There are several touchpoints where generative AI can transform the retail value chain.
Retail value chain
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
Distribution
End-to-end communication with third-party logistics handled by associates
Delayed response to distribution disruptions due to complexity of supply chain operations
In-store operations
Information searches (eg, price, in-store location, stock level) handled manually by associates, leading to delayed customer service
E-commerce
Hundreds of hours spent on the generation of e-commerce content
Manual rule-based website personalization, consuming employees’ resources
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
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)
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
In-store operations
Associates use gen-AI-powered assistants for instant voice access to information (eg, prices and promos, product location, stock level)
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
Marketing
Unlimited insights extracted from different unstructured sources (eg, product reviews)
Fully personalized marketing materials generated with increased efficiency for every customer
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)
Commercial
Enhancement of analytical tools with gen AI interface, automation of creative tasks (merchandiser copilot)
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
McKinsey & Company