AI has now become a core part of how modern planning teams forecast demand, build assortments, and move inventory.
But while most retail leaders know AI matters, many still see it as something abstract, something that happens “somewhere in the data.” The truth is simpler: AI is becoming a daily co-planner for brands and retailers. It automates the heavy analysis, spots exceptions, and helps planners make better decisions, faster.
Here’s how it works across the main planning workflows, and what to look for when evaluating AI-powered tools.
AI in Merchandise Financial Planning
Merchandise financial planning sets your top-down targets for sales, margin, and inventory. Historically, it’s been driven by intuition, growth percentages, and a lot of spreadsheet gymnastics. AI changes that through:
Smarter Forecasting
AI uses advanced models to forecast sales and demand using historical data. These models capture seasonality, trends, and external factors far better than static growth rates. The result is a financial plan that’s grounded in data, not just gut feel.
Scenario Simulation
AI lets you test “what if” scenarios instantly, like a delayed shipment, a flash sale, or a new product launch. Instead of manually tweaking numbers, the system projects how changes ripple across sales, margin, and inventory. You can save best-, worst-, and base-case scenarios side by side and plan contingencies with confidence.
Continuous Reforecasting
Rather than waiting for end-of-month reviews, AI automatically updates your plan as new data comes in. If a category starts outperforming or lagging, the AI adjusts the forecast and flags it for your review. Toolio customers, for example, use this to roll item-level reforecasts up to their merchandise plans, keeping top-down targets in sync with what’s actually selling.
Together, these capabilities help financial planners react faster, adjust targets earlier, and keep the business aligned with real demand.
AI in Assortment Planning
Assortment planning is where strategy meets execution and it’s one of the hardest areas to get right. AI brings clarity and precision to these choices with:
Bottom-up demand forecasting
AI generates granular forecasts for every style, color, and cluster. It chooses the best method for each product type, using attributes and analogs for new items, and time-series models for core styles. Forecasts automatically retrend as sales data flows in, so you’re always working from the latest signal, not last month’s assumption.
Assortment rationalization
AI helps you right-size your assortment by analyzing productivity. It shows where assortment width is diluting sales and where there’s unmet demand. For example, Toolio’s platform can highlight that 20% of SKUs contribute only 5% of sales, suggesting opportunities to trim low performers or invest in high-potential gaps. This leads to tighter buys and higher ROI on inventory.
Store clustering and localization
Machine learning can automatically group stores with similar demand patterns, saving planners from building clusters manually. These clusters aren’t static, they evolve as performance shifts. AI-based clustering ensures assortments are localized intelligently, aligning with real buying behavior in each region or store type.
Size curve optimization
Getting size ratios right is critical. AI cleans sales data to remove the impact of stockouts and infers true demand. It then recommends the right size distribution by store or region. This reduces missed sales from popular sizes running out and cuts markdowns on slow-moving ones.
In short, AI makes assortment planning more scientific while maintaining creative aspects. You spend less time guessing and more time curating.
AI in Allocation and Replenishment
Once assortments are set, the next challenge is getting the right products to the right stores at the right time. AI takes allocation from reactive to predictive.
Demand-driven allocation
Instead of using last year’s ratios, AI allocates inventory based on forecasted SKU-by-store demand. It looks at local traffic, demographics, and trends to predict what each store will sell. That means you’re sending product where it will actually move, not where it historically sat.
Automated replenishment
AI monitors every SKU and triggers transfers or reorders automatically, following rules you define. If one region is selling out and another is overstocked, the AI flags a transfer before it becomes a problem. This keeps in-stocks high and reduces manual workload for allocation teams.
Continuous retrending
As sales data updates, AI recalculates demand curves in real time. If a style starts trending on social media, the system adjusts allocation and replenishment immediately. Toolio customers use this dynamic reforecasting to prevent missed demand spikes and avoid sending more stock to stores where sell-through is slowing.
Sales curve automation
AI learns how products sell over time and shapes allocations accordingly. If swimwear sells 40% in June and 35% in July, the AI schedules stock to match that curve, ensuring the product is on shelves when customers want it.
This makes allocation feel less like a batch process and more like a living system that’s always tuned to current demand.
Cross-Workflow AI Capabilities
Beyond individual workflows, the best AI planning platforms offer capabilities that connect the entire process. Think of it as the “smart brain” that ties everything together.
Ensemble forecasting
Modern systems run multiple models at once (we call it “tournament forecasting”), and picks the best performer for each dataset. This tournament approach improves accuracy automatically, so planners don’t have to choose methods manually.
Promotional intelligence
AI analyzes past promotions and calculates true lift impacts. It learns, for example, that a 20% discount in footwear typically drives a 1.4x sales bump. When the next promotion is planned, it adjusts the forecast accordingly, helping planners buy the right amount without overstocking or running out.
AI assistant and exception alerts
Many systems now include AI chatbots that surface insights or answer questions in plain language. Toolio’s AI assistant, for example, can instantly explain variances or flag potential issues like “Store Cluster West trending out of stock next week.” For lean teams, these assistants act like extra analysts watching over the business.
Anomaly detection and data cleansing
AI automatically detects outliers, like spikes from data errors or missed promo tags, so bad data doesn’t distort forecasts. This “true demand” cleansing ensures decisions are based on reality, not noise.
Together, these cross-workflow features turn data into trustworthy, actionable insights across planning levels.
What to Look For in an AI Retail Planning Platform
Not all solutions are built equally. When evaluating planning software, focus on how the AI actually supports your team’s daily workflow.
1. Explainability
You need to see why the AI made a recommendation. Look for tools that show the drivers behind forecasts, like seasonality, trends, or promo effects. If planners can’t trust the output, they won’t use it. Toolio emphasizes transparency so users can see every input and model choice behind a forecast.
2. Planner-driven control
AI should assist, not dictate. The best systems let planners override forecasts or recommendations and feed those decisions back into the model. That keeps humans in charge while still improving model accuracy over time.
3. Continuous learning
AI shouldn’t go stale. Models should retrain regularly, ideally weekly, based on new sales and behavior data. This ensures forecasts stay relevant when market patterns shift.
4. Unified data environment
AI performs best when it has full visibility across planning levels. Look for platforms that connect financial plans, assortments, and allocations in one environment. Toolio customers benefit from this integrated structure because updates in one area automatically inform others, keeping plans synchronized.
5. Speed to value
AI tools need to deliver results fast. Modern, cloud-based solutions typically integrate and onboard faster so planners are up and running fast. Avoid platforms that require long, complex implementations or heavy IT involvement and customization.
6. Built-in exception management
The AI should flag risks and opportunities automatically, like overstocks, stockouts, or margin variances, so planners can act before issues escalate.
When each of these principles are in place, AI transforms how planning teams work, freeing them from low-value tasks and helping them focus on strategy.
What This Means for Your Planning Team
AI is now the engine of modern retail planning. It makes forecasts smarter, assortments leaner, and allocations faster. It helps you react in real time and plan with precision.
For retail leaders, the message is clear: don’t treat AI as an experiment. Treat it as part of your core planning workflow.
If you want to see how AI can fit into your team’s process, from merchandise planning through allocation, Speak to an Expert! We’ll show you how AI-powered planning looks in practice and what it can do for your business.
