How C.P. Company uses AI in assortment planning and market intelligence

C.P. Company has integrated artificial intelligence into its assortment planning and market intelligence processes, using AI-powered tools to refine product selection and pricing strategies. The Italian sportswear brand was cited in Lectra's 2026 report examining AI adoption across merchandising, assortment planning, pricing, and inventory management in the fashion industry.

What AI tools does C.P. Company use for merchandising?

According to the report, C.P. Company deployed AI-driven merchandising technology to enhance its ability to analyse market conditions and optimise product assortments. The implementation allows the brand's merchandising team to process larger volumes of market data and identify patterns that inform buying decisions. The technology supports both assortment planning, which determines which products to offer, and market intelligence gathering, which tracks competitor activity, pricing trends, and consumer preferences across channels.

The brand's approach reflects a broader industry shift towards data-driven merchandising. Rather than relying solely on historical sales data and buyer intuition, AI systems can analyse real-time market signals, social media trends, search behaviour, and sell-through rates to recommend optimal product mixes for different markets and seasons.

How does AI improve assortment planning processes?

AI-powered assortment planning tools address several longstanding challenges in fashion merchandising. Traditional planning methods require merchandisers to manually analyse sales history, forecast trends, and allocate inventory across stores or regions. This process is time-intensive and can miss emerging patterns in fast-moving markets.

AI systems automate much of this analysis by processing multiple data sources simultaneously. The technology can identify which product attributes, such as silhouette, colour, or price point, are gaining traction in specific markets. It can also flag underperforming categories earlier in the season, allowing teams to adjust orders or allocations before excess inventory accumulates. For brands like C.P. Company that operate across multiple markets with distinct consumer preferences, AI tools help tailor assortments to local demand while maintaining brand consistency.

What role does AI play in pricing and inventory decisions?

Beyond product selection, AI applications in merchandising extend to pricing strategy and inventory management. The Lectra report examined how fashion companies apply AI across these interconnected functions. Pricing algorithms can recommend initial price points based on material costs, competitor pricing, and perceived value, then suggest markdown timing and depth based on inventory levels and sell-through velocity.

Inventory management systems use AI to forecast demand at the SKU level, taking into account seasonality, promotional calendars, and external factors such as weather or economic conditions. These forecasts inform production quantities, warehouse allocation, and replenishment schedules. When integrated with assortment planning tools, the systems help merchandisers balance product variety against inventory risk, a persistent tension in fashion retail.

Why are fashion brands adopting AI for merchandising now?

Several factors are accelerating AI adoption in fashion merchandising. Consumer expectations for product freshness and availability have intensified, particularly in the wake of supply chain disruptions that left many retailers with mismatched inventory. Brands need to respond more quickly to demand shifts, which requires faster analysis of market signals than manual processes can deliver.

The technology itself has also matured. Early AI merchandising tools required extensive data infrastructure and technical expertise to implement. Modern solutions are increasingly designed for merchandising teams to use directly, with interfaces that present recommendations in familiar formats such as assortment grids or pricing ladders. Cloud-based deployment has lowered the barrier to entry for mid-sized brands that lack large IT departments.

Economic pressure is another driver. Excess inventory and aggressive markdowns eroded margins across the industry in recent years, making the financial case for better demand forecasting more compelling. AI tools that reduce overbuying or identify optimal markdown timing can deliver measurable return on investment within a single season.

What are the key benefits and limitations?

The primary benefits of AI in merchandising include faster decision-making, more granular market analysis, and reduced reliance on manual data processing. Merchandisers can spend less time compiling reports and more time on strategic choices such as brand positioning or cross-category coordination. The technology also creates consistency in decision-making processes, reducing the variability that comes from individual buyer judgment.

However, AI merchandising tools have limitations. The systems are only as good as the data they receive, and many fashion companies still struggle with fragmented data sources or incomplete historical records. AI recommendations also require human oversight, particularly for brand-critical decisions such as which trends align with the company's aesthetic or values. The technology excels at pattern recognition and optimisation within defined parameters, but cannot replace the creative and strategic judgment that experienced merchandisers bring to product selection.

Implementation requires investment not just in software, but in data infrastructure, staff training, and process redesign. Merchandising teams must learn to interpret AI recommendations, understand the models' limitations, and integrate the technology into existing planning calendars and workflows. For brands like C.P. Company, successful deployment depends on this operational change management as much as the technology itself.

What does this mean for the future of merchandising?

The adoption of AI in merchandising is reshaping the role of buying and planning teams. Rather than eliminating merchandising jobs, the technology is shifting the skill set required. Demand for data literacy, analytical thinking, and comfort with algorithmic recommendations is increasing, while routine data processing tasks are being automated. Merchandisers increasingly act as strategic decision-makers who use AI insights as one input alongside market knowledge, brand understanding, and creative vision.

As AI tools become more sophisticated, they are also enabling new merchandising strategies. Hyper-localised assortments, where product mix varies significantly between nearby stores based on neighbourhood preferences, become feasible when AI handles the complexity of planning and allocation. Dynamic pricing, already common in travel and hospitality, is gaining traction in fashion as AI systems can manage the frequent price updates required. Test-and-learn approaches, where brands introduce small quantities of experimental products and scale production based on early signals, are accelerated by AI-powered demand sensing.

For the fashion industry, the question is no longer whether AI will play a role in merchandising, but how extensively and how quickly brands can implement it effectively. C.P. Company's inclusion in Lectra's report signals that AI-driven merchandising has moved from early adopter experimentation to mainstream practice, with measurable applications across assortment planning, pricing, and inventory management.