Learning from Implementation Challenges Predictive maintenance initiatives fail more often than they succeed. Despite compelling ROI projections and executive enthusiasm, many programs stall during deployment, deliver disappointing accuracy, or simply get ignored by maintenance teams. After participating in predictive maintenance implementations across facilities operated by manufacturers like GE and Honeywell, I've seen the same mistakes repeated with frustrating regularity. These failures aren't due to inadequate technology. The tools for AI-Driven Predictive Maintenance are mature and proven. The problems are organizational, strategic, and operational. Understanding these common pitfalls helps maintenance teams avoid expensive mistakes and accelerate time-to-value. Pitfall 1: Starting Too Big, Too Fast The Mistake: Attempting to deploy predictive analytics across hundreds of assets simultaneously, often with unrealistic timelines driven by executive impatience or vendor promises.…