80% of manufacturers are exploring AI.1 Here’s how leaders are moving from pilots to measurable impact.
We see tremendous AI adoption across process manufacturing industries. The focus is shifting from experimenting with pilots to implementing AI in a way that delivers real business value. Leaders are now focused on how to get started and how to ensure a clear return on investment. Artificial Intelligence in Process Manufacturing: Preparing for an AI Futurea new manufacturing signals industry report published by Microsoft with research by IoT Analytics, presents insights into how manufacturers in process industries prioritize technology today and where AI fits into the picture. The report provides valuable insights for navigating the implementation of AI.
AI adoption is accelerating and entering a new phase
AI is gaining real traction in process manufacturing. Building on investments in Internet of Things (IoT), automation, and advanced process controls, manufacturers are focused on how AI can drive enterprise-wide decision-making and long-term value. This shift is no longer about if AI is worth pursuing—it’s about how to start effectively and drive measurable impact. As manufacturers move from pilot programs to broader deployment, the opportunity extends beyond task-level automation. AI is enabling predictive, real-time decision making across operations, research and development (R&D), and the supply chain—unlocking value that legacy systems can’t deliver alone. From my conversations with customers, the biggest barrier to generative AI isn’t the technology, it’s getting the data right.
This next phase of AI adoption depends on strong data foundations, grounded in enterprise data and context, with clear business alignment, and an organization-wide readiness to operationalize insights. Manufacturers that get this right are already seeing the results.
AI is supporting real business priorities
AI is helping manufacturers tackle two of their top business priorities: improving operational efficiency and driving revenue growth. By reducing waste, minimizing downtime, and optimizing output, AI-powered insights enable targeted operational improvements. The same data intelligence also fuels research and development (R&D), accelerates time-to-market, and uncovers opportunities for market expansion and business differentiation. One global chemical company reported that AI helped reduce the time-to-market for molecular enhancements from six months to just six to eight weeks1—a powerful example of how operational innovation translates into business acceleration.
The signals report also explores how industrial AI drives benefits beyond cost and throughput, from better data integration to improved customer satisfaction—ultimately enabling smarter, faster decisions across the value chain.
AI use cases with measurable business impact
The signals report surfaces real-world use cases where AI is delivering measurable results—not just technical improvements, but business transformation. From reducing downtime to accelerating product development, industrial leaders are applying AI in areas such as:
Process optimization
Sustainability, energy efficiency, and waste reduction
Research and development
Predictive maintenance and analytics
Adoption is scaling fast: 80% of manufacturers surveyed are either using or planning to adopt generative AI. These solutions are driving change across every level of the organization—from frontline operations to management decision-making.
A rubber and plastics manufacturer reported significant improvements to plastic design for more efficient production. A chemical company achieved a 90% reduction in demand forecasting costs and dramatically accelerated knowledge retrieval—enabling users to access answers in seconds instead of days.1 And in the words of one life sciences organization: “Our employees have more power to support farmers, help cure diseases and see consumers healthier.”1
These examples offer a compelling view into how industrial AI is already reshaping core operations, creating value well beyond the pilot stage.
Addressing security and complexity head-on
As more manufacturers embrace AI, leading organizations are not just navigating challenges—they’re building the strategies to overcome them. The signals report highlights two areas that require thoughtful planning: security and system complexity.
Security remains a key consideration. Nearly half of respondents say concerns around data protection—from IP theft to regulatory compliance—impact their AI adoption decisions. In industries where uptime, safety, and proprietary processes are critical, protecting sensitive data is non-negotiable.
Fortunately, security and AI aren’t mutually exclusive. Companies are investing in responsible AI practices, secure architectures, and governance models that enable innovation without compromising protection.
Complexity is the other major hurdle. Legacy systems often lack interoperability, and introducing AI may require adapting long-standing workflows. But many manufacturers are proving that modernization is possible—and that the payoff is worth it.
The signals report offers guidance on how to approach these challenges with the right foundation, so AI becomes a source of advantage, not friction.
Laying the foundation
Successful AI adoption requires a strong governance framework—it’s not about experimenting endlessly with every possible AI use case but rather focusing on the most strategic use cases that will deliver business value. Building this framework requires the right foundation to scale impact over time. Leading manufacturers are taking a structured approach: aligning AI investments to business goals, modernizing infrastructure, and investing in the skills needed to sustain innovation.
The signals report outlines four practical steps manufacturers are taking to move from isolated pilots to enterprise-wide transformation:
Identify business needs
Embrace structural flexibility
Get the data in order
Use AI to develop workforce capabilities
These are more than recommendations—they reflect what real manufacturers are doing to turn AI into a competitive advantage. And for many, AI is no longer optional, but essential to unlocking the next wave of efficiency, innovation, and competitiveness. The signals report brings each step to life with examples from the field.
Download the full report on Artificial Intelligence in Process Manufacturing to explore the research, benchmark your readiness, and take your next step toward AI-powered transformation.
Preparing for an AI future
Artificial Intelligence in Process Manufacturing
1 Artificial Intelligence in Process Manufacturing
Yury Gomez
Global Chief Commercial & Strategy Officer, Process Manufacturing Industry, Microsoft
Yury is the Global Chief Commercial & Strategy Officer for Process Manufacturing at Microsoft. With over 20 years of industry experience, she leads the GTM strategy and execution to help Fortune 500 manufacturers innovate at scale with AI and digital tech, driving end-to-end transformation and overcoming adoption hurdles to accelerate industry impact.
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