Our whitepaper, “From Experimentation to Execution: Practical First Steps for Implementing AI in Manufacturing Operations,” examines why many manufacturing AI initiatives stall after pilots. While AI adoption has increased across the industry, many organizations struggle to move from isolated experiments to scalable, operational impact. The issue is rarely technology—it’s deciding where to apply AI first and how to integrate it into real manufacturing workflows.

This guide focuses on closing that gap. Rather than treating AI as an IT-led transformation, it outlines how manufacturers can apply AI pragmatically—starting with operational bottlenecks, working within existing MES, ERP, and PLM systems, and building a foundation that scales. The result is assisted decision-making, earlier risk detection, and more predictable operations without disrupting production environments.

What the Whitepaper Covers:

• Why AI adoption in manufacturing often fails when approached as a tool rollout rather than an operational capability
• The shift from AI experimentation to execution—and what “getting started right” actually looks like
• Core principles for applying AI without disrupting asset-heavy, process-driven environments
• Foundational AI use cases manufacturers can implement first, including planning, quality, maintenance, documents, and shop-floor intelligence
• How AI augments existing systems instead of replacing them
• The operational impact of AI when applied correctly—earlier insights, reduced manual effort, and improved reliability
• A phased adoption path that reduces risk while enabling scale
• Key criteria manufacturing leaders should use to evaluate AI solutions

Download the whitepaper to understand how manufacturers are moving beyond pilots and building AI into everyday operations—turning complexity into a structured, scalable advantage.