Artificial Intelligence and Category Management: A Genuine Advancement or Merely a Marketing Phrase?
In the ever-evolving world of retail, Artificial Intelligence (AI) is making a significant impact, particularly in category management and merchandising. This transformation was the focus of a recent discussion moderated by Louisa Burwood Taylor, the head of news and research at the socials.
The event, which took place on July 30, brought together leaders from BCG, Deloitte, Daisy Intelligence, Lumi AI, and Constructor. The discussion highlighted the ways AI is being used to streamline processes, personalize shopping experiences, and drive business outcomes.
One of the key advantages of AI is its ability to forecast trends, predict prices, and plan promotions effectively. Companies like Black Swan Data, acquired by Mintel in June, use AI to predict rising trends in food, flavors, ingredients, or product claims. AI is also being used for smarter pricing and promotion planning, suggesting the best time to drop a price or run a promo based on past sales and elasticity models.
However, the implementation of AI solutions in category management and merchandising is not without challenges. A survey by BCG revealed that 50% of merchants hadn't received training on analytics tools. The key challenges include data and clarity issues, long sales cycles and slow decision-making, technology integration and team readiness, defining success and measuring impact, change management, and organizational resistance.
AI performs best in situations where rules are clear, data is clean, and success is clearly defined. Overcoming scattered and unclean data, slow and outdated processes, integration issues, workforce training, and the need for clear value metrics are principal barriers to scalable AI usage.
In category management, AI is already making a significant impact in areas such as assortment, merchandising, and execution. 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.
AI is also being used to personalize and make shopping more relevant for the shopper. Companies like Constructor, which extended its Series B by $25 million in 2023, use clickstream-based AI to personalize discovery and search in real time.
Digital visual merchandising and store execution are also areas where AI is making a difference. Flagship, a company that raised $5.5 million in 2024, builds digital twins of retail stores using computer vision, allowing teams to experiment with layout ideas, product setups, and see what drives sales before making real-world changes.
Localized assortment optimization using AI helps determine which products go where and in what mix, based on demand signals, margin data, and shopper needs. 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.
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 these advancements, progress has been slow due to long sales cycles, clunky tech integrations, and training overworked teams. BCG's 2025 survey found that 1 in 3 data points used by merchants was wrong. The same survey revealed that approximately 40% of available tech goes unused.
In conclusion, while AI holds immense potential for transforming category management and merchandising, overcoming scattered and unclean data, slow and outdated processes, integration issues, workforce training, and the need for clear value metrics are principal barriers to scalable AI usage. The webinar titled "AI Data-Driven Category Management," which took place online at 12pm ET, offered insights into these challenges and solutions. Registration for the webinar can be found on the socials's website.
- In the business realm, technology, through AI, is not only revolutionizing category management and merchandising in retail but also finance, as companies like Black Swan Data use AI to predict trending food, flavors, and product claims, and Constructor utilizes AI for real-time personalization of shopping experiences.
- Despite the profound impact of AI on various industries such as retail and finance, the implementation of these solutions faces challenges such as data accuracy issues, technology integration, team preparedness, and the need for clear value metrics, as highlighted by BCG's survey which found that one in three data points used by merchants was incorrect and approximately 40% of available tech goes unused.
