Manufacturers are adopting AI faster than ever, but they still aren't seeing the full value of these investments. Though deployments have skyrocketed over the past two years, foundational roadblocks remain, most often as a result of governance, training, and system architecture that have failed to keep pace with implementation.

Key takeaways:

Three-quarters (72%) of global manufacturers have adopted AI, up from 53% in 2024.
Only 10% have adopted AI/ML and automation at scale, and just two-thirds (35%) have core systems like MES, QMS, and CMMS in place.
More than half (60%) of leaders worry more about being too hesitant on AI than too aggressive, despite AI being cited among the top cybersecurity risks facing the industry.

To uncover other trends taking place across our industry, explore our 2026 State of Manufacturing Report today.

How fast is manufacturing's AI adoption actually moving?

In short: very fast. The 2026 survey found that 72% of respondents now use AI within their operations — which is up from 53% in 2024. Less than 1 in 10 respondents (7%) say their processes remain primarily manual. There is perhaps no clearer signal that AI's experimentation phase is over. It's now an expectation across sectors.

There is a caveat, though: "adoption" isn't uniform. The 72% figure includes manufacturers who have integrated AI throughout their operations alongside those who have invested in a few tools here and there. The latter group accounts for a significant portion, too. Just about one-third (30%) say they have adopted across all departments, and 43% say they plan to do so. Just 10% say they are using AI/ML at scale, and just 35% have foundational systems, like MES, QMS, or CMMS.

These findings reveal an industry in transition, facing a defining challenge that is fundamentally different now than it was two years ago. Overcoming it requires a new approach to "AI-readiness," focused on integration, governance, and standardization rather than access.

Where is AI actually being used?

Unsurprisingly, genAI adoption is top of mind for leaders. All respondents (100%) say they have either already implemented this flagship technology or are actively preparing to do so. This year, nearly two-thirds (65%) say they have already started — a significant increase from the 48% who had done so in 2024. However, framing genAI as the focus of manufacturing's journey is somewhat misleading.

GenAI capabilities are present in nearly every manufacturing use case, but they serve more as a user interface than as the core technology. There is a growing understanding that the value is in analytics engines built from tailored models that feed genAI outputs. Manufacturers are now focused on tangible, analytics-first functions with measurable ROI, citing quality control (50%), IT operations (46%), and supply chain management (45%) as the most impactful applications within their operations.

The survey revealed that adoption lags in other areas, and many of these processes are ripe for transformation. AI use remains limited in core production workflows, real-time controls, and cross-functional orchestration. Closing this gap will hinge on efforts to mature data infrastructure and support AI integration at scale.

What's getting in the way of adoption?

Manufacturers' points of view on barriers to adoption have shifted significantly. Responses reveal that the conceptual roadblocks that defined early transformations have been replaced by more tangible concerns centered on operational, financial, and organizational considerations.

Respondents cited implementation costs (40%), data privacy/security concerns (39%), infrastructure and technical limitations (38%), and a lack of skilled talent/expertise (36%) as the most pressing challenges they now face. These barriers map directly back to the adoption-maturity gap noted earlier. Cost, infrastructure, and talent are exactly what separate having a tool from operationalizing it.

Complicating the situation is another gap, this time in communication and perception. Leaders and workers assume they understand one another's point of view on AI and adoption, but the numbers prove otherwise. Among leaders, only 25% are enthusiastic about AI, and 39% are cautious or resistant. More than half (60%) of leaders say they're more worried about being too hesitant with AI than too aggressive (vs. 40%).

Among employees, 72% are receptive, and just 28% are resistant — notably ahead of leaders in terms of comfort. Workers don't share their leaders' concerns about speed of adoption or operational changes; rather, they are focused on the implications for their futures. More than half (53%) say they believe AI could replace significant swaths of departments in the next three years.

The irony: worker concerns stem from the perception that leaders are more enthusiastic than they are, while leaders assume workers share their reluctance. Remedying this challenge is more about organizational management and culture than anything else. This is something that a significant portion of organizations could stand to pursue, as nearly half (45%) of respondents cited implementing adequate AI governance as a key barrier.

Conclusion

Manufacturers are past the question of "should we adopt AI?" The data says yes, decisively. The open question is whether cost, infrastructure, talent, and governance can keep pace with adoption speed and appetite. As such, the next phase of AI adoption and expansion will be as much about rethinking governance, training, and management practices as it is about deploying tools.

To learn more about Parsec's 2026 findings, click here.