FarsightIQ is an AI-powered retail intelligence platform that helps retailers predict demand, optimize inventory, uncover actionable insights, and make faster, data-driven decisions. Its suite of predictive analytics, machine learning, and computer vision solutions helps improve forecast accuracy, boost sales and margins, reduce carrying costs, and enhance the customer experience. In this episode, we speak with Scott Pearson, VP of Sales and Marketing at Jesta, about how the company is using AI to help retailers move from hindsight to foresight and then into action. The conversation focuses on FarsightIQ, Jesta’s new AI division, and how it’s designed to help retailers deal with inventory distortion, stockouts, overstocks, and the complexity of modern retail planning. Scott explains that the platform is built as a decision-intelligence layer, not just a reporting tool, so it can observe demand, anticipate outcomes, recommend actions, and still keep a human in the loop. The episode also explores how FarsightIQ is organized into several modules, including demand forecasting, product attribution, replenishment, inventory balancing, risk detection, natural-language advising, and operational matching. Scott explains why traditional analytics often fall short, especially when retailers are dealing with fragmented data, changing channels, promotions, local events, and seasonal differences. He emphasizes that the system is designed to work with both constrained and unconstrained demand, helping retailers understand not just what sold, but what could have sold if inventory had been available. A major theme of the conversation is that AI is most effective when it supports, rather than replaces, human judgment. Scott stresses that merchants and planners still need oversight because they understand context that AI may miss, such as local events, vendor issues, or visual changes in-store. He also explains that successful AI adoption depends on clean, focused, and well-structured data, and that retailers should start with the highest-value use cases instead of trying to solve everything at once. The episode closes with Scott’s advice to focus less on systems themselves and more on the decisions those systems are meant to improve. Follow SourceForge: SourceForge.net - https://sourceforge.net SourceForge LinkedIn SourceForge X (Twitter) SourceForge Facebook Interested in appearing on the SourceForge Podcast? Contact us here. The SourceForge Podcast is the world's largest B2B software podcast.
