How AI Is Helping Consumers, and Retailers, This Holiday Shopping Season

New Adobe research shows spending is expected to increase this holiday season with AI-powered shopping booming despite financial and AI-driven choice pressures on consumers and supply pressures on retailers. To meet this demand, many retailers are looking to AI to turn their data into strategies and tactics to capitalize on the anticipated spending increase. But, successful retail AI implementations are only as good as the data that feeds them.

That data needs an event mesh to provide crucial real-time contextual data mobility across a retailer’s complex enterprise systems, which is essential for AI solutions to operate effectively and guarantee success this holiday season.

As Spending Once Again Increases, New Consumer Shopping Habits Come Forward

In the U.S. alone, Adobe predicts the 2025 holiday shopping season is expected to bring in $253.4 billion in online sales this year, up 5.3 percent year-over-year. Cyber Monday is expected to be the biggest shopping day of the year (up 8.3 percent YoY) and Black Friday a close second, seeing growth (up 4.9 percent YoY).

One thing that has changed significantly in this year’s report is how consumers will be looking for products. The findings showed AI-powered shopping will boom this year, with AI traffic set to rise by 520 percent year-over-year and peaking in the 10 days leading up to Thanksgiving, but this will be more for research than actual purchases.

Retailers Countering AI with AI

Deloitte has been surveying retail buyers to gauge their strategies as they navigate the run-up to the holiday season, concluding: “Newer forms of AI and advanced analytics, barely on the radar for retail buyers in 2020, may be helping to build resilience for the 2025 holiday rush.”

The report found that 78 percent of surveyed retail buyers leverage AI-enabled tools to enhance buying activities, while 74 percent specifically utilize AI to address challenges stemming from trade policy–related changes. Respondents who are using AI report improvements in several key areas:

Better supply chain management. AI analytics can predict potential disruptions and optimize logistics.

Pricing optimization. Algorithms analyze market trends and consumer behavior to dynamically price items based on demand.

Product assortment optimization. AI solutions help streamline inventory management to ensure products are in the right place at the right time.

Demand forecasting. Predictive models anticipate customer demand to reduce overstock and stockouts

AI Implementations Are Easier Said Than Done

While these benefits sound like a great way to capitalize on tech-savvy consumers spending more this holiday season, implementing AI at scale for national or international retailers is not as simple as flicking on a switch.

This is especially important in the latest iteration of AI with the growth of agentic AI applications. An AI agent doesn’t just follow pre-programmed instructions, but thinks on its feet, makes decisions, and adapts to new situations.

From an architectural standpoint, agentic AI requires retail tech departments to re-think how to integrate solutions to maximize their potential.

Enter event-driven architecture (EDA), which provides a robust solution by decoupling agents from one another using an event broker, or network of brokers called an event mesh. This more loosely coupled approach avoids rigid dependencies that make the systems brittle, hard to scale, and difficult to maintain.

By using an event broker, agents can communicate asynchronously. This loose coupling enables the independent evolution of agents, allowing different teams to build and deploy their specialized agents without the need to coordinate complex dependencies.

AI Success Hinges on Being Event-Driven for Retail Data Movement

An event mesh, powered by agentic AI, is uniquely positioned to address the retail AI priorities for this holiday season. By creating a unified, real-time data network, an event mesh enhances supply chain visibility and agility, enabling quick responses to disruptions. The same system anticipates the AI-powered consumer shopper demands by continuously analyzing data from various touchpoints, allowing retailers to adjust strategies rapidly. In so doing, it provides a foundation for true omnichannel capabilities.

AI-Driven Retail Uses Cases

Let’s look at five ways AI and an event mesh can supercharge retailers during this anticipated record holiday season:

The Smooth "Silent Shopping" Experience

The “silent shopping” model uses AI to solve in-store operational issues discreetly, minimizing disruption for customers. Sensors noting sudden temperature fluctuations or in-store cameras detecting spills, for instance, generate events that are instantly routed to the relevant staff's wearable devices. An AI within the system prioritizes these alerts based on urgency and staff location, ensuring quick, quiet incident management that boosts both customer satisfaction and staff efficiency.

The Comfort of Real-Time Loss Prevention

Retailers are reinventing theft prevention by integrating multiple data streams for real-time risk analysis. Video feeds detecting suspicious behavior are correlated with point-of-sale and inventory data within an event-driven architecture. AI analyzes this combined stream to accurately identify potential theft events, immediately alerting security personnel with specific information, enabling a more discreet and effective approach to reducing losses.

Out of Stock? Not with Intelligent Inventory and Demand Prediction

AI-powered inventory management moves beyond basic tracking to predict demand and automate the restocking process in real-time. By integrating event data from IoT devices like shelf sensors and RFID tags, AI analyzes stock levels and predicts future needs, adjusting orders based on external factors like weather. This efficiency minimizes waste and prevents costly stockouts, significantly reducing labor and human error.

Meet the AI Store Manager

The AI store manager acts as a central orchestrator for all store operations. It processes real-time events from foot traffic sensors, smart shelves, staff wearables, and even weather forecasts. Using this contextual data, the AI makes dynamic decisions: optimizing staffing levels, adjusting store layouts, or initiating restocking. This continuous flow of information and instruction creates a highly responsive, efficient store environment, ushering in the future of retail management.

Seamless Omnichannel Personalization

Achieving “omnichannel excellence” means providing a unified, seamless customer journey across all touchpoints. AI uses an event backbone to capture interactions—from website clicks to in-store beacon pings—to build a continuously updated customer profile. This ensures that when a customer switches channels (e.g., moving from an abandoned online cart to a physical store), their context follows them, allowing sales associates to provide immediate, personalized recommendations.

AI Is Not Just for Christmas

Real retail success with AI hinges on the quality and speed of the data feeding those AI systems. For AI to deliver on the promises of optimized pricing, flawless supply chains, and personalized customer journeys, it requires a constant, contextual stream of data from every part of the enterprise—from warehouse sensors and supplier systems to in-store checkouts and online clicks.

Ush Shukla is a distinguished engineer at Solace. As an enterprise integration architect, Shukla has more than 13 years of experience leading diverse teams in the implementation of large-scale middleware solutions across varied business domains.