Black Friday and Cyber Monday are entering a new era. In 2025, retailers are turning to artificial intelligence to shape what deals shoppers see, when they see them, and even how much they pay. What was once a rush of door-busters is becoming a test of algorithms and trust.
A Turning Point in Holiday Marketing
For decades, Black Friday and Cyber Monday have stood as anchor points in the American holiday shopping calendar — moments when consumers bend entire budgets and retailers flex their margins. But in 2025, the battleground is shifting. What once was a spectacle of door-buster queues and fevered online clicks is gradually becoming a quiet dance of prediction engines, chatbots and dynamic offers.
Retailers and industry insiders say this year’s holiday sales season will be a test: how well artificial intelligence can anticipate, influence and unlock demand — and whether consumers will trust it enough to follow. In effect, the next round of holiday deals may be shaped less by advertising muscle than by machine intelligence.
Personalized Deals and the Algorithmic Divide
The most visible manifestation of AI’s role in this holiday season will be personalization — not merely showing more relevant products, but adjusting what deals different people see, in real time. Retailers are deploying AI-driven recommendation systems that parse past behavior, real-time browse signals, inventory levels and competitive pricing to tune both what deal is offered and when it’s shown.
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At large online retailers, that capability has already scaled. Chatbots and recommendation engines are now credited with influencing up to 20 per cent of orders during recent Black Friday–Cyber Monday periods, according to Salesforce. Generative AI tools are also being embedded in marketing channels like SMS: for example, one home-decor company tasked an AI tool with timing and customizing text messages to individual users based on browsing history. It reported “double-digit growth” in engagement.
But the richness of choice carries risks. Some retailers test dynamic pricing strategies that shift offers mid-sale, sometimes surprising consumers when items in their cart change price. Smaller brands, lacking refined models, may see misfires: putting deeply discounted items in front of bargain hunters and missing margin elsewhere. Meanwhile, consumers may grow wary of offers that feel too opaque, or that vary between shoppers in unexplained ways.
Trust, Friction, and Consumer Sentiment
Even as the engines hum behind the scenes, the human side of shopping carries weight. A core tension undercuts the AI promise: people want relevance, but they often distrust the motivations and mechanics of personalization.
Recent commentary frames this as the “AI paradox” — consumers want systems to simplify decisions, yet remain “sceptical of being manipulated,” especially in a high-stakes moment like holiday shopping. Some shoppers report using AI assistants for gift ideas or deal-finding, while others avoid them to preserve a sense of control.
Retailers are acutely aware that bad experiences could backfire: a chatbot that pushes deals too aggressively, or a pricing shift seen as unfair, might erode brand loyalty rather than boost it. A more subtle strategy may win the day: letting consumers opt in to AI recommendations, layering transparency into how deals are tailored, or letting human editors intervene for curated collections.
In past seasons, not all AI tests succeeded. One furniture brand deployed a negotiation chatbot (letting customers haggle over clearance pricing) but saw limited engagement in later years — drawing questions about novelty fatigue or confusing user experience. The lesson: automation must hide friction, not introduce it.
Back-End Intelligence: Inventory, Forecasts, and Fraud
Behind the customer interface, AI’s impact may prove even more consequential — and less visible. Retailers are leaning heavily on predictive logistics: models that forecast demand down to the hour, optimize stock across warehouses, and route shipments to anticipate surges. These systems help reduce overstock, prevent stockouts, and limit waste — all critical in a moment when margins are thin.
On the payments side, fraud detection systems powered by AI are essential. As shopping volume grows, so too do attempts at abuse. Generative models simulate fraud patterns, identify anomalies, and flag suspicious transactions before damage is done. Without them, a burst of fraudulent purchases during peak shopping could erode net revenue.
Yet these systems are not foolproof. AI models depend on training data, and rapid shifts in consumer patterns — during a sale, for instance — may produce false positives or negatives. Retailers must balance security and convenience, or risk declining legitimate transactions at podium.
