Retailers today face mounting pressure to deliver faster and more efficiently while keeping costs down. Yet inaccurate forecasting continues to undermine these goals at the foundation. Even worse, studies as recent as 2023 have found that some forecasting models can lead to an additional bullwhip effect in inventory management, causing trouble in knowing when to shift inventory between facilities, reports SSRN. However, the advent of agentic AI is helping to mitigate these effects and grow retail forecast accuracy into something new–an indisputable forecast and source of decisioning for the brand.

For companies that have traditionally relied on, or even built analytics capabilities to improve demand forecast accuracy, the opportunities available now through AI-enabled platforms are grand. The value of retail AI goes far beyond forecasting growth and intervening before decisions lead to lost sales.

For example, poor forecasts disrupt retail chains, inflate operational costs and erode customer trust. Meanwhile, overestimating demand leads to excess inventory, markdowns and wasted space. In some cases, you may have excess inventory at certain locations and too little at others. Sure, you could better manage transfers between stores with some random set of analytics that are based around rules, but what happens when the rules change?

The forecast falls apart more quickly than a thin receipt that's run through the washing machine. But you could have reliable and data-driven processes through Agentic AI that will actually maintain and grow your margins. Plus, AI-driven decisioning handles those transfers before they affect customer experiences. After all, understanding lead times and localized demand patterns matters now more than ever. In other words, retail demand forecasting powered by AI is the only true way to possibly handle all that information.

So, let's take a few moments to better understand the challenges in traditional forecasting and how you can put your best foot (or product) forward.

What’s holding forecast accuracy back?

Many retailers blame external volatility for poor forecasts. But the real issue lies internally. Disconnected systems, outdated tools and siloed data prevent teams from making informed decisions. Legacy forecasting methods rely on static historical data. They assume demand patterns are stable. In reality, promotions, seasonality and external disruptions shift demand constantly. Manual workflows make this worse,  slowing down response times and introducing forecast errors. Retailers need to move toward strategic retail decisioning that adapts to real-world conditions using better forecasting process improvements.

Data fragmentation is another major issue. Sales and buying teams often operate in separate systems. Inventory management systems may not connect with your POS. Replenishment data may be isolated, overlooked or even forgotten. These gaps lead to conflicting assumptions and poor execution when it's time to reorder products or make decisions about what's needed to keep moving forward. Together, AI-powered forecasting in retail makes everything easier. It may sound like an oversimplification, but it’s true. Better tools allow teams to evaluate forecast accuracy in real time.

Of course, we all know about the need for more accurate forecasts that can peer beyond the veil of retail chaos. But, how do you get more accurate forecasts?

AI improves forecasting and reduces waste

Before we get to AI, let's go back to basics. Messy systems lead to messy forecasts. If your retail planning system is full of half-hearted guesses and poorly organized data, how can you ever hope to be better informed?

The answer? Stop trying to wing it based on misinformation that becomes problems, and use retail AI to cleanse the data and bring order to chaos.

Retailers must improve retail forecast accuracy by shifting from reactive to predictive systems. This requires real-time sales data, integrated platforms and adaptive models that evolve with changing conditions. Applying forecasting process improvements and refining how teams collect and manage data helps align demand planning with actual results.

The first step is identifying where processes break down. Manual workflows, siloed data and disconnected tools are common barriers. Once identified, these gaps can be addressed through targeted changes and a more flexible approach to forecasting methods.

A unified data environment is essential. Connecting sales and buying systems gives planners a full view of operations. This integration makes planning more precise and execution more reliable while reducing the risk of lost sales. Machine learning (ML-based) forecasting models also offer key advantages. They process large volumes of data and adjust to new inputs. These models can account for external factors like weather, regional events or promotions, as well as social events, like the NCAA tournament in San Antonio in early 2025, which affected retailers across the spectrum of sporting goods, clothing, and grocers. This helps retailers anticipate demand shifts and respond accordingly with more accurate forecasting.

Why retail forecast accuracy matters across retail

Forecast accuracy refers to how well the forecast and its associated decisions, like reordering certain SKUs or allocating inventory to different stores, contribute to revenue growth. If the forecast is accurate, revenue should grow or remain stable, at the very least. AI also helps reduce absolute percentage error and provides more accurate service-level expectations.

Proper retail forecast accuracy reduces stockouts, lowers excess inventory and improves delivery performance. It also supports better DC operations and planning. With reliable data, teams can consolidate shipments, avoid the high costs of last-minute decisions and keep the business growing, which means you gain more data for AI to measure and continuously increase forecast accuracy over time.

Retailers also gain flexibility. As consumer buying habits shift, accurate demand forecasts allow for faster adjustments in sourcing, staffing and fulfillment. This responsiveness is essential in today’s competitive environment.

Grow revenue with stronger forecast accuracy from invent.ai

To truly improve forecast accuracy, retailers must evaluate performance. This means using structured forecasting process reviews and benchmarking across department and retail channels. Teams should also consider cross-functional reviews to reduce forecast errors and improve overall transparency.

Speak with an invent.ai team member to explore how advanced forecasting with AI can be a game-changer for your brand.