Submit

About

AI for Manufacturing is the most complete searchable database of real AI implementations in manufacturing. Built for plant managers, operations leaders, and engineering teams evaluating AI adoption.

What is AI for Manufacturing?

AI for Manufacturing is the largest open database of real AI implementations in manufacturing, with over 1,100 documented case studies. We catalog what companies have actually done with AI — the use case, the technology, and the measurable results — so that plant managers, operations leaders, and technology evaluators can make informed decisions based on evidence, not vendor marketing.

Our Methodology

Every case study in our database goes through a structured collection and verification process. We do not fabricate data, generate synthetic results, or accept unverified claims.

Data Sources

Case studies are collected from three categories of sources:

  • Vendor-published case studies — documented implementations from AI platform providers such as Rockwell Automation, Siemens, Cognex, and others.
  • Independent research — reports from the World Economic Forum, McKinsey, and industry publications that document specific factory-level implementations.
  • Community contributions — case studies submitted directly by vendors and manufacturers, verified by our editorial team before publication.

Quality Levels

Each case study is assigned one of three quality levels:

  • Verified — complete content with at least two quantified metrics, full taxonomy classification (industry, use case, AI technology), and a traceable source.
  • Contributed — submitted by a vendor or manufacturer, reviewed by our team, and published with attribution.
  • Scraped — programmatically collected from public sources. Contains structured data but may have shorter content sections.

Taxonomy & Classification

Every case study is classified across four dimensions: industry (13 categories), use case type (11 categories), AI technology (8 categories), and company size. This standardized taxonomy enables cross-comparison across implementations and helps surface patterns — for example, which AI technologies deliver the strongest ROI in specific industries.

Editorial Standards

  • Metrics are reported exactly as published by the source — we do not round, extrapolate, or reinterpret results.
  • Every case study links back to its original source when available.
  • We distinguish between vendor-reported results and independently verified data.
  • Case studies without quantifiable results are still included if they document a real implementation with a named company.

About Us

We are a small team focused on making AI adoption in manufacturing more transparent and evidence-based. Our background spans manufacturing operations, data engineering, and industrial AI deployment.

Have questions, corrections, or a case study to share? Feel free to reach out.