Manufacturers today are not short on data, but many are short on clarity. Plants are saturated in dashboards, alarms and KPIs, yet both operators and leaders still struggle to answer basic production questions: Why is this line underperforming today? What changed? What should we do next? In many cases, increased visibility has paradoxically led to more confusion, not better decisions.
This workshop explores why data alone is insufficient for AI-driven manufacturing and why data contextualization is the missing link between insight and action. Led by Chris Stevens, President of US Automation for Siemens Digital Industries and Caleb Eastman, Field CTO for Siemens Digital Industries, the session will examine how contextualizing data across assets, processes and people fundamentally changes how AI systems reason, recommend, and act on the shop floor.
Participants will discover:
- What “data in context” means in real manufacturing environments, beyond better visualization
- Why capturing context at the edge, including operator and process knowledge, is critical, especially in brownfield environments
- How differing perspectives between control engineers and data scientists create friction and how aligning them builds trust in AI pipelines
- Why trust and guardrails, not algorithms, are the real bottleneck to scaling AI
- How contextualized data enables AI to move from reactive reporting to proactive recommendation and orchestration
This workshop emphasizes steps manufacturing leaders can take today to improve decision quality without disrupting operations. Through real-world examples attendees will leave with a clearer understanding of how to build AI-ready data foundations that operators trust, engineers respect and executives can scale.