In consumer goods, the shelf is where strategy becomes reality. Even the best product, promotion, or pricing strategy fails if execution breaks down in‑store. Out‑of‑stocks, poor planogram compliance, misplaced displays, and inconsistent merchandising all erode sales. Field teams often rely on manual audits, inconsistent photos, and subjective assessments. AI gives CPG leaders a way to see the shelf clearly, measure execution objectively, and guide field teams with precision.
What the use case is
Retail execution and merchandising intelligence uses AI to analyze shelf images, store‑level sales, planograms, and field reports to identify execution gaps and recommend corrective actions. It evaluates out‑of‑stocks, facings, display compliance, competitor presence, and pricing accuracy. It supports field teams by generating prioritized store lists, guided tasks, and real‑time insights during visits. It also helps category and sales teams understand which execution issues most impact sell‑through. The system fits into the retail workflow by reducing manual auditing and strengthening in‑store performance.
Why it works
This use case works because shelf conditions follow patterns that AI can detect more accurately and consistently than human auditors. AI can analyze thousands of shelf images to identify missing items, incorrect facings, or misplaced displays. It can correlate execution gaps with store‑level sales to pinpoint where action will drive the most impact. Field execution becomes more efficient because teams focus on the highest‑value tasks. Merchandising improves because compliance is measured objectively and continuously. The combination of image recognition, prioritization, and guided action strengthens both sell‑through and retailer collaboration.
What data is required
Retail execution intelligence depends on shelf images, planograms, store‑level sales, retailer inventory, and field reports. Structured data includes SKU lists, facings, pricing, and store attributes. Unstructured data includes photos, field notes, and retailer feedback. Historical depth matters for understanding execution patterns, while data freshness matters for real‑time prioritization. Clean mapping of SKUs to visual identifiers improves model accuracy.
First 30 days
The first month should focus on selecting one retailer, region, or category for a pilot. Sales and category leads gather representative shelf images and validate their completeness. Data teams assess the quality of planograms, SKU lists, and store attributes. A small group of field reps tests AI‑generated insights and compares them with in‑store reality. Early compliance alerts are reviewed for accuracy. The goal for the first 30 days is to show that AI can identify meaningful execution gaps without disrupting field routines.
First 90 days
By 90 days, the organization should be expanding automation into broader retail execution workflows. Image recognition becomes more accurate as models incorporate additional shelf conditions, packaging variations, and retailer formats. Field teams begin using AI‑generated task lists to prioritize visits and actions. Category teams integrate insights into retailer discussions and joint business planning. Governance processes are established to ensure alignment with brand standards and retailer expectations. Cross‑functional alignment with sales, category management, and supply chain strengthens adoption.
Common pitfalls
A common mistake is assuming that shelf images are consistently captured and high quality. In reality, lighting, angles, and store layouts vary widely. Some teams try to deploy shelf‑recognition models without involving field reps, which leads to mistrust. Others underestimate the need for strong integration with planogram and SKU databases. Another pitfall is piloting too many retailers at once, which dilutes focus and weakens early results.
Success patterns
Strong programs start with one retailer or category and build credibility through accurate, actionable insights. Field reps who collaborate closely with AI systems see clearer priorities and more productive visits. Merchandising intelligence works best when integrated into existing field tools rather than added as a separate app. Organizations that maintain strong data governance and cross‑functional alignment see the strongest improvements in shelf performance and retailer relationships. The most successful teams treat AI as a partner that strengthens execution, visibility, and commercial impact.
When retail execution and merchandising intelligence are implemented well, executives gain stronger in‑store performance, higher sell‑through, and a field organization that operates with far greater clarity.