Seventy-seven percent of manufacturers plan to increase AI investment over the next 12-18 months, with agentic AI—which can independently plan and execute tasks with minimal human intervention—gaining significant traction as 31% of AI-deploying manufacturers already use it. The shift reflects manufacturers' confidence in AI's productivity benefits, though workforce concerns about job security remain a critical consideration for successful adoption.
- 77% of manufacturers plan to increase AI budgets or resources in the next 12-18 months, with over half expecting 10-25% increases
- Agentic AI adoption is accelerating: 31% of manufacturers deploying AI already use agentic AI, which can independently execute tasks with minimal human intervention
- 64% of manufacturers have moved beyond exploration, with AI initiatives in pilots, operating, integrated, or broadly supporting business processes
- Workforce concerns persist: While 41% expect AI to boost productivity, 61% are at least somewhat concerned about career opportunities being limited
- 88% of manufacturers see value in a unified AI agent working across business applications, with 58% rating the concept very or extremely valuable
AI is moving further into manufacturers’ everyday operations, as nearly two-thirds of manufacturers (64%) have progressed beyond exploration, with AI initiatives in pilots or already operating, integrated, or broadly supporting business processes, according to Rootstock Software’s 2026 State of AI in Manufacturing Survey.
“AI’s potential isn’t only about what the technology can do—it’s also about how well employees are set up to adopt it,” says Rick Berger, CEO of Rootstock. “With AI, manufacturers have the potential to boost productivity and help employees deliver more value in their work, but realizing AI’s full value requires vision, training, and a commitment to keeping human judgment at the center of key processes. The companies that get this right are thinking about workforce development hand in hand with their AI strategy.”
Key takeaways:
· 77% plan to increase budgets or resources for AI-powered tools over the next 12–18 months, underscoring continued investment in the next phase of AI adoption.
· 41% of respondents expect AI to make them more productive, while 61% are at least somewhat concerned it could limit their career opportunities. The results suggest that as AI becomes more embedded in work processes, companies need to consider worker attitudes alongside AI investment.
· Agentic AI is gaining traction, with 31% of respondents deploying AI say they are already using Agentic AI, which can independently plan and execute tasks with minimal human intervention. Automation remains the most widely deployed form of AI at 52%, followed by Generative AI (49%) and predictive AI (40%).
· 20% plan to keep budgets unchanged and only 3% anticipate some level of decrease. Among those increasing budgets, more than half expect a 10–25% increase, and almost a quarter expect a 26–50% increase.
· 59% either use separate, specialized AI capabilities across applications or have not adopted AI broadly enough for cross-system integration to be relevant. At the same time, 88% see at least some value in a single, unified AI agent that works across business applications, and 58% rate the concept very or extremely valuable.
· 86% of manufacturers are at least somewhat confident their ERP can keep pace with AI-driven changes in manufacturing, but only 43% are very or extremely confident. ERP also remains among the Top 4 enterprise technologies expected to influence organizations’ ability to adopt and integrate more AI-powered tools.
· Supply chain management ranks as the top technology expected to drive further AI adoption, cited by 46% of manufacturers. Separately, 77% are already using AI, piloting it, or planning investment within the next 12 months to help manage supply chain disruptions and trade uncertainty.
· 73% prefer either human-directed AI or AI that can operate autonomously within defined limits and escalate when needed, while only 13% prefer fully autonomous AI. Governance is still developing. Employee training is the most common responsible AI practice at 40%, while 25% report not using any of the practices listed in the survey.
