At some point, there is the ability for AI to start drawing conclusions and to offer recommendations, or at least at minimum, to provide different scenarios based on what’s happening in your business. Mike Weber, CMO, Upshop
Introduce AI to Your Data: The jour- ney begins with AI-powered demand forecasting. Brask explained how this model blends historical sales, promo- tions, price shifts, and even external factors like weather or holidays. Importantly, he emphasized that “even with poor quality data, the nature of machine learning and AI in general can bridge the gaps that exist,” mak- ing forecast accuracy goals of 95% or more attainable. Train the System: Here, organiza- tions identify the right metrics and build what Weber called their own “approach to Moneyball.” By tracking plan engagement, forecast compli- ance, and menu compliance, busi- nesses can shift behavior in stores while AI fine-tunes production sched - ules around shelf life, sales patterns, and timing. Partner with AI : At this stage, teams become digitally empowered. Task management evolves from rigid
schedules to dynamic, AI-prioritized workflows. With large language mod - els (LLMs) layered over enterprise data, employees can “ask questions in whatever language [they] feel com- fortable with and then get actionable insights out of [their] own data,” Brask said. Autopilot : Finally, AI begins to draw conclusions and model out different scenarios. Specialized AI “agents” for supply, limited time offers, or waste can proactively suggest strategies or flag issues, even for events with no precedent, like a new store opening or product launch. Weber wrapped up by inviting ques- tions, stressing that these four steps form the foundation of any roadmap for embedding AI. The conversation he noted, is about practical possibili- ties, meeting businesses where they are today while preparing them for a future in which AI is central to every decision.
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