Emerson’s purpose-built industrial AI solutions are meant to enhance accessibility and reliability, enabling manufacturers to maximize efficiency and performance from automation systems.
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Emerson has announced a new AI-driven approach aimed at enhancing the reliability and performance of mission-critical manufacturing operations, including those in the pharmaceutical sector (1). The initiative builds on the company’s acquisition of Aspen Technology and leverages decades of domain-specific automation expertise, according to the company’s news release (1).
·Emerson's new Project Beyond platform supports deployment of AI tools across embedded, edge, and cloud systems in mission-critical environments.
·Localized, first-principles-based AI models aim to improve reliability, safety, and interpretability in industrial and pharmaceutical operations.
·Emerson's AI tools assist with system modernization, facility design, and sustainability planning, minimizing risks in regulated manufacturing settings.
At the Emerson Exchange 2025 (2) conference on May 20 in San Antonio, the company introduced Project Beyond (3), a software-defined, operational technology (OT)-ready platform developed to manage Emerson’s growing portfolio of AI models and applications. Designed for deployment across embedded, edge, and cloud environments, the platform integrates industrial AI with contextualized data to support operations where safety, reliability, and regulatory compliance are paramount.
AI has reached a critical stage in its evolution, with foundational models and open-source frameworks becoming increasingly accessible, Emerson noted in the release (1). While these developments offer potential benefits for automation and workflow optimization, Emerson notes that public generative AI (GenAI) tools remain unsuitable for industrial settings, where reliability and security are essential.
To address this gap, Emerson has developed an AI portfolio centered on localized, fit-for-purpose models (1). These tools, the company says, are built with embedded guidance grounded in first-principles physics and engineering, aiming to eliminate the types of inaccurate or unsafe outputs sometimes generated by general-purpose AI systems. Localized implementation also avoids the risks associated with transferring sensitive operational data to public cloud environments.
“Generic large language models are necessary for AI, but they are insufficient for a live, mission-critical industrial plant that cannot afford unreliable or unsafe results,” said Ram Krishnan, chief operating officer at Emerson (1). “Emerson’s solutions leverage industrial AI, built on decades of industry expertise and first principles constraints, to deliver correct, actionable guidance users can count on to make better operational and business decisions that will safely and rapidly drive competitive advantage.”
For pharmaceutical manufacturers, where automation plays a critical role in quality control, batch consistency, and regulatory compliance, Emerson’s suite of AI-enabled tools is designed to support high-availability applications (1). These include:
Additional virtual advisors within Emerson’s Ovation 4.0 Automation Platform and Guardian Digital Platform further support diagnostics and visualization of control system operations (1). These tools aim to reduce complexity for plant personnel and improve response times to system anomalies.
“Emerson is the only company with the wide range of domain expertise, deep stores of first principles models, vast array of fit-for-purpose technology solutions, and history of industry-shaping innovation necessary to deliver industrial AI tools that modern manufacturers can trust in a mission-critical environment,” Krishnan added in the news release (1). “With Emerson’s expertise, data and Boundless Automation vision, we are perfectly positioned to advance AI solutions that actually deliver on their promises.”
By focusing on AI models that align with specific industrial needs and constraints, Emerson aims to provide pharmaceutical and other manufacturers with tools that enhance automation reliability, reduce downtime, and improve operational insights without compromising data security or system integrity.
References
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