Generative AI in Retail: Unlocking Transformational Value for Your BusinessKeywords: generative AI in retail, AI use cases, retail AI strategy, customer-centric retail, digital transformation, AI in
- William Beresford
- Aug 8
- 6 min read
Introduction: Why Generative AI Matters for Retail
Generative AI (GenAI) is no longer a futuristic concept — it's fast becoming a strategic lever for transformation across the retail value chain. While AI in retail once meant forecasting or fraud detection, GenAI takes things further — enabling real-time content generation, immersive customer experiences, intelligent automation, and store simulations.
We are lucky to work with Gartner and according to them, by 2027, 90% of Tier 1 retailers will have successfully executed at least one business. As margins tighten, consumer behaviour shifts, and competition intensifies, retailers must now embrace GenAI not as a technology experiment — but as a business imperative.
From Hype to High-Impact Use Cases
The GenAI opportunity is real — but so is the noise. Many retail initiatives are chasing headlines, not impact. At Beyond, we’ve worked with clients to separate short-term sizzle from scalable value. We focus on practical, commercially grounded use cases that deliver measurable results.
Gartner suggest a super simple framework for GenAI that we think makes for a pragmatic way to look at things by categorising use cases by value and feasibility:
Likely Wins: Highly feasible, high-value (e.g. search, chat, personalization)
Calculated Risks: High potential but technically challenging
Marginal Gains: Low value or narrow applicability
Let’s explore some of the retail use cases that we think matter most — and how retailers are bringing them to life.
The GenAI Use Cases Retail Leaders Should Prioritise
Retail is ripe for reinvention — and Generative AI is fast becoming the engine for that change. But with so many potential applications, how do you cut through the hype and focus on what delivers? Here are six GenAI use cases where the opportunity is real, the value is high, and early adopters are seeing measurable returns.
1. Conversational AI to Transform Customer Interactions
Retailers are replacing rigid chatbot scripts with natural, human-like conversations powered by GenAI. These systems understand nuance, respond contextually, and can support sales, service, and discovery across messaging platforms, voice assistants, or mobile apps.
Done well, conversational AI doesn’t just reduce contact centre load — it drives sales conversion and builds brand affinity by making digital engagement feel intuitive and personal.
What Beyond is doing:
We’re currently trialling a bespoke conversational agent on our own website to engage visitors in real time, answer product and service questions, and qualify new leads. This live test-and-learn environment helps us understand the technical, operational, and brand implications before deploying similar solutions for clients.
2. Hyper-Personalisation at Scale Using Generative Content
GenAI makes it possible to deliver true 1:1 personalisation — not just with product recommendations, but with dynamically generated messaging, offers, product descriptions, and landing pages tailored to each individual.
This is particularly impactful when paired with loyalty and CRM programmes, creating a virtuous cycle of relevance and engagement. However, success requires high-quality product and customer data — and the ability to automate creative production at scale.
What Beyond is doing:
For a multi-brand retailer, we built a complete GenAI content engine that:
Ingests and harmonises supplier product data
Automatically generates enriched product descriptions for both online and shelf edge
Creates structured metadata that fuels segmentation, modelling, and personalisation across marketing and loyalty initiatives
This not only improves customer experience, but also significantly reduces manual content creation effort.
3. Smart Store Layouts Powered by Sensor-Driven AI
Understanding in-store customer behaviour — how people move, what they touch, where they pause — has always been difficult to capture and act upon. But with GenAI and sensor data, it’s now possible to analyse footfall, browsing patterns, and product interaction in real time.
Retailers can use these insights to redesign floor layouts, improve category adjacencies, and trial new merchandising strategies — turning stores into agile, data-driven environments.
What Beyond is doing:
We’re working with a national retailer to integrate GenAI with physical sensor data in-store. By analysing heatmaps and product interaction data, we’re helping them redesign store formats, improve signage and navigation, and optimise product placement — all aimed at improving customer dwell time and conversion.
4. Agentic Training and Change Support for Large-Scale Transformation
As retailers modernise systems and shift towards customer-centric operating models, training and change management become critical. Traditional training approaches struggle to scale across diverse roles — from head office to the shopfloor.
GenAI, especially in the form of agentic AI (intelligent systems that take action, not just give advice), can personalise training, deliver bite-sized learning in context, and continuously reinforce behaviour change — turning training into an ongoing capability rather than a one-off event.
What Beyond is doing:
We’ve implemented an agentic AI enablement solution for a retailer undergoing a major customer-centric transformation. This system delivers tailored learning journeys to marketing, operations, warehouse and in-store teams — ensuring every function understands its role in the new model, and reinforcing that learning with real-time nudges and coaching.
5. Automated Content Creation for Speed, Scale and Consistency
Retailers are constantly producing content — from marketing copy to product information, from email campaigns to in-store signage. GenAI dramatically increases content velocity while reducing cost, by generating copy that’s on-brand, localised, and relevant across formats.
This use case delivers quick wins in terms of productivity, but the deeper value lies in enabling continuous, data-driven content adaptation based on performance insights.
What Beyond is doing:
Our team has embedded GenAI content creation into clients’ e-commerce and retail media workflows — not only generating product descriptions and SEO content, but also structuring outputs so they can be tested, personalised, and measured in real time.
6. AI-Powered Product and Supply Data Intelligence
Retailers often struggle with fragmented, inconsistent product and supply chain data — making it difficult to maintain accuracy, move quickly, or optimise performance. GenAI can automate the extraction, enrichment, and validation of product data at scale, while generating metadata that supports better forecasting, planning, and customer targeting.
In a world of expanding product ranges, third-party sellers, and private label growth, getting this right is a competitive advantage.
What Beyond is doing:
We’ve developed agentic AI solutions that:
Automate supplier data collection across formats and sources
Enrich product records with attributes and imagery
Generate shelf-ready copy and data labels
Feed metadata into AI models that drive assortment planning, customer segmentation, and predictive analytics
This has dramatically improved data quality, content coverage, and campaign performance for our client — all with less manual intervention.
Beyond the Basics: What's Next in GenAI for Retail?
Retailers are beginning to explore next-generation capabilities including:
Multimodal AI
Combining text, image, voice, and sensor inputs to power smarter systems. Think:
Virtual try-ons using GenAI and AR
Search that understands natural language + product images
Vision AI used in-store to track product engagement or prevent stockouts
Agentic AI and Decision Orchestration
Unlike static tools, agentic AI can plan and execute workflows with autonomy, making real-time decisions across pricing, promotions, logistics, and beyond.
What Beyond is doing:
We’ve built autonomous product data agents for a retail client that:
Harvest and harmonise supplier data
Generate shelf and digital content
Feed downstream analytics, driving better product placement, pricing, and segmentation
Strategy First: How to Build a Winning GenAI Retail Roadmap
At Beyond, we believe successful GenAI deployment must be driven by strategy, not novelty. Here’s our high-level framework:
Final Word: Practise What You Preach
Too many consultancies offer advice on AI but don’t use it themselves. At Beyond, we live our recommendations. From launching our own internal agents to experimenting in real-world environments with our clients, we’re committed to staying on the front foot — learning by doing, managing risk responsibly, and always aiming to put data to work for people.
The age of generative AI isn’t coming — it’s here. Retailers who embrace it with focus and ambition will shape the future of the industry.
Get in touch to continue the conversation. https://www.puttingdatatowork.com/contact-us
Comments