That's an excellent question that hits right at the heart of industrial AI implementation! The gap between those slick trade show demos and real factory floors is massive, and here's what I've found from real-world cases:

First, the data problem is huge. Those 20-year-old PLCs with only Modbus communication weren't designed for continuous data logging. They're basically sending simple on/off signals, not the rich sensor data AI models need. Companies like Boeing lost $12 million on an AI quality control system that couldn't pull defect data from their 25-year-old databases.

Second, integration without halting production is the real challenge. Trade show demos run on perfect, isolated systems. In reality, you're trying to add AI to equipment that can't be shut down. Successful companies use modular AI 'agents' - small, focused tools that handle specific tasks like vibration analysis or temperature monitoring, connected through APIs that act as translators between new AI and old PLCs.

Third, the security risks are different. Adding AI to old systems creates new vulnerabilities that those legacy PLCs were never designed to handle. And let's not forget the human factor - maintenance teams who've worked with these machines for decades need to trust and understand the AI recommendations.

The reality is that successful implementations start small, focus on specific pain points, and build up gradually. Companies like BMW and Siemens use cloud-based AI modules connected through APIs to their legacy systems, showing real improvements without major system changes.

So while the trade show demos look amazing, the real work happens in the messy, incremental integration that respects both the limitations of old equipment and the realities of continuous production.