US industrial manufacturing companies are investing aggressively in advanced technologies, with annual spending averaging nearly $233 million—well above the $184 million average across other industries. Yet greater investment has not translated into consistent returns. While 93 percent of executives expect technology to drive competitive advantage over the next three years, only 80 percent frequently realize improved value from their technology investments, compared to 89 percent in other sectors. The challenge is not innovation or funding—it is executing at scale.
Three critical moves for the next 12 months:
- Attack process and tech debt head on: Mergers and acquisitions have left many organizations with fragmented ERP systems and redundant platforms that hinder AI value realization. Accelerating system modernization and prioritizing legacy exit strategies will be critical to building a unified foundation for enterprise-wide scaling.
- Focus on impact, not experimentation: Move beyond pilot fatigue by shifting from scattered experiments to high-value production use cases with proven, scalable solutions. Prioritize initiatives that deliver measurable business outcomes and can be expanded across operations.
- Fix data before blaming AI: AI rarely fails—data does. Siloed, inconsistent, and low-quality data undermines even the most advanced models. Strengthening data infrastructure, consolidating ERPs, and establishing robust data observability are essential prerequisites for scaling AI effectively.
