Frontline Leadership in Manufacturing AI Adoption
A joint study from the Manufacturing Institute and PwC (2026)
Manufacturers are spending more on artificial intelligence, but the success of that investment will be decided on the shop floor. In the third year of our research series on the manufacturing frontline, the Manufacturing Institute and PwC surveyed 102 HR, operations and executive leaders in Q3 2025. We found that AI adoption depends less on the technology itself and more on whether frontline leaders, such as production supervisors, line leaders and team leads, are prepared to guide their teams through the change.
Key findings
- Readiness is lagging behind responsibility. 54% of respondents rate their frontline leaders’ ability to lead AI-driven change as low or very low, and none rate it high. Yet nearly half say these same leaders are highly effective at shaping the hourly worker experience, which is increasingly tied to retention and attendance.
- Adoption is still exploratory. 45% of frontline leaders and 62% of frontline workers are seen as skeptical of AI. Only 2% of leaders and 8% of workers have integrated it into daily work. Executive use is also limited, even though 74% of respondents call leadership the deciding factor in major initiatives.
- Training hasn’t kept pace. Just 19% of manufacturers offer any AI-related training for frontline leaders, and 17% never provide role-specific development. Respondents favor hands-on formats: in-person workshops and on-the-job learning are rated far more effective than self-paced e-learning.
- People and systems gaps compound each other. Employee resistance (72%) and lack of training (57%) are the top expected barriers. Inconsistent or siloed data also undermines trust in AI outputs. Frontline leaders worry more about insufficient training and unclear purpose than about job loss.
- Frontline voices are left out. 45% say AI initiatives failed partly because frontline leaders weren’t included in design or rollout. Only 3% say most frontline AI suggestions get implemented.
What manufacturers can do
- Lead curiosity. Model AI use from the top and make room for low-stakes experimentation.
- Deliver value. Tie AI to safety, quality and uptime, starting with visible early wins.
- Earn trust. Bring frontline leaders in early, act on their feedback and be transparent about how data is used.
Manufacturers that strengthen these leadership fundamentals will be best positioned to move AI from pilot projects to everyday operations.
