Webinar | Industrial AI: Turning Engineering Data Into Decisions

Every engineer has sat through an AI presentation that made it look effortless: a few prompts, a clean pipeline, and a dashboard with all the answers—industrial environments rarely look like that. Data lives in historians, supervisory control and data acquisition systems, enterprise resource planning platforms, spreadsheets, maintenance logs, and decades of PDFs. Much of it was never designed to be connected, and the context needed to interpret it is often missing or inconsistent.

Making AI useful in that environment starts long before selecting a model. It requires data that is reliable, well governed, and organized in a way that reflects how industrial operations actually work. Without that foundation, even the best models produce inconsistent or misleading results.

In this session, Conner Schiissler draws on his experience building data and AI systems in the midstream energy sector to explore what it takes to deploy AI successfully in industrial environments. Rather than focusing on large language models or the latest AI tools, the session examines the engineering work that enables them: improving data quality, establishing governance, and designing architectures that support operational decision making.

Attendees will leave with a practical framework for assessing their organizations' AI readiness, examples of where AI is creating measurable value in energy and industrial settings today, and a realistic understanding of the challenges organizations still face when moving from experimentation to production.

Join the Calgary Branch for this event.

Conner Schiissler is a technology generalist with experience spanning platform engineering, data engineering, data science, software development, and cybersecurity. Over the past seven years, he has worked across the full data and AI lifecycle, helping organizations design modern data platforms, build production-ready AI solutions, and scale data-driven initiatives.

Much of his career has been focused on the energy sector, where he has helped organizations modernize their data platforms, improve operational decision making, and adopt AI in practical, measurable ways. His experience spans industrial data, cloud platforms, data architecture, and machine learning, with an emphasis on solving complex engineering and business problems rather than pursuing technology for its own sake.

Today, Conner works at Databricks, where he helps customers build trusted data foundations that enable analytics, machine learning, and generative AI at enterprise scale. He is passionate about making emerging technologies approachable and helping organizations move from experimentation to production by focusing on strong engineering fundamentals and well-governed data.

This event applies to the following Work Readiness Program skill area: