Elli Peterson's TechCrunch

My first encounters with AI Use Cases in CPG were less glamorous than the conference presentations suggested. The work involved reconciling stubborn SKU forecasts, explaining promotion lift that disappeared after an event, and helping supply planners respond when packaging constraints invalidated an otherwise sound production schedule. Those experiences taught me that artificial intelligence creates value only when it is embedded in the decisions that category teams, demand planners, RGM leaders, brand managers, and customer supply teams make every week. A model can be statistically impressive and still fail if it arrives after the consensus forecast is locked or recommends an action that cannot be executed at the shelf. A useful way to evaluate AI Use Cases in CPG is to start with the operating rhythm rather than the algorithm. In a large branded manufacturer, decisions flow through stage-gate reviews, demand-plan reconciliation, S&OP, executive IBP, trade promotion planning, pro...

Category managers at consumer packaged goods companies orchestrating promotion planning across hundreds of retail partners face a computational challenge that has intensified dramatically over the past five years. A typical promotion planning cycle for a mid-sized CPG brand now involves evaluating millions of potential promotional configurations—combinations of timing, depth, duration, featured SKUs, and retailer-specific mechanics—to identify optimal trade spend allocation. This combinatorial explosion has transformed trade promotion from a primarily strategic discipline into a computational problem requiring infrastructure capable of processing complex optimization algorithms at enterprise scale. The migration of these workloads to cloud-based artificial intelligence platforms represents one of the most significant operational shifts in consumer packaged goods trade management over the past decade. The practical application of AI Cloud Infrastructure in trade promotion workflows add...