Why AI is quickly becoming essential manufacturing infrastructure
What you'll learn:
- AI is moving from research labs into real-world manufacturing systems, enhancing materials discovery and plant operations. Investments such as Nokia's $4 billion expansion aim to develop AI-ready networks critical for Industry 4.0.
- The DOE's Genesis Mission seeks to transform U.S. manufacturing infrastructure as autonomous AI systems are being developed to be used in manufacturing processes in real time.
- AI-compatible chemistry platforms like Excelsior Sciences' are addressing data bottlenecks in drug discovery and small molecule manufacturing.
AI is becoming an operational force in manufacturing as it moves out of the lab and into real systems, while billions of dollars are flowing into the network infrastructure required to move the data that artificial intelligence requires reliably and in real time.
For manufacturers to adopt AI systems, they need data to feed algorithms to develop performance, uptime and reliability that depend on the effectiveness of physical assets, processes and connectivity in supporting AI.
DOE invests in AI, launches Genesis Mission
AI often is seen as a tool for accelerating science. What if it becomes a national infrastructure for designing the next generation of industrial systems?
The Genesis Mission could signal a structural shift for manufacturers and a future where materials, processes, and systems are increasingly developed through AI-driven experimentation rather than traditional engineering or trial-and-error.
See also: Roadmap to physically intelligent industrial operations
For maintenance and reliability professionals long-term, assets and systems designed with AI-enabled modeling may arrive with more predictable performance packages, better failure modeling, and data architectures built for lifecycle optimization out of the box.
The U.S. Department of Energy launched the Genesis Mission via an executive order from President Trump in late November, establishing a national effort led by DOE to transform American science and innovation through artificial intelligence.
The mission will mobilize DOE’s 17 national laboratories together with industry and academia to build an integrated discovery platform to harness AI and advanced computing to double the productivity and impact of U.S. science and engineering within a decade.
It aims to address three challenges:
- Energy dominance though advanced nuclear, fusion, and grid modernization.
- Advancing discovery science with DOE and industry investment in the quantum ecosystem.
- Ensuring national security with advanced AI technologies for security missions, safe U.S. nuclear stockpiles, and development of defense-ready materials.
DOE also commissioned the Anaerobic Microbial Phenotyping Platform at Pacific Northwest National Laboratory, a new AI-driven biotechnology platform for high-throughput microbial experimentation, to support Genesis Mission goals by enabling faster exploration, growth, and optimization of microbial systems using automation and AI, potentially transforming how biological research is conducted.
Excelsior Sciences raises funds to advance AI research
Excelsior Sciences has raised $95 million in funding to advance a novel chemistry platform designed to be compatible with machine automation and AI for small molecule discovery and manufacturing.
See also: Adoption of automation, AI-powered tools accelerating across sectors, survey shows
The company wants to reinvent the process of discovering and manufacturing small molecules by creating systems that use AI to automate chemistry, addressing challenges in drug discovery and manufacturing by enabling faster generation and testing of compounds. It aims to address the AI adoption bottleneck caused by a lack of real-world data.
Nokia expands U.S. R&D, manufacturing investment
Nokia also plans to expand its U.S. research, development, and manufacturing investment by $4 billion to accelerate innovation in AI-ready network connectivity technologies.
It aims to strengthen AI-optimized networking solutions and advanced technology areas such as automation, quantum-safe networks, semiconductor manufacturing, testing, packaging, and material sciences.
Rutgers engineers develop autonomous AI
Research led by Rutgers University engineers demonstrates autonomous AI systems that can improve expeditionary additive manufacturing and experimental discovery and has shown how AI can accelerate innovation in manufacturing by reducing the need for costly physical experiments.
See also: The strategic importance of industrial data fabrics
The research shows how autonomous AI is moving beyond analytics and into direct control of physical processes in manufacturing settings, addressing the challenge of process stability under non-ideal conditions.
Editor’s note: See our sister publication Plant Services for more of Anna Townsend's superb in-depth feature.
About the Author
Anna Townshend
Anna Townshend has been a journalist and editor for almost 20 years. She joined Control Design and Plant Services as managing editor in June 2020. Previously, for more than 10 years, she was the editor of Marina Dock Age and International Dredging Review. In addition to writing and editing thousands of articles in her career, she has been an active speaker on industry panels and presentations, as well as host for the Tool Belt and Control Intelligence podcasts. Email her at [email protected].