AI Adoption Is Universal, but Full Integration Is Rare

The study found that 100% of manufacturing leaders surveyed said their organizations are using AI in some form, ranging from small pilots to deployment across multiple processes. Predictive maintenance and quality control are the most common use cases, reflecting a focus on near-term operational gains.

Yet widespread use has not translated into enterprise-wide transformation. More than half of manufacturers (56%) said AI is implemented only in select areas, and just 10% reported that AI is fully embedded across their operations. The gap highlights a central tension: manufacturers are investing aggressively in AI, but most have not yet scaled it across engineering, production, supply chain and commercial functions.

Integration and Data Quality Are the Primary Barriers to AI at Scale

The biggest obstacles to AI adoption are no longer technical ambition, but system complexity. Nearly one-third of manufacturers cited software platform integration as a top challenge, while others pointed to workload prioritization and data management issues.

Manufacturers continue to operate with sprawling IT environments built on legacy systems, resulting in fragmented and inconsistent data. The study emphasizes that enterprise-ready AI depends on clean, structured and integrated data, something many manufacturers still lack. Poor data quality not only limits AI’s effectiveness but introduces risk, as incomplete or inaccurate data can undermine model accuracy and decision-making.

As a result, manufacturers are increasing software budgets to address these issues. More than three-quarters of respondents said their software spending increased over the past year, driven largely by the need to support automation initiatives.

Talent, Not Technology, May Be the Tightest Constraint

While manufacturers are investing heavily in AI tools, the study shows that workforce readiness is lagging. Globally, more than one-third of manufacturers said their teams are most in need of AI skills. The need is even higher among U.S. manufacturers, in particular, with 44% of respondents citing this as a key issue.

This shortage compounds integration challenges, according to the report, as organizations lack the internal expertise needed to deploy AI responsibly, manage models and extract value from advanced analytics. Manufacturers that fail to close these gaps risk underutilizing their technology investments or creating operational blind spots.

In response, manufacturers are prioritizing upskilling existing employees, staying current on technology trends and establishing stronger product and data backbones. Leaders expressed confidence in their ability to support Industry 5.0 goals, but the study suggests that success will depend less on buying new tools and more on building the skills and data infrastructure to use them effectively.

Revalize commissioned TEAM LEWIS to survey 500 decision-makers who use or oversee CPQ, PLM and engineering modeling or simulation software at manufacturing companies with at least 100 employees. The survey included respondents in the United States, Austria, Germany and Switzerland. The survey was conducted in fall 2025. Respondents were recruited by OpinionRoute, a global market research panel provider.