On AI-Powered Retail Planning: Smart Merchandising is Transforming Inventory and Profitability

Artificial intelligence was once a nebulous, futuristic promise in retail. Today, though, it’s firmly planted in the day-to-day decisions that shape inventory, margin, and customer experience. From predictive demand forecasting to dynamic allocation and replenishment, AI is redefining what good planning looks like.

And therein lies the opportunity. Retailers that wholly embrace AI-driven planning are reducing stockouts, minimizing markdowns, and improving full-price sell-through. But that’s not to say that this shift is just about automation. It also means making smarter, faster, and more precise decisions across the merchandising lifecycle.

Here’s how AI is helping merchandise planners to move beyond hindsight and gut feel, and toward a new model of profitability and agility.

In forecasting, intelligence trumps estimates

Historical data is no longer a reliable predictor when consumer behavior, market conditions, and economic signals shift rapidly. AI-driven demand forecasting negates the traditional forecasting shortcomings by incorporating real-time inputs and predictive analytics. Machine learning models continuously refine themselves based on current sales, inflation data, seasonal patterns, and even competitor activity. So, rather than guessing based on last year’s performance, retailers can anticipate demand with far greater accuracy, down to the SKU and store level.

The results are measurable. At o9, retailers using our demand planning solution have achieved a 0.9% increase in gross margin, 80% stockout reduction, 10% drop in inventory write-offs, and fully automated replenishment for food and merchandise categories.

The need for assortments that match demand, not assumptions

The days of one-size-fits-all assortments are over. With consumers discovering trends through social media and expecting hyper-relevance in every channel, merchandise planning has to be faster and more localized than ever.

AI-powered assortment optimization uses store-level purchase patterns, regional preferences, and real-time trend analysis to shape more responsive product mixes. Algorithms can recommend SKU adjustments based on online sentiment, cluster stores by demand patterns, and refine product depth and width dynamically.

Here’s what that means in the real world: less inventory waste, higher engagement, and assortments that reflect what customers actually want to buy, not what planners hope they will.

Smarter inventory allocation means precision over padding

Allocating inventory has always been a balancing act: too much, and markdowns rise; too little, and sales are lost. AI offers a third path—precision.

By predicting where products will perform best, AI enables planners to place stock more accurately from the outset. As sell-through data comes in, machine learning models adapt in real time, triggering inventory shifts and replenishment that keep high-demand locations stocked without flooding slower stores.

Retailers using AI for allocation not only reduce logistics costs and stock transfers, they also improve sell-through and limit margin erosion.

Replenishment that responds to reality

Replenishment often operates on lagging indicators and fixed cycles, but AI makes it dynamic. So, instead of reacting to stockouts, AI anticipates them.

Demand-driven replenishment models analyze live sales, promotions, and weather to proactively trigger restocks. AI can also connect with distribution centers and vendor systems to shorten lead times and optimize inventory flow.

This kind of responsiveness improves availability while minimizing excess. In practice, it helps retailers stay lean without compromising service levels.

What about AI-powered personalization?

While much of the focus around AI in planning centers on inventory, its long-term impact may lie in how it enhances customer experience.

AI-driven personalization enables more relevant product recommendations, localized pricing, and better store-level planning. Whether it’s curating an email campaign, adjusting markdown strategy, or assigning assortments to specific stores, AI empowers planners to fine-tune strategy in a way that aligns with customer expectations.

As competition intensifies and customer loyalty becomes harder to win, personalization powered by planning data becomes a critical differentiator.

The future of planning is already here

Not only are the leading retailers embracing AI in merchandise planning digitizing old processes, they’re also replacing intuition with intelligence. In doing so, they’re reducing waste, protecting margin, and elevating the customer experience at scale.

As these AI tools become more sophisticated, we see the gap widening between retailers who use them strategically and those who don’t. For merchandise planners, the question is no longer when to adopt AI. It’s how quickly you can build the foundation to make it work.

Because in a margin-conscious, experience-driven world, smart planning is what makes profit predictable.

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