LLM to ROI - Exhibit 1

There are several touchpoints where generative AI can transform the retail value chain.

Before generative AI

After generative AI

Retail value chain

In-store operations Information searches (eg, price, in-store location, stock level) handled manually by associates, leading to delayed customer service

Marketing One-size-fits-all marketing approach due to limited customer insights derived from structured data Creation of marketing materials through a lengthy, iterative process

Back office Time-consuming administrative processes, such as HR and payroll, prone to errors and inefficiencies

Commercial Analytical tools of different maturity level, sometimes hard to adopt

End-to-end value chain Independent decision making by individual functions, leading to a recurring cycle of searching for the underlying causes of commercial events, often failing to identify the true underlying factors

E-commerce Hundreds of hours spent on the generation of e-commerce content Manual rule-based website personalization, consuming employees’ resources

Distribution End-to-end communication with third-party logistics handled by associates Delayed response to distribution disruptions due to complexity of supply chain operations

Procurement All supplier negotiations (including end-to-end contract creation) handled manually by associates, often leading to overlooked details Tedious supplier assessments based on limited data, leading to suboptimal choices

End-to-end value chain End-to-end optimization of decision making across the value chain through faster, more precise, and personalized insights from structured and unstructured data sources, fueling decision on pricing, promotions, stock allocation, digital marketing, and other levers of performance

Commercial Enhancement of analytical tools with gen AI interface, automation of creative tasks (merchandiser copilot)

Back office The next-generation “white collar” lean—transferring administrative processes of support functions to gen-AI-powered chatbots and interfaces (eg, software development copilots, HR/financial copilots)

Marketing Unlimited insights extracted from different unstructured sources (eg, product reviews) Fully personalized marketing materials generated with increased efficiency for every customer

E-commerce Automated generation of e-commerce content (eg, product profiles, descriptions) within a few minutes E-commerce customer experience personalized spontaneously by automated front-end development techniques

In-store operations Associates use gen-AI-powered assistants for instant voice access to information (eg, prices and promos, product location, stock level)

Distribution Initial communication and email messages to third-party logistics handled by gen AI chatbots Returns management process, along with a response to distribution disruption, supported by gen AI

Procurement The initial round of supplier negotiations handled by generative AI (gen AI) chatbots Procurement associates utilize gen AI to assist in closing deals (eg, gen-AI- powered briefs and automated summaries of supplier terms and key insights)

McKinsey & Company