A 360-Degree View of AI in Manufacturing
Artificial intelligence (AI) has transformed the modern manufacturing landscape, promising everything from predictive maintenance to next-generation robotics.
To truly grasp the breadth of this revolution, we spoke with experts representing diverse roles and perspectives: Elias Brown, Data Manager at Vallourec; Dr. Hasan Poonawala, Professor of Robotics at the University of Kentucky; Chancellor Jungwoo Ryoo and Director John Williams, Penn State; Shree Parikh, Business Development Manager at the US Center for Advanced Manufacturing; Konrad Konarski, Chairperson of the AI Applied Consortium; and Michael Burgess; Vice President of Operations. Each contributor offers unique insights, covering data-driven optimization, workforce development, cross-industry collaboration, and the physical infrastructure that supports AI.
Together, they present a 360-degree view of how AI is reshaping manufacturing.
A Focus on Efficiency and Cost Reduction
“In the rapidly evolving landscape of modern manufacturing, AI and generative AI are emerging as transformative tools to enhance efficiency and reduce costs,” says Elias Brown, Data Manager at Vallourec.
Brown’s front-line experience in data analytics highlights how AI can streamline production.
“AI-driven predictive maintenance is one of the key applications in manufacturing. By analyzing data from sensors and machinery, AI can predict equipment failures before they occur, reducing downtime and maintenance costs. Generative AI, on the other hand, can design optimized components and products, leading to material savings and faster production cycles.”
This move toward data-driven operations replaces guesswork with continuous streams of sensor information. Manufacturers anticipate breakdowns, prevent costly disruptions, and keep production lines operating at peak efficiency. Generative AI, meanwhile, enables rapid prototyping and swift product iterations, allowing new designs to reach the market with unprecedented speed.
Security and the Human Element
Despite AI’s clear operational benefits, Brown stresses that security and human capital remain pivotal.
“Ensuring robust security measures is paramount. AI systems must be designed with strong cybersecurity protocols to protect sensitive data and prevent cyber threats. This includes implementing encryption, access controls, and continuous monitoring to safeguard manufacturing operations.”
He also underscores the importance of upskilling employees.
“Rather than displacing workers, AI should be leveraged to upskill the workforce. Training programs can help employees develop new skills to work alongside AI systems, fostering a collaborative environment. This approach not only enhances productivity but also empowers workers, ensuring they remain integral to the manufacturing process.”
Shree Parikh, Business Development Manager at the US Center for Advanced Manufacturing, says the real power of AI in manufacturing isn’t just automation—it’s augmentation.
“AI doesn’t replace human intelligence; it amplifies it, transforming workers into innovators and factories into ecosystems of continuous learning. The future of manufacturing belongs to those who harness AI not just to cut costs, but to create new value, enabling predictive insights, mass customization, and sustainable production at an unprecedented scale."
In other words, AI serves as a tool that augments human capabilities. With the right training, employees become innovators who use AI-driven insights to make smarter decisions on the factory floor.
Next-Generation Process Control
Dr. Hasan Poonawala, Professor of Robotics at the University of Kentucky, focuses on merging AI algorithms with data-driven models of manufacturing processes. He emphasizes the importance of multimodal large language models as a groundbreaking approach to advanced manufacturing.
“Multimodal large language models are an exciting technology that can empower workers to easily manage advanced manufacturing processes by connecting their goals and observations with expert process knowledge,” he said.
By incorporating streaming sensor data, audio, camera feeds, machine telemetry these AI systems dynamically optimize parameters for additive and subtractive manufacturing. Poonawala envisions a future where human operators and AI collaborate seamlessly.
“These models will enable worker-driven modifications to the process during deployment, paving the way for Industry 5.0 in advanced manufacturing processes.”
This synergy promises consistent product quality, reduced material waste, and faster turnaround times.
Building the Future Workforce
Chancellor Jungwoo Ryoo and Director of Labs John Williams from Penn State emphasize that tomorrow’s workforce must be AI-literate and ready to innovate.
“Youth workforce development focusing on manufacturing in general and AI in manufacturing in particular is crucial for ensuring the industry remains innovative, efficient, and competitive in the face of rapid technological advancements.”
They highlight Penn State DuBois’ Industry 4.0 Academy, a program targeting high school students and covering AI, robotics, additive manufacturing, and cybersecurity.
“Investing in youth development programs, like Penn State DuBois’ Industry 4.0 Academy, allows the manufacturing sector to tap into a fresh talent pool adept in AI technologies, ensuring a sustainable workforce.”
Such initiatives equip young professionals with problem-solving, critical thinking, and leadership abilities, skills essential for adapting to rapid technological change.
Fostering Innovation and Cross-Industry Collaboration
Konrad Konarski, Chairperson of the AI Applied Consortium, focuses on building collaborative ecosystems that accelerate AI adoption across sectors.
“At the AI Applied Consortium, we strive to create a collaborative ecosystem where diverse stakeholders pool their expertise to address common challenges in AI adoption. Our goal is to foster knowledge exchange, provide resources for research and development, and catalyze new solutions that can be quickly scaled across industries,” he said.
Konarski stresses the urgency of digitizing operations, even for companies not yet ready to adopt advanced AI tools.
“In today’s fast-evolving environment, the velocity of innovation demands that manufacturers take immediate steps in their digital transformation journey. Each day they sit on the sidelines, they leave data on the table, data that can eventually be harnessed for advanced AI solutions. Over time, this inertia can seriously erode an organization’s future competitiveness.”
A Holistic View of AI Infrastructure
Michael Burgess, Vice President of Operations at the AI Applied Consortium, calls attention to the physical infrastructure supporting AI. While data centers and energy-efficient cooling systems are fundamental to large-scale processing, Burgess believes manufacturers can play a critical role in creating next-generation infrastructure.
“American manufacturers are in a unique position to lead not just in AI adoption, but also in the physical infrastructure that powers AI. We’re not just looking at how manufacturers can use AI; we’re examining how they can produce the next generation of energy-efficient, scalable, and modular AI infrastructure components, ensuring the U.S. remains at the forefront of AI-driven industrial innovation, he said.
By aligning manufacturing operations with the production of AI hardware, companies can spark a cycle of innovation that boosts competitiveness and fuels further technological advancement.
Conclusion
From predictive maintenance and generative design to workforce development and specialized AI infrastructure, the impact of AI in manufacturing is both expansive and deeply integrated. Elias Brown envisions data analytics and generative AI dramatically improving efficiency, while Dr. Hasan Poonawala looks ahead to worker-driven AI models that minimize defects. Chancellor Jungwoo Ryoo and John Williams highlight the need for youth-focused education to ensure a steady pipeline of skilled innovators, and Konrad Konarski points out the mounting opportunity cost of failing to digitize and collect data today. Shree Parikh emphasizes that AI is not just about automation but about empowering manufacturers to harness real-time insights, drive smarter decision-making, and foster a culture of innovation. Finally, Michael Burgess underscores how AI infrastructure, both physical and virtual, is becoming a manufacturing priority.
Taken together, these perspectives offer a 360-degree view of AI in manufacturing. Rather than focusing on a single dimension of automation, cost savings, or technology, leading voices emphasize a synergistic approach that integrates people, processes, security, and infrastructure. By balancing immediate operational gains with a forward-looking strategy, manufacturers can lay the groundwork for long-term resilience, innovation, and competitive advantage.