While AI fragments shopping channels, it also enables hyper-personalization of the fulfillment experience. By integrating external data like weather, transit times, and regional issues, brands can proactively communicate with customers about their orders, creating a deeper, more valuable connection.
AI will fragment the customer journey across countless platforms, moving purchases away from brand-owned websites. Retailers must build systems to manage inventory and product information across this decentralized landscape, not just focus on perfecting their own site experience.
AI can analyze a customer's support history to predict their behavior. For instance, if a customer consistently calls about shipping delays, an AI agent can proactively contact them with an update before they reach out, transforming a reactive, negative interaction into a positive customer experience.
Instead of merely reacting to supply chain disruptions, AI allows companies to become proactive. It can model scenarios involving labor shortages, tariffs, and weather to reroute shipments and adjust inventory promises on websites in real-time, moving from crisis management to strategic orchestration.
Instead of large, multi-year software rollouts, organizations should break down business objectives (e.g., shifting revenue to digital) into functional needs. This enables a modular, agile approach where technology solves specific problems for individual teams, delivering benefits in weeks, not years.
A 'GenAI solves everything' mindset is flawed. High-latency models are unsuitable for real-time operational needs, like optimizing a warehouse worker's scanning path, which requires millisecond responses. The key is to apply the right tool—be it an optimizer, machine learning, or GenAI—to the specific business problem.