> ## Content Index > Fetch the complete content index at: https://www.cognativ.com/llms.txt > Use this file to discover other available public pages before exploring further. # AI Co-pilots for Holiday Shopping: 2025 is a Turning Point for Retailers - URL: https://www.cognativ.com/blog/ai-copilots-holiday-shopping-2025/ - Published: 2026-10-30T20:41:53.000Z - Updated: 2026-10-31T09:59:52.000Z - Description: Retailers spent the 2025 peak season wiring AI gift-finders and agents into the basket. Here is what worked, what broke and how agentic checkout shapes 2026. - Author: Kevin Anderson - Tags: News The 2025 holiday season was the first where AI was not just a novelty widget on the website. It sat between customers and products as gift-finders, chat assistants and "shopping copilots." Before the season, almost every large US retailer said it would lean on AI for personalization, pricing and service during peak, while some were already building experiences optimized for AI discovery rather than human browsing. The opportunity was huge, but so were the risks: hallucinated recommendations, broken journeys under peak load, and AI that optimizes for clicks instead of trust. This article was first published on November 28, 2025, in the middle of Cyber Week. It was updated on October 30, 2026 with the season's final results and a dated look at what has happened since, so it reads as a record of the 2025 turning point rather than as new news. ## Key takeaways Retailers need a clear strategy for how AI supports customers across the entire journey, not just a chatbot tucked in the corner of the site. The leaders connected AI assistants to real-time inventory, pricing and logistics so that recommendations were accurate, shippable and profitable. Those who rushed in without solid data and architecture felt the downside first in customer support, and then in their P&L. - Nearly all large US retailers surveyed (97%) planned to use AI in the 2025 holiday season for personalization, chatbots, inventory and pricing optimization. - AI-powered gift-finders and assistants became a real entry point to product discovery, both on retailers' own sites and inside third-party tools like ChatGPT and Gemini. - The competitive battle is shifting from "who has the biggest ad budget" to "who has the best AI-ready data, infrastructure and product content." - Final season data showed traffic from generative AI tools to US retail sites rising sharply, from a modest base, and brand-owned agents linked to faster sales growth. - To sustain results beyond the holidays, retailers need robust ecommerce platforms, modern data foundations and a clear AI-first roadmap, not just seasonal pilots. ## Holiday 2025: the first AI-native shopping season Peak season 2025 was different because AI was present at every layer of the stack: front-end search, conversational assistance, logistics optimization and risk management. For many shoppers, the "storefront" was no longer a homepage. It was a chat box that asks, "Who are you buying for?" and then curates the catalog around that context. The pre-season signals were clear. A [FedEx and C Space survey released on November 19, 2025](https://newsroom.fedex.com/newsroom/global-english/97-of-large-u-s-retailers-to-use-ai-this-holiday-season?ref=cognativ.com) found that 97% of large US retailers surveyed planned to use AI to enhance the shopping experience that season, from service chatbots to pricing and recommendations. The same report said four in 10 consumers planned to use AI for deals, gift ideas, product comparisons and brand research. Demand itself was strong. The [National Retail Federation reported on December 2, 2025](https://nrf.com/media-center/press-releases/thanksgiving-holiday-weekend-draws-a-record-203-million-shoppers?ref=cognativ.com) that a record 202.9 million consumers shopped during the five days from Thanksgiving through Cyber Monday, up from 197 million a year earlier. ## How retailers are using AI to win the basket AI in holiday shopping is no longer just about recommending "similar products." It spans the entire journey from idea to doorstep, and retailers are layering several use cases on top of each other. ### Gift-finder copilots and conversational discovery AI gift-finders help customers turn vague intents like "something under $50 for a colleague who loves cycling and coffee" into specific product bundles. These copilots can: - Guide customers through questions, narrowing down category, budget and preferences. - Explain product differences in clear language, particularly in complex categories like electronics or beauty. - Keep context across multiple queries, working closer to a human store associate than a keyword search box. Building this kind of experience usually needs more than off-the-shelf site search. Catalogs, attributes and content must be rich enough for a language model to reason over them, and the assistant must be able to call live systems for stock and price. That is the core of [AI agent development](https://www.cognativ.com/ai/agent-development/) for commerce: an assistant that can act on real data, not just chat about it. ### Dynamic pricing, promotions and inventory-aware recommendations Behind the scenes, AI optimizes what gets shown, at what price and when. Typical capabilities include: - Real-time repricing based on demand, stock, competitor signals and margin rules. - Promotion recommendation engines that suggest discounts likely to convert without eroding profitability. - Inventory-aware recommendations that steer demand towards products with healthier stock or shorter lead times. Without solid data engineering, these systems fragment across departments, so retailers increasingly centralize ecommerce, logistics and marketing signals for models to consume. ### Personalization across channels Customers do not think in "channels"; they simply expect experiences to feel consistent. AI now powers: - Personalized homepages and app surfaces based on historical behavior. - Tailored email and push campaigns generated in near real time. - Call-center and in-store associate tools that surface relevant offers for each interaction. Getting this right requires more than clever prompts. It needs coherent data models, a clear handling of consent and identity, and architectures designed for continuous experimentation. Our guide to [using an LLM with private data](https://www.cognativ.com/blog/how-to-use-llm-with-private-data/) covers the patterns that keep customer data under control while still feeding personalization. ![A shopper's vague gift request goes to an AI copilot, which asks questions, checks live stock and price, and returns a gift bundle.](https://storage.ghost.io/c/cc/dc/ccdca949-0fe4-4119-8990-70a44ad75060/content/images/2026/10/ai-copilots-holiday-shopping-2025-1.png) A gift-finder copilot clarifies intent, checks live data and returns products the retailer can actually ship. ## Risks retailers cannot ignore The speed of AI rollout in 2025 was impressive, but the risk surface expanded just as quickly. Treating AI copilots as harmless "helpers" underestimates their power to shape purchasing behavior and brand perception. ### Hallucinations, brand risk and compliance If an AI assistant fabricates product claims, misstates return policies or invents discount codes, the retailer owns the consequences. Customers will not excuse hallucinations just because "the AI said it." Retailers therefore need: - Guardrails on what the assistant is allowed to say, grounded in an authoritative knowledge base. - Clear escalation flows to human agents when confidence is low. - Testing regimes similar to software QA, but focused on AI behavior and content safety. Observability and governance make a real difference here, helping teams catch failure patterns early and measure AI against business KPIs rather than vanity metrics. An [enterprise AI governance framework](https://www.cognativ.com/blog/enterprise-ai-governance-framework/) gives that work owners, review steps and audit trails. ### Data privacy, consent and regional rules Holiday campaigns push personalization to the edge, and AI amplifies regulatory exposure, especially in regions with strict privacy regimes. Retailers should maintain transparent consent and preference management for AI-driven profiling, separate analytical and operational data, and map AI use cases to rules on automated decision-making and explainability. For retailers in several geographies, an AI strategy that aligns legal, data and engineering teams is a prerequisite for scaling beyond pilots. ### Operational fragility at peak load AI adds new points of failure: model hosts, vector databases, feature stores and orchestration layers. Under peak load, these systems must scale as reliably as payment gateways or inventory APIs. Typical issues include: - Latency spikes that make chat-based journeys feel sluggish. - Rate limits or capacity constraints from third-party AI providers. - Model timeouts causing blank or fallback responses at the worst possible moment. This is where capacity planning, caching and fallbacks in your [AI infrastructure](https://www.cognativ.com/ai/infrastructure/) matter, not just for long-term innovation but for getting through Black Friday without degraded performance. ![A shopper question is checked against an authoritative knowledge base; a confident answer goes back to the shopper, while low confidence routes to a human agent.](https://storage.ghost.io/c/cc/dc/ccdca949-0fe4-4119-8990-70a44ad75060/content/images/2026/10/ai-copilots-holiday-shopping-2025-2.png) Guardrails ground each answer in approved data and hand low-confidence cases to a person. ## Building the AI backbone for peak season and beyond The retailers happiest with their AI investments months later are the ones that treated the season as a proving ground for long-term capabilities, not a one-off campaign. ### Data and infrastructure foundations Effective AI shopping experiences depend on well-structured product information, customer behavior, supply chain signals and content. Many organizations discover during holiday readiness that their systems were built for static sites, not AI-native personalization. Key priorities include: - Cleaning and enriching product catalogs so attributes are machine-readable and consistent. - Data pipelines that keep stock, price and content fresh in near real time. - Infrastructure optimized for AI workloads, from embedding search to recommendation models. ### Choosing an AI-first architecture Retailers have three broad options: a thin integration of third-party AI assistants with minimal backend changes; bespoke AI components woven into existing ecommerce platforms; or a fully AI-first architecture where discovery, merchandising and content are designed with AI from the ground up. Each path trades off control, speed and cost, so the choice should follow long-term goals and ensure what was built for holiday 2025 still adds value in 2026 and beyond. ![Three parallel columns: thin integration of third-party assistants, bespoke AI components in the existing platform, and a fully AI-first architecture.](https://storage.ghost.io/c/cc/dc/ccdca949-0fe4-4119-8990-70a44ad75060/content/images/2026/10/ai-copilots-holiday-shopping-2025-3.png) Retailers can bolt on assistants, build AI into current platforms or redesign around AI from the ground up. ## What this means for retail strategy in 2026 Once customers get used to natural-language gift-finders and personalized journeys, they will not want to go back to static catalogs and generic promotions. The bar for an acceptable digital retail experience rose after the 2025 season, and the gap between AI leaders and laggards is widening. For leadership teams, the question is no longer whether to use AI in holiday shopping, but how to embed it into the core of ecommerce strategy: platform choices, data investments, organizational design and vendor selection. Retailers that approach this systematically can scale one successful holiday campaign into a durable AI advantage across the year. ## What has happened since (updated October 30, 2026) The figures below come from each company's own release and use different data and definitions, so they should not be added together. ### How the 2025 season ended On January 7, 2026, [Adobe reported](https://news.adobe.com/news/2026/01/adobe-holiday-shopping-season?ref=cognativ.com) that US online spending from November 1 to December 31, 2025 reached a record $257.8 billion, up 6.8% year over year. Traffic to US retail sites from generative AI tools rose 693.4%, although Adobe noted that the base of users remains modest. A day later, [Salesforce estimated](https://www.salesforce.com/news/stories/2025-holiday-shopping-data/?ref=cognativ.com) that AI and agents influenced 20% of global holiday retail sales, or $262 billion. It reported that traffic from AI search tools converted nine times more often than social referrals, and that retailers with their own AI agents grew 6.2% year over year against 3.9% for those without. Salesforce notes that these figures are extrapolated from its platform data. On January 12, 2026, the [NRF said](https://nrf.com/media-center/press-releases/cnbc-nrf-retail-monitor-s-december-data-shows-strong-holiday-season-spending?ref=cognativ.com) its card-based Retail Monitor showed holiday sales up 4.1%. Its [October 15, 2026 forecast release](https://nrf.com/media-center/press-releases/nrf-forecasts-2026-holiday-sales-growth-as-consumers-remain-resilient?ref=cognativ.com) gives a different figure for 2025: up 3.1% to $972 billion. The two come from different releases and data, so we report both. ### Agentic checkout moved into the assistants The copilot story kept moving from recommendation to purchase. On September 29, 2025, [Stripe and OpenAI introduced](https://stripe.com/newsroom/news/stripe-openai-instant-checkout?ref=cognativ.com) Instant Checkout in ChatGPT and the open Agentic Commerce Protocol, starting with US Etsy sellers. On January 11, 2026, [Google announced](https://blog.google/products/ads-commerce/agentic-commerce-ai-tools-protocol-retailers-platforms?ref=cognativ.com) the Universal Commerce Protocol, co-developed with Shopify, Etsy, Wayfair, Target and Walmart, to power checkout in AI Mode and the Gemini app with retailers as seller of record. Google says UCP works alongside MCP and A2A; our explainer on [how the Model Context Protocol works](https://www.cognativ.com/blog/how-model-context-protocol-works/) covers the plumbing these agents rely on. ### Looking at the 2026 holidays On September 28, 2026, [Adobe forecast](https://news.adobe.com/news/2026/09/adobe-us-holiday-shopping-season-to-hit-record?ref=cognativ.com) $275.1 billion in US online holiday spend, up 6.7%, with AI traffic to retail sites expected to rise 130%. In July, [Salesforce predicted](https://www.salesforce.com/blog/holiday-retail-predictions-2026/?ref=cognativ.com) that 20% of holiday ecommerce traffic will originate from AI chat agents and that one in three ecommerce sites will have a site-specific shopper agent live by Cyber Week. NRF forecasts total holiday sales up 4.2% to $1.013 trillion. All are forecasts. ![A shopper asks an AI assistant, which uses a commerce protocol to reach the retailer's catalog and checkout, while the retailer remains seller of record.](https://storage.ghost.io/c/cc/dc/ccdca949-0fe4-4119-8990-70a44ad75060/content/images/2026/10/ai-copilots-holiday-shopping-2025-4.png) Commerce protocols let assistants complete checkout while the retailer stays the seller of record. ## Frequently Asked Questions What is an AI shopping copilot? An AI shopping copilot is a conversational assistant that helps shoppers find, compare and buy products. It asks about budget, recipient and preferences, then recommends items grounded in live stock and pricing. How much did AI influence 2025 holiday sales? Estimates differ by source. Salesforce attributed $262 billion, or 20% of global holiday retail sales, to AI and agents. Adobe measured a 693.4% rise in generative AI traffic to US retail sites, from a modest base. What is agentic commerce? Agentic commerce is shopping in which an AI agent does more than recommend: it can compare options and complete checkout for the shopper. Protocols such as the Agentic Commerce Protocol and Google's Universal Commerce Protocol standardize how agents and retailers exchange that information. Should retailers build their own shopping agent? Third-party assistants drive discovery, but a brand-owned agent controls tone, policy accuracy and the final sale. Salesforce reported faster 2025 sales growth for retailers with their own agents, though that is correlation, not proof of cause. What are the biggest risks of AI shopping assistants? Hallucinated product claims or policies, privacy and consent violations, and failures under peak load. Grounded answers, human escalation and load testing reduce all three. What is forecast for the 2026 holiday season? Adobe forecasts $275.1 billion in US online spend, up 6.7%, with AI traffic up 130%. NRF forecasts total holiday sales of $1.013 trillion, up 4.2%. ## Where to go from here The 2025 season showed AI copilots are part of how people shop, and 2026 is extending them to checkout. If you are planning how AI will shape your next holiday season, or how to stabilize the pilots you already launched, this is a good moment to take stock of your architecture, data readiness and product roadmap. Start with catalog quality, grounded answers and peak-load resilience, then decide where an owned agent adds value over third-party assistants. If you want help designing an AI-first roadmap that fits your business, not just this year's peak window, [talk to Cognativ about your retail AI plans](https://www.cognativ.com/contact/).