This session digs into how to work with engineers to make sure technology is actually used in the field, and if not why? Speakers will walk through concrete examples of building credible business cases, and giving frontline technicians and managers ownership in the process so they see how the technology augments their work instead of threatening it.

• Choosing and scoping use cases that tie directly to field KPIs instead of vague "innovation" goals
• Redesigning processes and data foundations before automating, so AI doesn't accelerate bad workflows
• Regularly communicating with, and upskilling, field techs to show how technology is enabling and not replacing them
• Driving adoption on the front line through training, trust, and feedback loops to avoid the "90% of AI projects fail" trap 

AI tools promise everything, but their real-world results often have difficulty living up to the hype and high cost that comes with them ie. AI product lifecycle predictions providing only marginally different suggestions than current predictive analytics. Despite these limitations executive boards continue to view AI as a magic bullet to optimize and automate the workforce for the sake of efficiency.  

Service leaders will discuss lessons learned from actual AI business cases leaving you with takeaways on: 

• Building a more tangible AI ROI to understand and better communicate the actual benefits and weaknesses from a cost, efficiency, and support perspective
• Creating a better semantic data layer for AI, and LLMs to draw from, by identifying critical structured and unstructured data from work logs, sensor data, equipment manuals, etc.
• Understanding how various AI tools use data and come to their answers in order to know their strengths and weaknesses in different use cases
• Improving communication with the board by creating realistic AI expectations and implementation timelines