AI Implementation Roadmap - Manufacturing Edition

Overview

Manufacturing AI touches the physical world. Predictive maintenance failures can miss critical warnings. Quality AI errors can release defective products. Robotics AI affects worker safety. Production optimization AI can disrupt output. You can't deploy manufacturing AI the way you deploy IT applications.

This 120-day playbook provides a realistic timeline for industrial AI deployment. It addresses OT integration, safety validation, and production continuity—the factors that make manufacturing AI different.

What's Inside

• Why Manufacturing AI Implementation Is Different: Comparison showing how manufacturing differs from IT implementation. Safety requirements, physical validation needs, and OT complexity that require different approaches.
• Implementation Framework: Three-phase structure designed for manufacturing environments

Phase 1: Foundation (Days 1-40)

• Use case definition with operations input
• Safety impact assessment
• OT architecture review
• Data readiness assessment (historian, sensors, MES)
• Vendor evaluation (if applicable)
• Security and network planning
• Production impact analysis
• Resource and team formation
• Governance approval

Phase 2: Pilot (Days 41-80)

• Limited pilot deployment (single line, shift, or area)
• OT integration implementation
• Safety validation testing
• Performance baseline measurement
• Worker training and feedback
• Production impact monitoring
• Refinements based on pilot learnings
• Scale-up planning

Phase 3: Production (Days 81-120)

• Phased production rollout

• Full worker training

• Go-live support

• Monitoring and alerting implementation

• Safety monitoring continuation

• Performance optimization

• Documentation and handoff

• Success measurement

• Use Case Playbooks: Specific guidance for common manufacturing AI applications:

  • Predictive maintenance
  • Quality inspection and defect detection
  • Robotics and automation
  • Production optimization
  • Supply chain and inventory AI

• OT Integration Guide: Connecting AI to operational technology—historian systems, PLCs, SCADA, MES, and sensor networks. Network architecture, security considerations, and protocol guidance.

• Change Management: Worker engagement, union considerations, training approaches, and overcoming resistance

• Success Metrics: Manufacturing-specific metrics including OEE impact, quality improvements, safety outcomes, and maintenance effectiveness

Who This Is For

• Manufacturing CIOs/CTOs implementing AI technology
• Operations Leaders deploying AI on factory floors
• OT/IT Integration Teams bridging environments
• Plant Managers overseeing AI deployment
• Project Managers implementing manufacturing AI

Why This Resource

Manufacturing implementation requires manufacturing-specific guidance. OT integration, safety validation, and production continuity aren't covered in generic IT implementation approaches. This roadmap understands factory floor realities—shift schedules, production pressures, and OT constraints.

Use case playbooks provide specific guidance for common manufacturing AI applications, not generic frameworks you have to adapt yourself.

FAQ

Q: Why only 120 days for manufacturing but 180 for healthcare?

A: Healthcare has FDA regulatory requirements and clinical validation processes that extend timelines. Manufacturing has safety requirements but typically fewer regulatory gates. Adjust timeline based on safety criticality and regulatory requirements of your specific use case.

Q: How do we handle OT security concerns?

A: The OT integration guide addresses security throughout—network segmentation, data diodes, secure protocols, and security architecture patterns for connecting AI to operational technology.

Q: What about pilot disruption to production?

A: Phase 2 is designed for limited pilot scope—single line, shift, or area—to minimize production impact while validating AI performance. Production impact analysis in Phase 1 identifies safeguards needed.