Why AI Succeeds or Fails in Electronics Manufacturing

See why scalable AI depends on more than algorithms, and how MES, connectivity, and governed data create the foundation for production-ready intelligence.

Read white paper

Why AI Succeeds or Fails in Electronics Manufacturing

See why scalable AI depends on more than algorithms, and how MES, connectivity, and governed data create the foundation for production-ready intelligence.

Read white paper
White Paper

Is your manufacturing data ready for AI?

AI has the potential to improve quality, optimize production, support predictive maintenance, and accelerate decision-making in electronics manufacturing. But many initiatives stall before they scale.

The issue is rarely the AI model itself. More often, the problem is the data foundation underneath it: inconsistent identifiers, disconnected systems, machine data trapped locally, shallow data history, fragmented MES environments, and limited data governance.

This white paper, authored by Hugo Leite, Industry Manager of Electronics/SMT at Critical Manufacturing, explains why AI succeeds or fails in electronics manufacturing, and what platform, IT/OT, and MES leaders need to put in place before AI can deliver reliable value at scale.

Read the white paper to know:

  • Why AI projects stall when factory data is fragmented
  • The six data gaps that prevent AI from scaling
  • Why MES is critical as the central system of record
  • How connectivity unlocks machine and process data
  • How a data platform supports scalable, governed AI
  • A practical framework to assess AI readiness

Why this white paper matters

AI cannot scale on workarounds, manual exports, inconsistent data models, or disconnected systems. To make AI operational, manufacturers need data that is captured, structured, contextualized, accessible, and governed across the production environment. For manufacturing IT, OT, MES, and digital manufacturing leaders, this white paper shows why the foundation for AI starts with the systems that manage and connect factory data.

It explores how manufacturers can strengthen:

  • Data capture across machines, lines, and production systems
  • Standardization of identifiers, defect codes, and reason codes
  • Integration between MES, ERP, inspection, test, and equipment data
  • Accessibility of machine and process data for AI tools
  • Data governance for more reliable, trusted AI outputs
  • Scalability beyond isolated pilots and proofs of concept

AI should not become another layer of complexity on top of disconnected factory systems. It should become part of a connected, governed manufacturing ecosystem where data flows from the shop floor, gains context through MES, and supports intelligent decisions.

This white paper explains the foundation stack needed to move from fragmented data to scalable AI: MES as the system of record, connectivity to unlock machine data, and a data platform to make intelligence operational.

Download the white paper to see how electronics manufacturers can prepare their data, systems, and infrastructure for AI that works beyond the pilot stage.

Download white paper

Discover why AI succeeds or fails in electronics manufacturing, and how to build the data foundation needed for scalable, production-ready intelligence.

If you haven't received the resource in 24h, please reach out to the marketing team at [email protected]