The end-to-end AI chain emerges – it’s like talking to your company’s top engineer

Key Points:

  • Generative AI and numerical AI represent two ends of a continuum, with language models and numerical models converging to open up new realms for AI applications.
  • The collaboration of generative AI with numerical-based AI can enhance productivity and time-saving implications, providing insights and recommendations based on past scenarios and data analysis.
  • The integration of AI technologies across industries like manufacturing and winemaking offers a way to interact and query models, emphasizing the importance of collaboration between operational technology and information technology teams for efficient data utilization.


Generative AI is emerging as a powerful tool that complements traditional AI by enabling highly interactive verbal inquiries and bridging the gap between operational and information technology functions. Peter Zornio, Senior VP and CTO for Emerson, highlights that generative AI and numerical AI represent two ends of a continuum in the AI landscape. While numerical AI relies on datasets of numbers, language-based generative AI utilizes vast amounts of text and other content for its models. This convergence of the two AI approaches enables innovative applications in various industries, facilitating seamless interaction with AI systems.


Zornio emphasizes the collaborative potential of generative AI, likening its function to consulting an experienced expert within a company. By asking questions like “Why is production slowing down?” operators can receive actionable insights and recommendations based on historical data and scenarios. This approach not only enhances productivity and saves time but also serves as a valuable support system for product-related queries, consolidating manuals and support interactions into an accessible system.


The implications of applying end-to-end AI solutions extend across sectors such as manufacturing, including industries as diverse as petrochemicals, automaking, and winemaking. For instance, winemakers can leverage AI to analyze key factors influencing wine quality, such as temperature, sugar content, and fermentation duration, by posing specific questions to the system. This comprehensive AI framework offers a robust means of querying and interacting with data-generated models, ultimately acting as a valuable assistant in decision-making processes.


Furthermore, Zornio underscores the importance of collaboration between operational technology and information technology teams to leverage AI effectively. This collaboration necessitates data integration and alignment between the two departments to optimize the utilization of AI technologies. Establishing a seamless architecture for data exchange between operational and IT realms, possibly utilizing cloud-based AI models like OpenAI, is essential for driving successful AI adoption and realization of its benefits.



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