Retailers are under pressure to forecast demand in a world where conditions shift constantly. Consumer behavior changes quickly. Supply chain disruptions are common. Promotions, regional buying patterns and delivery delays all affect margin. Traditional forecasting or software tools are not built to handle this complexity.

Many retailers are now turning to AI to improve every customer experience and meet customer expectations with greater precision. As shared by Nvidia, “AI has made a significant impact in retail and CPG, with improved insights and decision-making (43%) and enhanced employee productivity (42%) being listed as top benefits among survey respondents.” AI’s use within forecasting software for retailers is particularly valuable.

Think back to archaic forecasting processes. Those reliant on manual data entry and analysis may fail to consider the whole state (of the business and broader/localized economies) that determines whether a product can be delivered or purchased in the right place, right time and at the right price. Retailers need systems that connect demand forecasts with planning, inventory and pricing management. But why? What should successful systems bring to the table and what questions should retailers be asking to choose the right software? 

Why retail forecasting often fails

Forecasting accuracy depends on access to unified, current data across all systems. Many retail teams operate in silos. POS data is often disconnected from ERP and e-commerce platforms. This fragmentation makes it difficult to respond to real-time changes in demand.

Consumer behavior is also more volatile than ever. Flash sales, influencer-driven spikes and regional trends can shift demand in hours. Static retail forecasts created months in advance cannot adapt to these changes. Retailers must rethink how they approach strategic retail decisioning to remain agile and responsive. But the problem can get worse. 

Operational blind spots begin to arise. Without visibility into the actual store and the whole supply chain network capacity, teams make decisions in isolation. This leads to mismatches between inventory and fulfillment capabilities, degrading the customer experience and stalling revenue growth due to longer retail cycles and the development of a negative feedback loop.

With time, the issues become all that anyone sees, and you’re left back at square one–with upset customers, problems maintaining margin and little, if any, visibility into what’s needed. In other words, bad experiences beget bad decisions and vice versa, and that negative feedback loop can affect business executives, retail leaders, store-level teams, consumers–practically anyone. 

What forecasting software must deliver

Forecasting tools must go beyond predicting sales. They need to reflect the constraints of demand across millions of variables. That includes lead times, storage, fulfillment costs, waste (due to expiration of perishables), changing of fashion trends, seasonality and much more. A forecast is only useful if it can be applied to improve planning or revenue growth.

Forecasting platforms must support speed and scope in decision-making. The right forecasting solution should use AI modeling that learns from both historical and sales data. This allows advanced reasoning models within the AI to adjust to changes in demand, shipping delays or supplier issues. Teams must also be able to simulate the effects of promotions, vendor delays or regional events. This helps them prepare for disruptions and plan capacity accordingly.

Effective forecasting improves inventory accuracy, gross sales, margin and value. These improvements reduce costs and increase customer satisfaction. In turn, retailers can avoid unnecessary markdowns and curb excess inventory by aligning stock levels with actual demand.

Questions  to ask your next retail forecasting software provider

Here are the key considerations for finding forecasting software that properly ticks all modern retail needs:  

• Is the software system agnostic, open and compatible with all existing tech and infrastructure? 
• Can it model or apply scenario planning features that simulate disruptions and demand shifts, helping to see value before it affects your margin? 
• Does the platform support collaboration across internal teams and external partners, including suppliers?
• Does the software forecast consider demand at the SKU level across regions and fulfillment centers?
• Can it incorporate real-world constraints like delivery windows and dock schedules/issues, such as locations without dock access (think retailers in the mall)?
• Does it support shared dashboards and transparent assumptions? 
• Can it learn from past performance to improve future forecasts?
• Does it actually move the needle, reducing work by making decisions on your behalf that are continuously focused on optimizing for profit? 

Support and strengthen your retail forecasting with invent.ai

Forecasting is not just about predicting demand. It is about executing against it. Retailers must embrace the next age of insight for making data-driven decisions–the use of AI-based decisioning. The value is profound and grows exponentially as time passes, and as new AI models come out, you need a partner that has been a prime leader in AI for retail, not a run-of-the-mill vendor that's suddenly interested in AI agents. Trust the experts in retail AI; connect with an invent.ai team member to see how our AI-Decisioning Platform can help you forecast with precision and deliver stellar retail experiences, for both the management team and your customers.