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...

My clearest lessons about AI Use Cases in Fashion did not come from polished innovation presentations. They came from Monday trade calls where a hero color was unavailable in core sizes, allocation teams were moving units between stores, and merchants were debating whether another promotion would rescue a weak option or merely surrender margin. Working alongside planning, merchandising, sourcing, and fulfillment teams taught me that artificial intelligence creates value only when it improves the decisions people make against a ticking seasonal clock. A useful overview of AI Use Cases in Fashion should therefore begin with the operating grain of the sector: style-color-size, store, channel, and week. Fashion models may look impressive at category level while concealing the exact fragmentation that causes lost sales and excess stock. The real test is whether a use case helps a merchant place a better buy, a planner revise demand earlier, or a fulfillment engine expose inventory that can...

The integration of AI in Order Management has sparked numerous discussions and a fair share of misconceptions. As practitioners navigate this new terrain, understanding what AI truly offers compared to widespread myths is fundamental. Despite some reservations, AI in Order Management continues to demonstrate substantial benefits in areas such as Inventory Optimization and Demand Forecasting Solutions. By dissecting these myths, we can better appreciate the true potential of AI technologies. Myth 1: AI Guarantees Immediate Results Contrary to popular belief, AI is not an instant fix. It requires time for systems to learn patterns and adjust processes like the Procure-to-Pay Cycle for optimal efficiency. Leading firms like Blue Yonder demonstrate that initial implementation stages are crucial for long-term success. Myth 2: AI is Only for Large Enterprises While giants like SAP and Oracle are frontrunners, AI solutions are scalable and becoming increasingly accessible for SMEs looking to...

Three years ago, our e-commerce operation was drowning in data but starving for insight. We had millions of SKU-level transactions flowing through our systems daily, sophisticated dashboards that required two analysts to interpret, and decision-making cycles that stretched for weeks while competitors moved in days. The turning point came during a particularly brutal holiday season when our manual inventory planning process led to simultaneous stockouts on best-sellers and 40% overstock on slow movers. That failure became the catalyst for our journey into autonomous analytics—a transformation that fundamentally changed not just our technology stack, but how our entire organization approaches decision-making in the digital shelf era. The promise of Autonomous Retail Analytics initially seemed straightforward: deploy intelligent systems that analyze data continuously, surface insights without human prompting, and trigger actions based on predefined business rules. The reality proved far ...

Three years ago, our category management team faced a crisis that would fundamentally reshape how we approached technology infrastructure. We had just completed our most ambitious promotional calendar yet—coordinating trade promotions across 47 retail partners, optimizing shelf space allocation for 230 SKUs, and managing markdown strategies that touched every major channel. The systems buckled under the computational load. Our on-premise servers couldn't handle the real-time demand forecasting models we needed, and our promotional performance analysis reports arrived three days late, rendering them useless for in-flight optimization. That failure taught us our first lesson: in modern CPG operations, infrastructure isn't just a back-office concern—it's the foundation of competitive advantage. The journey that followed transformed not just our technology stack but our entire approach to trade promotion planning and execution. Implementing AI Cloud Infrastructure wasn't s...

In the consumer packaged goods industry, the ability to analyze scan data from thousands of retail locations and adjust trade promotion strategies in real time has become a competitive necessity. Behind every optimized promotional lift calculation and every refined category management decision lies a complex technological foundation that most practitioners never see. The infrastructure enabling these capabilities represents a fundamental shift in how CPG enterprises process information, allocate trade funds, and respond to market dynamics. Understanding the mechanics of AI Cloud Infrastructure reveals why organizations like Procter & Gamble and Unilever have been able to transform their trade promotion management from quarterly planning cycles to dynamic, data-driven operations. This infrastructure doesn't simply store data or run calculations faster—it fundamentally changes what becomes possible in promotional budget planning, retailer collaboration planning, and merchandisin...

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...

When you see Amazon's "customers who bought this also bought" recommendations or receive a perfectly timed promotional email just as you're considering a repurchase, you're witnessing the output of sophisticated predictive models running continuously in the background. The e-commerce industry has evolved far beyond reactive decision-making, and today's competitive landscape demands that retailers anticipate customer needs, inventory requirements, and market shifts before they fully materialize. Understanding how these prediction engines actually function reveals why some retailers consistently outperform competitors while others struggle with excess inventory, missed opportunities, and declining customer engagement. The foundation of Predictive Analytics for Retail lies in transforming vast streams of transactional, behavioral, and operational data into actionable forecasts. Every product view, cart addition, abandoned session, purchase, and return generates ...