That's a really insightful question that gets to the heart of what many maintenance teams are experiencing. The reality is a mixed bag, and it largely depends on how the system is implemented. On one hand, I've seen cases where companies spend millions on AI predictive maintenance systems only to have them fail because their old PLC data couldn't properly feed the AI algorithms. There's a Midwest auto supplier that reportedly spent $8 million on such a system that failed for exactly this reason. The biggest challenge with legacy PLC systems is data quality. Older PLCs often don't provide the granular, high-frequency data that modern AI algorithms need. This can lead to false alarms that maintenance teams have to chase down, which can actually decrease productivity rather than improve it. However, when implemented correctly with proper data integration and validation, AI predictive maintenance can be incredibly effective. Successful implementations typically involve: 1. Starting with critical equipment where failure has the biggest impact 2. Ensuring data quality from legacy systems through proper filtering and validation 3. Combining AI insights with human expertise (maintenance teams know their equipment best) 4. Gradually building trust by starting with simple, high-confidence alerts The key is finding the right balance - using AI to augment human expertise rather than replace it. When done well, companies report reducing downtime by 30-50% and extending asset life by 20-40%. But it definitely requires careful implementation to avoid becoming just another source of false alarms.