Retailers have invested heavily in forecasting, data and planning technology. Yet inventory still ends up in the wrong stores, replenishment decisions lag behind demand and markdowns happen too late to protect margins. Better information doesn't automatically lead to better execution.

At the 2026 Retail AI Leaders Summit in New York City, retail executives discussed how their organizations are using AI to improve planning, manage inventory and respond to changing conditions. Their experiences highlighted several challenges that retailers continue to face, from disconnected planning processes to building trust in AI recommendations.

Five lessons stood out from the discussions:

1. Seasonal plans must account for changing demand

Retailers spend months preparing for seasonal demand, but customers don't always follow the calendar. A specialty retailer described how weather influences the start of its selling season. An unusually warm weekend can bring customers into stores weeks earlier than expected, while colder conditions can delay demand. Regional differences add another layer of complexity, with some regions entering peak season before others.

For planning teams, unexpected demand creates immediate inventory challenges. Products may be available across the network but not in the locations where customers need them. Pre-season forecasts provide a starting point. Once the season begins, current sales, inventory positions and updated demand forecasts should influence replenishment, allocation and transfer decisions. Retailers that adjust inventory as demand develops have more opportunities to capture sales and avoid excess stock when the season ends.

2. Connected planning prevents conflicting decisions

Forecasting, assortment, allocation, replenishment and pricing influence one another, yet many retailers manage these functions through separate systems and teams. Consider a product selling more slowly than expected. Merchandising may be preparing a markdown while replenishment continues sending additional units to stores. Each team follows its established process, but the combined decisions increase inventory exposure and put further pressure on margins.

Retail executives discussed how disconnected workflows make it harder to coordinate decisions across departments. Connected planning allows teams to evaluate how changes in one function affect others. A markdown decision, for example, should account for incoming inventory, current store availability and expected demand. Replenishment settings may also need adjustment to prevent additional stock from arriving after prices have been reduced.

AI can evaluate these relationships, identify competing actions and recommend adjustments that support shared revenue and margin objectives.

3. Explainability builds confidence in AI recommendations

Retailers make decisions using years of experience, category knowledge and an understanding of customer behavior. Introducing AI into established workflows requires teams to understand how recommendations are developed.

A growing jewelry retailer discussed the importance of transparency when introducing AI into its planning processes. Having the reasoning behind each recommendation available upfront gave employees the context to understand decisions, explore alternatives and evaluate potential outcomes. Giving employees opportunities to question recommendations, dig deeper into the reasoning and test what-if scenarios helped establish confidence in the technology.

For example, if AI recommends transferring inventory between stores, it has already evaluated the demand signals, inventory constraints and financial considerations behind that recommendation and can explain why the transfer is the best course of action. Planners can then dig deeper, explore alternatives such as adjusting replenishment and run what-if scenarios to understand how a transfer might affect availability at the sending location or how another shift in demand could change the recommendation.

Clear explanations and the ability to test scenarios help planners apply their commercial judgment while using AI to evaluate more options.

4. AI agents can reduce the time between identifying problems and taking action

Retail planning teams spend considerable time reviewing reports, investigating exceptions and deciding which issues require attention. An inventory imbalance may appear in a dashboard, but someone still needs to investigate the cause, assess available options and coordinate a response.

AI agents can take on that investigative work. By continuously monitoring sales, inventory and planning results, agents can identify emerging issues, explain what is driving them and recommend actions ranked by expected business value.

For example, when inventory accumulates in one region while demand increases elsewhere, an AI agent can investigate the imbalance, evaluate potential transfers and assess the financial implications of different responses. Recommendations can then be directed to the appropriate decision-makers for review and approval.

Remi, invent.ai's built-in AI agent, follows this approach. Working alongside the platform's forecasting and optimization models, Remi identifies issues and opportunities, explains recommendations, tests potential changes and helps carry approved adjustments into subsequent planning runs. Retail teams can spend less time gathering information and more time evaluating decisions that affect availability, revenue and margin.

5. Business outcomes should determine the value of AI

Forecast accuracy remains an important measure of planning performance, but accurate predictions have limited value when retailers cannot respond effectively. A forecast may identify stronger demand in a particular region, but the opportunity is lost if inventory cannot be repositioned before products sell out. Similarly, detecting excess inventory early provides little benefit if markdown or replenishment decisions are delayed.

Retail executives emphasized the importance of evaluating AI through measurable operational and financial results. Relevant measures include product availability, inventory productivity, markdown exposure, revenue, margin and time spent on manual planning activities.

Retailers also need clear ownership of decisions and processes for reviewing recommendations, approving changes and measuring results. Successful AI adoption depends on whether recommendations lead to timely decisions and whether those decisions improve business performance.

Putting retail planning into action with AI

Discussions at the 2026 Retail AI Leaders Summit showed how retailers are moving beyond isolated AI applications toward more connected planning and execution.

Seasonal demand requires flexibility. Inventory and pricing decisions need coordination. Planners need to understand and evaluate AI recommendations before approving changes. Technology must also help teams identify priorities and carry decisions into execution.

Retailers will continue investing in forecasting and planning capabilities. Greater value comes from connecting those capabilities across the business and responding to changes while there is still time to influence results.

A strong retail plan establishes direction. Consistent execution determines how well retailers respond when conditions change.