AI in retail
AI in retail: demand forecasting and replenishment, personalization and recommendations, pricing and markdown optimization, and store operations.
AI is reshaping retail and distribution across demand forecasting, merchandising, pricing, supply chain, and customer experience. This block builds sector-specific fluency: how AI techniques apply to retail's core problems (demand volatility, assortment complexity, thin margins, omnichannel logistics), where genuine value has been proven versus overhyped, and how to evaluate vendor claims and ROI with realistic benchmarks. It also covers the governance layer retail leaders cannot ignore: algorithmic pricing scrutiny, personalization and data privacy rules, bias in customer-facing models, and supply chain risk from automated decisions. The goal is practical judgment: knowing which AI applications merit investment, how to size their impact, and what checks to run before deployment, without drifting into generic financial or technical theory.
Ce que vous allez maîtriser
- Map AI techniques (forecasting, computer vision, recommendation engines, NLP) to specific retail value chain functions like demand planning, inventory, pricing, and customer service
- Critically evaluate vendor and internal AI proposals in retail using sector-appropriate ROI frameworks and realistic adoption timelines
- Identify where AI genuinely creates value in retail versus where hype exceeds proven impact, avoiding costly misinvestment
- Apply governance checklists and risk assessments before deploying AI in pricing, personalization, or supply chain automation