AI and AR Bring Real-Time Intelligence to Retail Shelves
Consumer packaged goods (CPG) brands are using artificial intelligence (AI) and augmented reality (AR) to obtain real-time retail execution data at individual store locations. The technologies provide field teams with deeper store-level and stock-keeping unit (SKU)-level insight than traditional methods based on manual surveys, spreadsheets, email reporting and retrospective data.
AR allows field representatives to point a mobile device at a shelf and instantly access SKU-level information. This includes product availability, missing items, pricing, planogram compliance and point-of-sale materials (POSM). The technology converts unstructured shelf conditions into actionable insight at the point of execution.
The approach also changes the time required to complete merchandising audits. Brands using image recognition technology complete audits around 75 per cent faster than manual methods. The reduction in audit time can reduce repeat store visits and improve confidence in the quality of retail execution data.
AI can detect out-of-stocks, pricing discrepancies and gaps from a single in-store photograph. The detection covers shelves, coolers and displays and can include competitor products. This gives representatives information about specific issues while they are still in the store.
Traditional retail processes often involved representatives collecting data that was then turned into reports and reviewed days or even weeks later. By that stage, valuable sales opportunities had already been lost. Real-time detection changes that sequence by identifying issues immediately and highlighting affected SKUs so representatives can correct problems before leaving the store.
AI does not eliminate all retail errors. Human execution remains part of the process. However, automatic, real-time detection is able to significantly reduce the time that out-of-stocks and pricing errors remain unnoticed.
AI agents are also enabling frontline teams to act on retail insights rather than collect data. When embedded directly into mobile applications, the agents can provide guided decision-making based on current store conditions. This differs from relying on static spreadsheets or planograms that may already be out of date.
Representatives can ask specific questions and receive immediate, data-driven recommendations. With GoSpotCheck integrated with Salesforce Consumer Goods Cloud, AI can recommend feasible next steps based on current shelf conditions rather than simply reporting problems. These actions can include suggesting promotional activity based on shelf presence or identifying priority actions for a particular store.
Agent-assisted ordering is another capability enabled by combining different sources of retail information. AI agents can use shelf data, in-store inventory, sales history and existing orders when making ordering decisions. The information can be used to optimise orders and improve consistency across supply chains.
AI can also support merchandising decisions at individual stores. Each location serves a different customer base and operates within a different competitive environment, while shopping behaviour can vary between stores. AI allows brands to account for those differences instead of applying a single national planogram everywhere.
SKU-level and POSM data gathered across shelves, coolers and displays can show how products perform against competitors at each location. The information can cover assortment, pricing and share of shelf, giving brands a more detailed view of retail execution from store to store.
Those insights can inform decisions about which SKUs to prioritise, where promotions are most likely to succeed and how product facings should be adjusted to match local demand. The information can help narrow the gap between an ideal retail execution strategy and what is actually happening in-store.
The connected shelf is also being framed as more than a system for identifying what is present on a shelf. The focus is shifting towards continuous, real-time shelf intelligence in which AI agents can identify issues and coordinate responses across field teams and business systems. The systems are intended to move beyond simply detecting conditions towards responses that can occur automatically.
Field teams continue to have a role in this process. AR and AI agents can remove much of the time spent on manual documentation and audits, allowing representatives to concentrate on customer relationships, problem-solving and strategy.
The use of these technologies does not remove the human role in retail execution. Instead, the source describes AI as enhancing people’s work rather than replacing them. Alongside the use of connected shelf data, businesses are expected to standardise shelf information across partners and platforms while continuing to invest in the people who build customer relationships.
