Responses to Rootstock Software’s 2026 State of Manufacturing Technology Survey depict an industry that is still investing, but with sharper priorities. Manufacturers report measurable gains in AI use and enterprise software adoption, even as uncertainty around customer demand, tariffs, and supply chain reliability forces leaders to weigh near-term performance benefits over longer, more complex transformation efforts.
“Manufacturers aren’t slowing down on digital transformation, but they’re being more selective about the initiatives they move forward with,” said Rick Berger, chief executive officer of Rootstock Software. He noted that many companies are increasing investment in enterprise systems that improve responsiveness, planning confidence, and operational control in volatile conditions.
AI Investment Shifts Toward Planning and Execution
The survey found that 94% of manufacturers now use some form of AI, underscoring how widely these tools are embedded across operations. At the same time, the data shows a more targeted pattern of adoption. Nearly three-quarters of respondents, 73%, believe they are on par with or ahead of peers in AI usage, suggesting that competitive differentiation is increasingly tied to how AI is applied rather than whether it exists.
Predictive AI recorded the largest adoption gain, rising 12 percentage points to 48%. Investment momentum also shifted toward execution-oriented use cases. Spending on AI for supply chain planning jumped 19 points to 35%, while process optimization climbed 11 points to 36%. These moves reflect a focus on forecasting accuracy, throughput, and responsiveness rather than experimental or peripheral applications.
Economic conditions are shaping these priorities. Manufacturers enter 2026 with a mixed demand outlook: 31% expect customer demand to decline, compared with 19% anticipating growth. Tariffs remain a material concern, with 39% expecting higher raw material or component costs and 29% citing increased difficulty in cost forecasting. As a result, AI investments are increasingly evaluated through their ability to support scenario planning, sourcing decisions, and cost visibility under volatile trade conditions.
ERP Consolidation Emerges as a Core Enabler
Alongside AI adoption, the survey highlights a growing emphasis on platform simplification and process consolidation, particularly around ERP systems. Nearly half of respondents, 49%, identified simplifying infrastructure and standardizing application platforms as the most important ERP outcome, the highest-rated response overall.
This shift reflects the operational friction many manufacturers face. Thirty-three percent cite a lack of the right talent as a primary barrier to progress, while 31% point to insufficient cross-department collaboration. These constraints are pushing companies to reduce system complexity and data silos, rather than layering new tools onto fragmented environments.
That dynamic is also evident in spending plans. Sixty-one percent of manufacturers expect to increase enterprise software investment over the next 12 months, with the largest group planning increases of 11% to 25%. Despite macroeconomic uncertainty, these commitments suggest that ERP and core platforms are increasingly viewed as foundational to workforce productivity and cross-functional alignment.
“Manufacturers are increasingly looking to ERP as a way to consolidate platforms and bring fragmented parts of the business together,” said Ohad Idan, vice president of product at Rootstock. According to Idan, expectations are rising for ERP systems to unify data across sales, operations, and supply chains, creating a common foundation for forecasting, decision-making, and employee effectiveness as AI use expands.
Where Capacity Constraints Shift From Machines to Data
A growing body of industry reporting shows that manufacturers able to translate AI and ERP consolidation into meaningful gains tend to share one operational characteristic: tighter control over how information moves through their networks. As supply chains become more sensitive to tariff swings, customer mix changes, and shorter planning cycles, execution bottlenecks increasingly stem from slow or inconsistent data handoffs rather than physical limitations on equipment or labor. The next phase of modernization may hinge less on adding new digital capabilities and more on redesigning the connective tissue, data governance, version control, and cross-functional ownership, that ensures those capabilities work under real operating pressure.
