Hey there! As someone who's been through this exact struggle, I can tell you the gap between vendor promises and reality is pretty significant. Vendors often sell this vision of plug-and-play AI magic that'll revolutionize your maintenance overnight, but the reality is much more complex. First, the technical challenges are real - legacy PLC systems weren't designed for AI integration. You're dealing with limited processing power, memory constraints, and communication protocols that weren't built for streaming data to ML models. Vendors promise seamless integration, but you end up wrestling with data extraction from proprietary systems, dealing with inconsistent data quality, and figuring out how to get real-time inference working within deterministic PLC scan cycles. Then there's the data challenge - vendors talk about 'smart algorithms' but don't mention you need months of historical failure data, which most plants don't have systematically recorded. You're often stuck with incomplete maintenance logs and sensor data that wasn't collected with ML in mind. The ROI justification is another gap - vendors promise massive savings, but management wants to see concrete numbers before investing in legacy equipment upgrades. You're caught between proving the value while dealing with the technical debt of older systems. The good news? It's doable with realistic expectations. Start small with pilot projects, focus on overlay solutions that don't require replacing existing PLCs, and build your case gradually. The key is understanding that this is an evolution, not a revolution - and being prepared for the real work that vendors don't always mention!