← Back to Field Notes

December 15, 2025 · The Bloomfield Team

Manufacturing Technology Adoption Rates: 2025 Survey

Throughout 2025, we compiled technology adoption data from industry surveys by NAM, Deloitte, McKinsey, and our own conversations with manufacturers across the United States. The picture that emerges reveals an industry in transition: certain technologies are reaching mainstream adoption while others remain in early stages. The gap between what manufacturers say they want to implement and what they actually implement remains wide.

This report covers adoption rates for eight technology categories, the barriers that slow adoption, and the ROI data available from early implementers.

Foundation Technologies: ERP and CAD/CAM

ERP and CAD/CAM are now baseline tools. At 78% and 85% adoption respectively, they represent the infrastructure layer that most manufacturing operations take for granted. The remaining holdouts are typically very small shops (under 15 employees) running on QuickBooks and manual processes.

The more relevant question for these technologies is not adoption but utilization. Most manufacturers use 20 to 30% of their ERP's capability. The job tracking, purchasing, and invoicing functions are well-utilized. The reporting, analytics, and planning functions are largely unused. The value sitting inside these systems dwarfs what most shops extract from them.

Growth Technologies: Machine Monitoring and Digital Quality

Machine monitoring (34% adoption) and digital quality systems (41%) represent the current growth wave. Both technologies have moved past the early adopter phase and into early mainstream adoption. The ROI case for both is well-established: machine monitoring typically reveals 15 to 25% more available capacity than manual tracking suggests, and digital quality reduces documentation time by 50 to 70% while enabling trend analysis that paper-based systems cannot support.

The barrier to further adoption is integration complexity. Both technologies produce the most value when connected to the ERP, and most shops lack the internal expertise to build those connections. The vendors that solve the integration problem will capture the largest share of this growing market.

AI: High Interest, Early Adoption

At 22% adoption, AI in manufacturing has doubled from its 2024 level but remains in the early stages. The use cases driving adoption are concentrated in three areas: quoting assistance (44% of AI adopters), predictive quality (28%), and customer communication (22%). These three use cases share a characteristic: they work with data that already exists in the operation and deliver results that are directly measurable.

The 78% that have not adopted AI cite three consistent barriers. Uncertainty about where to start (63%). Concerns about data readiness (51%). Difficulty building a business case (47%). These barriers are falling as early adopters publish results and the entry points become clearer.

Knowledge Management: The Biggest Gap

At 14% adoption, knowledge management is the most underinvested technology category relative to its potential impact. With the manufacturing workforce aging curve accelerating, the gap between the importance of knowledge preservation and the investment in it represents the largest unaddressed risk in American manufacturing.

We expect this category to see the fastest growth over the next two years as retirements force the issue. The shops that build knowledge management infrastructure before the retirement wave peaks will have captured institutional expertise that their competitors lost permanently.

ROI Data From Early Adopters

Among manufacturers that have implemented AI tools for 12 months or more, the reported outcomes are consistent: 40 to 65% reduction in quote turnaround time, 12 to 20% improvement in win rates, 25 to 40% reduction in new hire ramp time (for knowledge management implementations), and 50 to 70% reduction in quality documentation time.

The shops that achieve these results share a common approach: they started with a single, well-defined use case, measured the baseline before implementation, and expanded only after proving results. The shops that attempted broad technology overhauls without this discipline reported lower satisfaction and longer time-to-value.

The data is clear. The technologies that deliver the highest returns in manufacturing are the ones that make existing information more accessible, not the ones that generate new information. The ERP data, the job history, the institutional knowledge, the quality records all already exist. The technology that closes the gap between having that data and using it is where the investment belongs.

For a deeper look at how these ideas connect across the shop floor, see our complete guide to AI in manufacturing.