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X-WR-CALNAME:Efficiency First California
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X-WR-CALDESC:Events for Efficiency First California
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DTSTART;TZID=America/Los_Angeles:20260804T120000
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CREATED:20260723T192451Z
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UID:69675-1785844800-1785848400@efficiencyfirstca.org
SUMMARY:The AI-Ready Jobsite: Turning Field Data Into Predictable Builds (and Better Quality)
DESCRIPTION:Most jobsite software wasn’t built to capture what AI actually needs. The fields\, timestamps\, and check-ins that platforms record today were designed for tracking tasks\, not for training models or powering diagnostics. That gap is the real bottleneck standing between most builders and the AI tools they’re hearing so much about. This session lays out a practical path forward. We’ll define the minimum viable dataset a jobsite needs to capture\, show how that data supports descriptive and diagnostic analytics that surface the real drivers of delay and rework\, and then explore what becomes possible once AI is layered on top — interrogating reports\, suggesting recovery paths\, simulating what-if scenarios\, and automating parts of schedule and field management. The goal is a set of repeatable practices any builder can apply\, regardless of current tech stack\, to support more starts per superintendent while improving predictability\, quality\, and cost performance. \nLearning Objectives: \n\nIdentify the minimum viable jobsite dataset needed to move from basic tracking to AI-ready field operations\nUnderstand how to apply analytics to pinpointing the actual root causes of delay and rework on a jobsite\, rather than just reporting that they happened.\nEvaluate practical AI use cases layered on field data\, including report interrogation\, recovery-path suggestions\, and what-if scenario simulation.\nApply a repeatable framework for improving predictability and cost performance regardless of current technology stack
URL:https://efficiencyfirstca.org/event/the-ai-ready-jobsite-turning-field-data-into-predictable-builds-and-better-quality/
CATEGORIES:EEBA
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