By Lori Schafer
Outdoor participation remains strong, but that enthusiasm is not automatically translating into product sales. Consumers are buying more selectively, prices are rising and outdoor retailers are managing categories shaped by short selling seasons, unpredictable weather and complex assortments.
The Outdoor Industry’s Association 2026 trends report illustrates the pressure. Outdoor product sales finished 2025 slightly below 2024 levels, while the first quarter of 2026 brought a 7.3% decline in unit sales across every product category. At the same time, average retail prices increased 6.7%, making margin protection even more important.
These conditions call for more precise decisions about inventory, pricing, assortments and vendor data. Agentic AI can help outdoor retailers respond faster, but only when it operates from complete, governed data and works alongside experienced merchandising teams. Here are tips for a practical starting point.
Data Drives Agentic Success
First, it’s often not an AI model’s fault if an AI pilot fails; it’s the data underneath. In outdoor retail, this can be seen when product records are missing key attributes, sizing and colorway data appears inconsistently across vendors, and sales history data is too fragmented to use for a true forecast.
Before any AI initiative can help a retailer, the data needs to be unified in one governed view. It’s not a glamorous first step, but it’s a mandatory one to help unlock agentic AI to improve operational efficiencies.
Seasonal Demands Require More Than a Markdown Calendar
Outdoor retail runs on unpredictable windows. Unusually cold weather in October can increase the need to stock insulated jackets; an early thaw in February may slow that demand. Traditional markdown planning is built around fixed calendar cadences, like a quarterly review or a seasonal reset, which is the wrong rhythm for a category driven by weather and short-selling seasons.
Agentic systems steady the flow and don’t wait on scheduled reviews. Merchandising teams working with AI agents continuously have a better read on sell-through, weather signals and regional demand at the SKU-and-store level. Agents can identify emerging overstock, recommend inventory transfers or promotional actions, and determine when a markdown may be necessary.
It’s about protecting margin. A merchandising team at a major sporting goods retailer doesn’t need to be manually tracking five regional forecasts each week; an agentic AI system surfaces key decisions to address, shares a recommended action, and the buyer approves, adjusts and makes precision decisions.
Vendor and Assortment Complexity Taxes Merchandising Teams
Outdoor retailers can also simplify assortments with AI. Retailers juggle multiple brands, each with highly technical product attributes and innovations that they’re touting. Every brand also has its own sizing logic, materials data and compliance requirements.
Merchandising teams can spend a disproportionate amount of time chasing vendors for product information like a missing weight spec or incomplete colorway matrix. A rules-based agentic AI system identifies data gaps in existing product information, and merchandising teams work with the AI agents to approve issues of compliance, cost or launch timeframes. The agents scour the data and work with merchandisers to make exacting decisions, and time is money in retail.
Outdoor Retailers Don’t Need a Tech Overhaul to Improve Efficiency
Moving toward operational agentic assistance doesn’t require a multi-year IT platform overhaul. Retailers can start by creating a foundation of high-quality, enriched, governed data that teams across the organization can use with agentic tools. Teams get trained on using the AI, and that takes time, but it becomes a genuine tool to navigate seasonal volatility, vendor complexity and the new AI-driven discovery landscape.
The path begins with trusted data, but the value comes from better execution. When agentic AI helps teams anticipate seasonal shifts, improve product information, and place the right inventory in the right markets, outdoor retailers can respond faster without giving up human judgment. That combination of stronger data, intelligent assistance and experienced decision-making can help retailers protect margins through every peak and valley.
Lori Schafer is CEO of Digital Wave Technology, an AI-native enterprise platform company helping retailers, consumer brands, and health and wellness organizations operationalize agentic AI at scale.
