Retailers are under pressure. With market shifts, supply chain issues and unpredictable consumer behavior, retail demand planning has never been harder. Yet most are still leaning on outdated systems that can’t keep up. According to Retail Dive, more than half of retail and CPG executives plan to invest in AI tools for marketing and financial forecasting this year.

One invent.ai client shared that they’d reached the end of Excel. Literally. Even with good data, they couldn’t keep pace. It wasn’t just a tool problem. It was a structural one. Manual spreadsheets and siloed insights created more questions than answers.

This is where AI-decisioning changes everything. It connects systems, people and data to give retailers the confidence to make better decisions.

Why forecasting models fall short

Traditional forecasting methods can’t keep up with the pace of retail. Static tools often rely on outdated assumptions and can’t adapt to daily shifts in consumer behavior or disruptions across the supply chain.

Retailers face volatile conditions driven by inflation, evolving customer expectations and retail tariffs. Legacy models fail to reflect these industry dynamics. To move forward, retailers need responsive models designed for speed and clarity.

How modern retailers are changing course

Leading teams are replacing legacy models with forecasting approaches that center on real-time data and AI-powered forecasting. They bring together inputs from brick-and-mortar, e-commerce and supply chain systems to surface insights faster.

Retailers also prioritize cross-functional planning. When merchandising, planning and finance teams align their actions to a data-driven forecast, decisions become clearer and more accountable. Robust forecasting strategies would need tens of millions of accurate decisions made daily. The only way to make that many decisions is to turn to AI. Why? The answer is simple. It’s because the human brain can't process information as quickly as it needs to happen to make a meaningful impact on your revenue. Plus, there are multiple data points that must be considered at once, and it’s just not physically possible for your brain to assimilate that amount of information.

Step 1: Build your forecasting foundation with the right data

Forecasting accuracy depends on how complete and current your data points are. Aggregating input from point-of-sale, inventory systems, customer behavior, market trends and fulfillment improves visibility while reducing blind spots. Invent.ai superpowers automate this process and prompt better decisions, freeing up teams from manual work and allowing faster scenario modeling, more reliable demand signals and better downstream planning.

Step 2: Gain strategic edge with AI-powered forecasting

AI-powered insights give retailers the ability to forecast demand changes before they surface. When applied effectively, qualitative forecasts lead to more accurate inventory decisions, stronger pricing strategies and better customer experience.

By  using AI to monitor market signals, retailers can adjust before problems escalate. More agility means margin protection and more adaptive merchandising. Rather than relying on lagging reports, AI helps prompt timely decisions that reflect current behaviors and localized market changes.

Top performing teams are now applying AI-powered forecasting to assortment planning and identifying gaps before they affect store performance. Assortment strategy best practices use data to align localized demand with inventory decisions that drive efficiency and revenue.

Step 3: Make forecasting collaborative

Disconnected teams mean disconnected decisions. To succeed, forecasting needs to be collaborative across functions—from merchandising and finance to logistics and other retail roles. But collaboration can’t happen without infrastructure to support it.

Forecasting collaboration relies on system visibility, centralized data and aligned KPIs. Retailers that succeed in this area create shared accountability across departments. Teams with real-time access to updated forecasts can adjust their plans quickly enough to save margin and even grow profit dollars. AI tools play a key role by supporting consistent, cross-functional decisioning environments.

Step 4: Expand your forecasting playbook

Retailers ready to take the next step go beyond inventory planning with their forecasting models. They're applying insights to promotions, labor planning, store planograms or whole-store layouts and fulfillment strategies. Instead of focusing only on prediction, the goal is to influence retail outcomes through connected decision-making.

A growing number of retailers are also combining AI-driven modeling with input from frontline teams. These qualitative methods bring store-level context into broader forecasting logic. The result is a more flexible, more grounded approach to planning.

Apply AI-powered forecasting and retail decisioning with invent.ai

Retailers don’t need to guess anymore. With AI-powered forecasting models and an integrated decisioning platform, they can move with speed and confidence.

Every forecast becomes a strategic lever. Whether managing supply chain disruptions or navigating market trends, teams equipped with the right data and tools make clear, timely decisions that protect revenue and keep them ahead.

Want to strengthen your retail forecasting capabilities? Connect with an invent.ai team member to see how our solutions can help.