Emerson Deploys Generative AI to Accelerate Test and Measurement Workflows
By integrating 'Nigel AI' and a data-centric platform, Emerson aims to reduce bottlenecks in high-complexity engineering for semiconductors and EVs.
Emerson is expanding its artificial intelligence capabilities within its Test and Measurement (T&M) portfolio to accelerate product development and manage rising system complexity. By integrating generative AI into broader engineering workflows through tools like "Nigel AI," the company is shifting its strategy from basic AI assistance to comprehensive operational integration.
Emerson is implementing a "data-centric platform philosophy" that organizes four specific data types: measurement data, test system data, company IP, and product data. This approach allows the company to layer generative AI over modular hardware, utilizing the software legacy of National Instruments (NI) to optimize test solutions. Norma Dorst, VP of Global Marketing for NI Test and Measurement at Emerson, describes AI as a "virtual teammate" designed to assist rather than replace human engineers, noting that while AI accelerates development, it "does not remove the need for human judgement."
This transition comes as industrial products in aerospace, defense, and semiconductors grow increasingly complex. Engineering complexity is doubling roughly every six months, making traditional manual testing a significant bottleneck. To counter this, Emerson is promoting a "shift left" approach, which integrates testing much earlier in the design cycle. This strategy is specifically aimed at accelerating innovation in high-stakes sectors such as electric vehicles (EVs) and semiconductors.
By automating the orchestration of test workflows and data analysis, Emerson aims to drastically reduce time-to-market for critical technologies. The move represents a fundamental shift in the role of AI in the physical engineering process, moving it from a coding assistant to an integrated part of the validation pipeline. For the industry, this means a potential reduction in the friction between design and verification, where precision and reliability are non-negotiable.
As Emerson scales these AI-driven validation patterns, the focus remains on ensuring that existing customer hardware investments remain viable while adding modern intelligence. The company continues to monitor how the integration of company IP and product data into AI models can further refine the "shift left" methodology to keep pace with the accelerating rate of hardware complexity.