Here’s something that might surprise you: the biggest bottleneck in your manufacturing AI strategy isn’t finding the right large language model—it’s what happens to your data before it ever reaches that AI. According to new insights from IIoT World, manufacturers are discovering that their document preprocessing systems are actually the most critical AI models in their entire stack.
Think about it from a practical standpoint. Your ERP generates thousands of documents daily—work orders, quality reports, maintenance logs, supplier communications. If your AI can’t properly interpret a maintenance report because the preprocessing mangled the technical specifications, or if it misreads a quality control document due to poor data preparation, then having the world’s most sophisticated LLM won’t help you one bit.
The Real AI Revolution is in the Details
This shift in focus makes perfect sense when you consider what’s actually happening on plant floors. The most successful manufacturing AI strategy implementations I’ve seen aren’t the ones with the flashiest chatbots—they’re the ones that figured out how to clean, structure, and contextualize their operational data effectively. It’s less glamorous than talking about ChatGPT for manufacturing, but it’s where the real value lives.
Speaking of real AI value, Embedded.com’s top 10 products of 2025 reveal that on-device AI processors dominated last year’s most popular tools. This trend toward edge AI computing is crucial for manufacturing environments where latency matters and connectivity can be unreliable. When your predictive maintenance algorithm needs to make split-second decisions about equipment shutdown, you can’t afford to wait for cloud processing.
Development Tools Catch Up to Industry Needs
The automotive sector is seeing significant toolchain improvements with IAR’s enhanced development capabilities for Renesas RH850 microcontrollers. The addition of cloud-enabled licensing, container support, and CI/CD integration might sound like IT buzzwords, but these features address real pain points for control system developers. Faster development cycles and better testing environments ultimately mean more reliable automation systems hitting production floors.
Even in specialized applications, advanced manufacturing capabilities are proving their worth. Apple Rubber’s rapid prototyping recently saved whale research in Alaska when critical equipment failed. While that’s not exactly industrial automation, it demonstrates how agile manufacturing processes can solve urgent problems—a capability that’s equally valuable when your production line needs custom components fast.
The takeaway here is clear: successful digital transformation isn’t about implementing the most advanced AI you can find. It’s about building solid foundations—clean data pipelines, reliable edge processing, and robust development workflows. Are you spending enough time on these unglamorous but critical infrastructure pieces in your own facilities?