Demand Forecasting
Demand Forecasting on Beyond Market Intelligence: a running collection of 2 stories we have gathered and hand-picked because they are worth your time. Every post here touches on demand forecasting in some way — the news, the analysis, the deep dives, and the occasional surprise find. Acme AI is the next-generation, AI-powered spreadsheet platform built to replace Excel and redefine how analysts, data scientists, and enterprise teams work with data. New stories are added to this page as we find them, so check back if you want to keep up with what is happening around demand forecasting, or subscribe to the RSS feed to get them as soon as they are published. Browse the collection below, or head back to the homepage to see everything Beyond Market Intelligence is covering right now.
Target SVP says its real AI moat isn't the models — it's everything built around them
Target SVP Siobhán McFeeney asserts that Target’s competitive advantage in AI isn’t solely reliant on advanced models, but rather the robust infrastructure built around them. The company’s approach prioritizes deliberate agent deployment, ensuring they address high-value problems and “earn” autonomy through demonstrable results. This framework, encompassing architecture, taxonomy, and rigorous observability, enables scalable AI investment and allows Target to strategically leverage models—from frontier to specialized—for optimal cost-benefit. For deeper insight into agent architecture, explore Microsoft’s recent reference architecture for AI agents on AKS.
When Data Science Makes Us Sad: The Story of an Overbooked Flight
Data science isn't always a victory. Sometimes, it highlights uncomfortable truths, as revealed in "When Data Science Makes Us Sad: The Story of an Overbooked Flight." This compelling piece explores a real-world scenario where algorithmic decisions resulted in an $8 million payout versus a potential $5,000 resolution—and the possibility of significant public backlash. Discover how seemingly rational data models can lead to unexpected, and costly, outcomes. For a deeper dive into optimizing AI performance, explore "Prompt Compression Techniques."