Research

AI for Retailers

Discover Reply’s point of view and experiences helping retailers to navigate AI-powered development, characterised by real-time intelligence and customer centric innovation.

Artificial Intelligence: from an optional technical deployment to a structural necessity for the Retail industry

This Reply Research shows how retailers are currently moving beyond small, incremental step changes to their portfolios, choosing instead to digitally transform through connected, AI-driven automation. This shift represents a transition from reactive service to a proactive “connective intelligence” that bridges the gap between digital efficiency and physical presence.

Retailers are increasingly orienting themselves towards unified technological systems that integrate traditionally disjointed legacy systems. AI facilitates a continuous flow of data and logic that enables real-time decision-making, ensuring every touchpoint from the warehouse to the shop window is intelligent and aligned. AI frameworks are designed to integrate with complex brownfield environments, allowing retailers to improve their capabilities without replacing the entire existing infrastructure.

Agentic AI is enabling autonomous decision-making in retail

Agentic AI unlocks a higher level of maturity by allowing systems to reason, set goals, and execute tasks with limited human supervision. These systems provide a continuous adaptation model that standard automation cannot offer.

  • Autonomous Replenishment
    AI agents can independently analyse shelf conditions, communicate with suppliers, and order stock replenishment.

  • Exception Management
    Systems automatically detect and resolve inventory anomalies or sales data discrepancies that are often invisible to the human eye.

  • Multi-Agent Orchestration
    Specialised agents collaborate to manage distinct business logic, such as inventory reconciliation, order creation, and returns processing, through a unified conversational interface.

  • Decision Support
    By delegating repetitive reasoning tasks to agents, retailers ensure background operations are managed with precision while the sales force focuses on customer relationships.

Generative AI as a marketing and engagement engine

Generative AI acts as a creative engine that dissolves the historic barrier between mass commerce and personalised service. It enables retailers to communicate in ways that feel bespoke while maintaining the execution speed required for global scale.

Retailers are leveraging these models to produce high quality product descriptions and promotional images on a massive scale without sacrificing brand authenticity

Generative AI powers conversational interfaces that offer shoppers immediate, context-based guidance and personalised product recommendations based on their purchase history.

This shift facilitates a concierge-style service that can anticipate customer needs before they are explicitly requested, even acting as a virtual assistant that adds items to the cart based on personal style.

Retailers are evolving manual activities into intelligent workflows where AI analyses historical sales and warehouse data to recommend pricing and products for flyers. This extends to creating interactive digital assistants that allow customers to "dialogue" with the flyer to receive advice based on their specific needs.

As consumers move from traditional search engines to LLM-based conversational systems, retailers are pivoting from SEO to GEO, structuring their digital content to be "machine-readable" so that AI answer engines select and feature their products in single-response queries.

How are omnichannel journeys being redesigned?

The redesign of omnichannel journeys moves retailers beyond monolithic systems and toward composable commerce, where individual components operate as independent, AI enhanced services.

  • Conversational Commerce
    Advances in natural language processing allow AI systems based on Large Language Models to learn a customer's specific needs and style, managing complex queries with precision.

  • Vector-Based Search
    Systems move from exact keyword matching to understanding underlying intent, significantly improving product discovery and reducing failed searches.

  • Mobile-First Engagement
    AI-powered applications facilitate Scan and Shop mechanisms, where image recognition classifies items and facilitates self-checkout through proximity sensors as the user exits the store.

  • Unified Workspace
    Store staff can use Agentic AI-powered unified workspace frameworks to manage sales, operational activities, and visual merchandising through a single interactive environment.

Some Examples of In-Store Innovations

  • Digital Concierges and Virtual Experts
    Interactive kiosks near shelves feature virtual experts that guide customers through complex purchasing choices using simple, non-technical language.

  • Computer Vision and Loss Prevention
    AI-powered cameras identify loose produce at scales and monitor self-checkouts to identify products moved without being scanned, significantly reducing stock loss.

  • Automated Auditing and Compliance
    The AI Store Check platform allows employees to take photos of shelves that the system compares against planograms to highlight price tag errors or layout anomalies in real-time.

  • Interactive Trial and Customisation
    Smart mirrors in fitting rooms recognise garments to suggest complementary items, while 3D digital humans provide personalised styling advice, creating an immersive "emotional room" experience.

  • Shelf Digitalisation and Continuous Monitoring
    Advanced optical sensors transform shelf images into structured data to track picking trends and rotation speed, triggering replenishment before stockouts occur.

Predictive operations and data fully leveraged across the entire value chain

  • The supply chain is evolving into a predictive ecosystem supported by multi-agent architectures, forecasting engines, and robotics. Furthermore, AI provides the essential tools to manage, interpret, and activate granular data that is often locked in separate, unstructured systems.

  • Retailers now employ predictive replenishment models that correlate internal sales data with external variables, such as weather patterns and local events, to refine forecast accuracy and trigger reorders automatically.

  • This is supported by autonomous drones in warehouses that scan barcodes to determine quantity and identify expiry dates based on batch numbers, flagging logistical bottlenecks early.

  • Furthermore, AI-based dashboards allow managers to ask questions in natural language and receive dynamically generated visual reports, ensuring data driven decisions are made at the point of sale.

  • Retailers can leverage AI models to predict the future sales of individual customers, enabling data-driven decisions regarding acquisition costs and lifetime value. Clustering techniques segment customers based on buying habits, allowing for highly personalised marketing and increased loyalty.

  • Generative AI is used to create synthetic consumer profiles based on real data samples to simulate decision-making processes, allowing for virtual testing of new products and strategies. Algorithms also refine pricing models constantly, ensuring competitiveness whilst safeguarding profit margins.

Reply as a partner to bridge the gap between Retailers’ corporate vision and day-to-day adaptability

Reply’s network model offers specialised companies that combine technological expertise with focused management consulting and creativity to accelerate safe and effective AI adoption. Reply experts help global and local retailers leverage AI across all processes, from accurately predicting preferences to offering highly personalised shopping experiences.