing queue. Our agents, deployed as microservices within your Infor OS tenant or a secure external environment, consume these events. They use computer vision models to analyze inspection photos for visual anomalies (corrosion, leaks, misalignment) and NLP models to extract and classify issues from technician notes, cross-referencing findings against the asset's history from the INFOR_ASSET and INFOR_WOHIST tables.

When the AI detects a high-confidence anomaly requiring intervention, it automatically constructs and submits a corrective work order via the Infor EAM REST API. This process involves: 1) Enriching the work order description with AI findings and evidence references, 2) Setting the priority based on asset criticality and anomaly severity, 3) Assigning a default craft and planning warehouse from the asset's master data, and 4) Attaching the original inspection record and analyzed images as related documents. The work order is created in a "Pending Review" status, triggering a notification to a planner or supervisor within Infor EAM's workflow engine for final approval and scheduling, ensuring human oversight is maintained.

Governance and rollout are critical. We recommend a phased approach, starting with a pilot asset type or location. All AI-generated actions are logged in a dedicated audit trail within Infor OS, tracing the decision from the original inspection ID through the model inference to the created work order number. This auditability is key for compliance and continuous improvement. The system is designed for zero-trust principles, where AI services have scoped, API-level permissions only to read inspection data and create work orders, never to approve or execute them. This architecture ensures the integration augments—rather than disrupts—existing Infor EAM governance, approval chains, and planner workflows, turning mobile inspection data into actionable maintenance intelligence in near real-time.

A production-ready integration layers AI securely atop your existing Infor EAM data model. The core architecture involves an event-driven pipeline: when a technician submits a mobile inspection in Infor EAM Mobile or Infor OS, the inspection record, along with any attached photos or notes, is queued via API or webhook. Our AI agents, hosted in your cloud environment, process this data—applying computer vision to photos and NLP to free-text observations—and return structured findings (e.g., "anomaly_detected": true, "confidence_score": 0.92, "suggested_priority": "High"). These results are written back to the inspection record in a custom object or extended field, triggering a conditional workflow in Infor Process Automation to generate a corrective work order, assign it, and notify the relevant planner.

Governance is enforced at every step. All AI-generated outputs are tagged with source data references and confidence scores, creating a full audit trail within the EAM's native history tables. Access to AI features and the ability to override automated work order creation is controlled via existing Infor OS roles and permissions. For high-risk or low-confidence findings, workflows can be configured to route the inspection for human review by a senior technician or supervisor before any action is taken, ensuring the EAM remains the system of record for all maintenance decisions.

A phased rollout minimizes disruption and builds trust. Phase 1 (Pilot): Deploy AI analysis in "shadow mode" for a single asset class (e.g., HVAC units). The system logs suggested actions without creating work orders, allowing planners to compare AI recommendations against their standard practice. Phase 2 (Assisted): Enable automated work order drafting for high-confidence, non-critical findings (like routine wear), but require planner approval before assignment. Phase 3 (Automated): Expand to broader asset types and enable full automation for pre-defined, high-confidence scenarios, while maintaining human-in-the-loop gates for safety-critical equipment. This measured approach de-risks the implementation and allows your team to calibrate the AI's performance against your specific operational standards.