Ashley Hetrick for BDO USA
Predicting demand trends with AI: when generative AI knows what’s selling before you
Imagine you’re a retail leader for a fast-growing wellness brand in 2030, you’re anticipating next week’s demand, and you know that a bad forecast could cost millions. You open your integrated inventory planning dashboard – an animated panoramic display of projections, simulations and product recommendations that your AI (artificial intelligence) co-pilot modeled while you slept. You start the day by reviewing your best-selling products, finding out which items are trending, and planning upcoming orders.
Dozens of product recommendations instantly appear on your screen, all generated by artificial intelligence, each suggesting subtle adjustments to orders and inventory based on carefully analyzed demand signals. Your generative AI bot works around the clock to synthesize data from multiple sources and provide insights for you to digest. While AI won’t make calls for you without set parameters, it’s great at pointing out patterns and anomalies that you can evaluate. Retail executives who want to fully automate their systems and decision-making through agentic AI have this option, but as a starting point, many will experiment with generative models first.
Take this notification, for example: Your system is set up for 5,000 orders in the Northeast US region for the MegaPink Hydration Cup, but market trends suggest that demand is cooling in this region. Adjust quantity or divert orders to the West Coast?
You pause – it’s an interesting flag. These hydration cups disappeared from shelves a few weeks ago after being promoted by a well-known fitness influencer, but trendy products are moving quickly these days. It appears that after an uptick, it is time to reduce orders for this SKU. Every retailer has felt the impact of social media sellouts, which rise quickly, then fall just as quickly, often outpacing traditional forecasting tools.
With a single click, you confirm the AI’s recommendation, then move on to the next action. Before you’ve even had your first cup of coffee, your generative AI collaborator has given you 20 quick, data-driven choices that align with real-time demand fluctuations.
This scenario is not far from current reality. Generative AI is already helping retailers leverage dynamic pricingfor example, continuing to use AI for demand planning is a natural next step, especially as retailers know that accurate inventory forecasting is essential to managing the bottom line. The overall cost of inventory distortion – the imbalance that occurs when retailers stock too much or too little product – has reached $1.7 trillion in 2024according to the IHL Group. These costs can significantly reduce margins, BDO United States reports.
In the not-so-distant future, retail leaders will work in tandem with generative AI to interpret demand signals and refine supply chain decisions in near real time. But to future-proof their operations center, retailers must first develop a solid understanding of the data they have.
From social flow to stock market flow
Sales, marketing and inventory data will always form the basis of retail operations, but today’s leaders can leverage data from sources far beyond their own internal systems. External channels such as social media, reviews and market reports can provide visibility into market developments that internal data cannot capture.
With generative AI, interpreting and synthesizing this wide variety of information is faster and easier than ever, allowing retailers to understand trending clusters, perform multimodal video analysis, and use sentiment analysis for a robust market picture.
By 2025, funding for generative AI in retail jumped to $33.9 billion globally, an 18.7% year-over-year increase, according to All about AI. This metric highlights the industry’s growing appetite for AI tools that can help retailers improve their decision-making.
Using advanced tools to help predict demand is a step change from historical methods. Retail leaders have traditionally relied on monthly sales and past demand metrics to forecast potential trends, but can now use real-time market signals to anticipate trends as they emerge rather than reacting retroactively.
For example, machine learning (ML) algorithms can analyze audio, captions and visual cues in social media videos, to detect keywords or descriptors that may signal changes in consumer demand. Generative AI can then combine these results with sentiment analysis and other contextual data to provide a comprehensive recommendation on how best to handle an impending spike or slowdown in demand. That said, most social data remains confusing. Models can overreact to hype, so retailers need safeguards to prevent false positives from turning into excess inventory.
For organizations looking to truly future-proof their operations, agentic AI can go even further by autonomously acting on selected recommendations based on parameters set by the retailer. In practice, a robot agent could move inventory between regions, generate a draft purchase order, or update supplier quantities when SKU velocity exceeds a certain threshold.
In the past, this level of analysis and execution required hours of manual labor. Once a retailer identified a change in demand, they still had to adjust dozens of different orders from multiple suppliers, call suppliers, import new SKUs, and much more. AI has changed that. Its ability to spot trends in advance can be a major competitive advantage, but retailers still need the right people, processes and mindset to turn that information into action. To support this level of autonomy, retailers will also need clean, connected data and strong governance to keep AI-driven actions consistent, traceable and compliant.
Translating social signals into smarter predictions
To effectively leverage generative AI, retailers must take deliberate steps to prepare their organization. Consider the five steps below to get started.
Step 1: Build a Strong Database
AI is only as good as the data it ingests, which means input must first be clear and precise. By integrating their data across systems, retailers can connect sales, marketing, inventory and external sources, allowing AI to use various data inputs to get a clear picture of demand trends. For most retailers, this requires cleaning up inconsistent product data, eliminating duplicate SKUs, and connecting systems that have long operated in silos. Unifying data into a master data repository allows AI to generate meaningful recommendations. A strong database must also include robust data governance. Retailers must establish roles and responsibilities for consistency, reliability and compliance.
Step 2: Define risk appetite
Retailers should evaluate their willingness to act on signals generated by AI models, particularly those from fast-moving sources like social media. These social signals can be noisy or short-lived, and the cyclical nature of customer trends creates inherent risk for fast-moving players. By the time a retailer has adjusted its inventory orders to meet burgeoning demand, customers’ attention may have already turned elsewhere. By striving to balance speed, experience and judgment, managers can establish a tolerable basis risk exposure and act more strategically when determining which opportunities are worth pursuing.
Step 3: Train and test AI models
A commitment to training and experimentation is essential to support any AI use case. Models need guidance to help them distinguish between real changes in demand and temporary noise. For retailers using agentic AI, autonomous agents should also be tested to ensure they follow escalation rules and avoid unintended actions. Explicit instructions and regular model testing can allow retailers to standardize processes, mitigate errors, and enable their AI models to make repeatable recommendations.
Step 4: Label and Analyze the Data
Forward-thinking retailers must look beyond the top trends and identify the underlying factors that influence them. Promotions, seasonal changes, and marketing campaigns can all skew demand data, creating spikes that aren’t purely organic. Assuming that every spike is worth paying attention to can lead retailers to make decisions based on fleeting trends or inorganic shifts in consumer appetite. Careful analysis, coupled with human judgment, is essential for AI-driven demand planning to work as intended. To better understand data, retailers should invest in upskilling their teams to develop the necessary technical knowledge. Ideally, teams should be comfortable working with AI independently to interpret its recommendations and add their own judgment to inform decision-making.
Step 5: Iterate and Evolve
Implementing generative AI for demand forecasting is a journey. Organizations must always evolve and iterate on their agent models and rules to redefine guardrails and improve decision thresholds and triggers. Retailers should conduct controlled experiments to validate AI results and encourage collaboration across teams to share insights, refine data governance, and adjust strategies over time. Using generative AI is a learning process. So, the more retailers experiment, the more the model learns and can better adapt recommendations to changes in the market.
This story was produced by BDO United States and revised and distributed by Stacker.