Artificial Intelligence in Category Management: Authentic Solution or Merely a Marketing Phrase?
In the ever-evolving world of retail, the use of Artificial Intelligence (AI) and data-driven insights is transforming the landscape of visual merchandising and store execution. This transformation is not limited to enhancing digital twins and computer vision but extends to the heart of category management.
Category management has been grappling with challenges such as outdated planning cycles, scattered data, manual execution, and slow decision-making. A survey by BCG in 2025 revealed that 1 in 3 data points used by merchants was incorrect, highlighting the need for a change.
AI is being employed in various ways to address these challenges. For instance, it is being used for forecasting, trends, and pricing, including real-time signal analysis to avoid empty shelves and spotting demand swings before they appear in Nielsen data. Companies like Daisy Intelligence and Lumi AI are leading the way in this regard.
However, the implementation of AI in category management is not without its challenges. BCG identified organizational resistance, legacy systems, and a lack of time as the top blockers. The extensive manual work required to organize and tag large product catalogues accurately, the complexity of creating detailed and hierarchical category trees, and integrating AI solutions into existing retail systems are significant hurdles.
Real-world examples, such as those demonstrated by Constructor and Flagship, are proving that these challenges can be overcome. Constructor uses clickstream-based AI to personalize discovery and search in real time, while Flagship builds digital twins of retail stores, allowing teams to test layout ideas, product setups, and evaluate sales impacts before making real-world changes.
The application of AI could potentially improve category management, but requires the pairing of suitable use cases with factors that drive 70% of AI's value. Impact Analytics, a company that raised $40 million in 2024, helps CPGs analyze shopper and sales data to determine ideal product mixes and pricing by retail account, not by region.
Localized assortment optimization using AI involves analyzing demand signals, margin data, and shopper needs to decide which products go where and in what mix. For example, retailers like Lowe's are using AI, computer vision, and digital twins to test layouts, adjust placements based on season or weather, and react faster to viral trends.
Moreover, AI is making a significant impact in assortment, merchandising, and execution on retail shelves. Black Swan Data, a company acquired by Mintel in June, helps food brands identify rising trends using AI to analyze social chatter and consumer behavior to predict which flavors, ingredients, or product claims might be the next big thing. Aravita, a Brazil-based startup backed by Qualcomm Ventures, helps supermarkets fine-tune order quantities for perishables using data from weather, demand patterns, inventory, and shelf life.
Despite the challenges, the potential benefits of AI in retail category management are undeniable. Overcoming the challenge of manual data handling through sophisticated AI-driven automation, combined with organizational change to embrace AI as an augmenting tool rather than a replacement, are key strategies for successful AI implementation in retail category management.
On July 30, a webinar titled "AI Data-Driven Category Management" will be held online, featuring leaders from BCG, Deloitte, Daisy Intelligence, and Lumi AI discussing the use of AI in category management and merchandising. Registration for the webinar can be found on the socials's website.
References: 1. [Link to Reference 1] 2. [Link to Reference 2] 3. [Link to Reference 3]
- The integration of AI in category management extends beyond visual merchandising and store execution, also impacting finance, business, and technology sectors as AI is employed for forecasting, trends, pricing, and localized assortment optimization.
- In the retail industry, AI solutions are being used not only by advanced companies like Daisy Intelligence and Lumi AI, but also impact analytics firms, such as Impact Analytics, which helps CPGs analyze shopper and sales data to optimize product mixes and pricing.
