We analyzed industry data and real-world use cases to reveal where AI is working — and where the hype stops.

When manufacturers were asked about their primary goals for AI, cost and efficiency came out on top. That tracks: from back-office operations to the production floor, AI offers obvious opportunities to cut waste, reduce manual steps, and speed up workflows. Production cost reduction and product quality improvement follow close behind, pointing to a consistent focus on operational improvement. 

But the full picture is more strategic than any single priority. 

The range of goals across this data tells a more important story: manufacturers aren't expecting AI to solve just one problem. Revenue growth, business resilience, product innovation, and compliance all register as meaningful priorities. For companies competing on tight margins, efficiency gains aren't just about reducing costs. They also create capacity to reinvest in higher-value work, strengthen the customer experience, and protect the business against disruption. 

When asked where they are focusing AI efforts, manufacturers pointed to the plant floor first. At 56%, production and quality improvement is the most commonly cited area, reflecting the direct connection between plant performance and business outcomes. But the story doesn't stop there. Engineering, back office, front office, and R&D all show nearly equal levels of focus, signaling that AI is being deployed broadly across the organization rather than in isolated pockets. 

What's particularly telling is the breadth of functional coverage. 

Even areas that don't directly touch the customer—like accounting, order processing, and product development—are being targeted alongside customer-facing functions. This suggests manufacturers are approaching AI as an enterprise-wide opportunity rather than a departmental experiment. 

A clear signal from this data is that manufacturers are focused on augmenting human decision-making rather than removing humans from the equation. Better data, automated manual tasks, streamlined workflows, and eliminating data entry all rank at the top—reflecting a practical, near-term desire to free people from repetitive work and put better information in front of them faster. 

What's notably lower on the list is just as revealing. 

Fully automated, agentic AI—where decisions happen without a human in the loop—sits at the bottom of the priority list. Trust, experience, and process maturity need to develop before manufacturers are ready to hand the wheel entirely to an algorithm.  

Roughly half of manufacturers cite lack of knowledge, missing data science skills, and not enough time as their top challenges. These are capability gaps, not cultural ones. The message isn't that manufacturers are resistant to AI; it's that they're unsure where to start and don’t have the resources to move forward confidently.

What's notably absent from the top of the list is just as important.

Job loss fears, distrust, and resistance show up far lower than many might expect given the broader public conversation around AI. For solution providers, that's a meaningful signal: the opportunity isn't to convince manufacturers that AI is worth pursuing. It's to help them build the knowledge, skills, and infrastructure to actually do it.