Manufacturers are investing heavily in artificial intelligence, but many projects fail to deliver expected value because tools aren’t fully woven into day-to-day operations, according to Machine Design’s interview with Matt Scavetta, chief technology innovation officer at Future Tech. Companies often buy powerful AI systems without preparing their workforce or processes to use them effectively, leading to “last-mile failures” where insights go unused or are misapplied on the factory floor.
One clear sign that investment outpaces readiness is when new dashboards and AI features are rolled out but workers revert to familiar manual habits such as spreadsheets and workarounds. This happens because teams haven’t been trained or given time to adapt, and the organizational change management needed to support new systems is lacking. In many cases, more budget goes to technology than to training and process redesign, leaving a disconnect between tools and the people meant to use them.
Scavetta points to broader industry data showing that around 80% of companies use the latest AI tools without yet achieving meaningful business outcomes, a pattern that plays out in manufacturing as well, especially where skilled roles are in short supply, and retirements are increasing staffing gaps.
Another challenge is merging information technology (IT) and operations technology (OT). While convergence can streamline systems, it can also complicate deployments if teams are siloed or unclear on shared goals, making it harder to integrate AI recommendations directly into workflows.
To avoid these pitfalls, Scavetta encourages manufacturers to balance AI spending with investments in people and change management, giving teams time to learn and adjust. He also emphasizes that meaningful AI success depends on treating data quality and process alignment as foundational, not optional steps, so that outputs can actually be trusted and adopted in real time.