AI and Industry: The State of Production Health 2025

The manufacturing industry is navigating growing complexity as economic pressure, labor constraints, and digital transformation converge across operations. This year’s State of Production Health report from Industry Week and Augury explores how manufacturers are adjusting through smarter AI adoption, workforce enablement, and efforts to connect siloed systems. It offers insight into where the industry stands today and highlights the priorities shaping its future.

This year’s report explores how manufacturers are scaling AI across production environments during a period of economic volatility and digital transition. According to the third annual survey, based on responses from 500 global manufacturing executives, AI is maturing beyond pilots, and manufacturers are eager to scale it responsibly. Key findings include: 

AI Moves from Pilot to Practice

This year, more manufacturers are scaling AI across the enterprise: compared to 2024, three times as many report scaling 50 percent or more of their AI pilot projects. But while adoption accelerates, measuring impact is still lagging for many organizations. Machine health remains the most common AI use case, yet it ranks seventh when it comes to quantifying impact.

Confidence in AI is high—83 percent say they’re advanced or very advanced in applying it—but many of its most promising applications are still underleveraged or poorly understood. U.S. manufacturers trail European peers in leveraging AI for asset care, and ecosystem fragmentation continues to slow progress.

Workforce Development Lags Behind Urgency

Despite widespread agreement that AI will play a central role in workforce development, most manufacturers aren’t prioritizing it. Only 22 percent listed upskilling as a key objective for AI—ranking last in the survey in 2024 and 2025—despite 99 percent saying they expect AI to positively impact their efforts. 

As 2.8 million manufacturing workers near retirement, the industry faces a critical opportunity to preserve expertise and empower newer talent through accessible, user-friendly tools. So it’s not great news that resistance from frontline users and skills gaps in IT remain the top barriers to AI success. 

Sustainability Becomes Top Priority

For the first time, “meeting sustainability, ESG, and regulatory goals” ranked as the number-one objective for AI investment—up from ninth in 2024. Companies are increasingly using AI to reduce waste, optimize energy usage, and improve visibility into supply chains. 

But process optimization, which could help unlock measurable ESG improvements, remains one of the least utilized GenAI applications. Manufacturers are eager to reduce emissions and improve environmental performance, but often lack the operations and data to act quickly.

Looking Ahead

As manufacturers move past pilot purgatory and into enterprise-scale AI adoption, the focus must shift from deployment to impact. Despite ongoing economic uncertainty, 96 percent of manufacturing leaders express optimism about the future of the industry, driven in part by a growing belief that AI can be a strategic lever rather than just a technical solution. 

Yet while the pace of adoption is accelerating, measurable outcomes are still emerging. Machine health, while the most common use case, ranks far lower in terms of impact, and upskilling, despite being viewed positively by 99 percent of respondents, remains a bottom-tier AI objective in practice. 

This disconnect emphasizes the need for clearer success metrics and more targeted application of AI tools. Manufacturers that can align AI investment with production priorities like yield, sustainability, and workforce enablement will be best positioned to unlock real value.

The opportunity now is to treat usability, integration, and change management not as technical afterthoughts but as strategic initiatives. Digital change management was named the top limitation to hitting production targets in 2025, with ecosystem disconnects close behind. 

These barriers can halt progress before it starts, particularly for larger enterprises with more sites and more complexity. At the same time, challenges around workforce turnover and skill shortages are only intensifying. The path forward will require leaders to embed AI into the rhythms of plant-floor operations, prioritize cross-functional collaboration, and deliver tools that are not just advanced but intuitive.