15 Prompts Shoppers Will Use with AI Agents This Black Friday.

Black Friday lands on November 27, and a growing share of shoppers will open an AI assistant before they open a retailer’s website. They will type one detailed request that includes budget, deadline, and preferences, then let an AI shopping agent do the comparing. Retailers whose products are easy for AI to read, verify and recommend collect that visit. 

Key Takeaways

• AI and agents influenced 20% of last holiday season’s retail sales, so businesses must prepare for Black Friday.
• AI-sourced visits to US retail sites grew 71% in September and converted 90% better.
• AI-driven traffic is forecast to grow 159% on Thanksgiving and 95% on Black Friday.
• McKinsey sees agents orchestrating up to $1T of US B2C retail revenue by 2030.
• The 15 prompts span discovery, personalization, checkout and after-sale, each rewarding one store capability.

The AI in Retail market size is anticipated to reach $82.72 billion by 2031, up from $18.64 billion in 2026, showing a 34.7% CAGR over 2026-2031. Artificial Intelligence’s role in the retail industry has moved beyond POCs into production environments. Be it layering LLMs on top of existing data pipes for personalizing content or computer-vision checkout for reducing waste & saving waste, such attributes collectively drive AI in the retail market.

McKinsey estimates that agentic commerce could orchestrate up to $1 trillion of US B2C retail revenue by 2030 and $3 trillion to $5 trillion globally, and it describes AI agents as the new gatekeepers of commerce. Adobe Analytics shows how quickly that future is arriving: visits to US retail sites from AI sources grew 71% in September 2026 and converted 90% better than other traffic. 

This POV blog through the fifteen prompts we expect to see most often this season and what each one asks of your product data, your systems and your brand. 

AI Shopping Agents on Black Friday: The Numbers to Know 

Last holiday season set the baseline. Salesforce estimates that AI and agents influenced 20% of retail sales, equal to $262 billion in global online revenue, and retailers running their own agents grew sales 59% faster than their peers. Adobe reports that generative AI traffic to US retail sites rose 693% over the same period, with AI referrals converting 31% better than other sources. 

This season points higher. Adobe forecasts AI-driven traffic growth of 159% on Thanksgiving, 95% on Black Friday, and 88% on Cyber Monday. Shopper intent supports the forecast. YouGov finds that planned AI use among likely US Black Friday and Cyber Monday shoppers has more than doubled in a year, from 16% to 34%, and a Statista Consumer Insights survey found that about 70% of American respondents could imagine using AI tools when buying Christmas gifts. 

The US market shows the scale. Salesforce data puts US online holiday sales at $294 billion in 2025, and McKinsey’s ConsumerWise survey found that 68% of US consumers used at least one AI tool in the past three months. 

Two patterns shape the planning that follows. First, AI currently shapes discovery and comparison, with checkout adoption building gradually, and the same McKinsey survey places discovering and deciding on brands, products or services third among AI tasks, at 19% of AI users. Second, visitors who arrive from AI assistants bring strong intent, which makes visibility inside AI answers one of the most valuable positions in retail. Gartner describes agentic commerce as a move from human-centric business models to dynamic flows powered by agents, and its consumer research shows that readiness varies by generation, income level, and community type. 

15 Prompts Shoppers Will Use with AI Agents This Black Friday 

Each prompt below reflects a shopping intent that AI agents handle well today, paired with the business capability it rewards. The prompts are grouped by stage of the journey so your teams can assign ownership. 

Product Discovery 

Prompt 1: Compare the Best Laptops Under a Set Budget 

“Find me a laptop under $900 for video editing with 16GB RAM and compare the top three.” 

This request turns a product category into a ranked shortlist, which makes it a core example of AI product discovery. The agent reads specifications, prices and reviews across stores, so structured attributes decide who appears. Processor, memory, display and warranty details should sit in clean fields that match your product feed. Brands that publish complete specifications become easy to compare, and agents reward that clarity with a place on the shortlist. 

Prompt 2: Find the Right Gift Within Budget and Deadline 

“What is a good gift for a 10-year-old who loves robots, under $50, delivered before December 5?” 

Gift prompts blend interest, age, budget and deadline into one request. Agents answer best when catalogs carry use-case tags, age ranges and live delivery estimates. Retailers who tag products by occasion and recipient interest give the agent the vocabulary it needs. A delivery promise attached to each product page builds shopper confidence and gives the agent a strong reason to recommend you. 

Prompt 3: Check Whether a Black Friday Price Is a Good Deal 

“Is this a good price for this noise-cancelling headphone? Show me what it cost over the last 90 days.” 

Shoppers now verify deals in seconds. AI agents compare current prices against price history and against other retailers, so pricing integrity becomes a brand asset. Consistent was-and-now pricing, transparent promotion dates and stable list prices in the weeks before Black Friday help your offer read as genuine. Structured price data lets an agent confirm your discount with confidence. 

Prompt 4: Compare Total Price Including Shipping and Returns 

“Find the lowest total price for this blender across stores, including shipping and return terms.” 

Total cost is the new headline price. Agents add shipping, duties and return terms to the sticker price before ranking options. Machine-readable shipping tables and return policies help your offer rank on the full picture. For US retailers, clear shipping fees by region and transparent sales tax details strengthen the comparison. 

Also Read: AI agent development cost

Prompt 5: Set a Price Target and Let the Agent Buy 

“Watch this camera and buy it for me when the price drops to $600 or lower.” 

This is the clearest preview of agentic commerce in action. The shopper delegates authority within a price limit, and the agent acts once the condition is met. Businesses prepare with product APIs, secure payment authorization, spending caps and audit trails. McKinsey places trust and responsible AI at the center of agent-led transactions, so governance deserves attention early in the build. 

Prompt 6: Summarize Verified Buyer Reviews

“Summarize what verified buyers praise and what they flag about this vacuum cleaner.”

Agents condense thousands of reviews into a single paragraph, and that paragraph becomes your reputation at the moment of decision. Verified-buyer labels, timely replies to feedback and accurate product descriptions strengthen the summary. Treat review quality as a conversion lever, and use recurring review themes to refine product copy before the sale begins. 

Personalization and Bundles

Prompt 7: Build a Gift Basket for a Specific Person

“Build a $120 gift basket for my mother, who loves gardening and tea.”

Bundle prompts reward catalogs that connect categories. The agent selects complementary items across your range, then checks the total against the budget. Relationship data between products, such as pairings, sets and accessories, gives agents a ready framework. Retailers with curated bundles and gift-ready packaging options gain a natural advantage in conversational commerce. 

Prompt 8: Restock Favorites From Past Purchases 

“Based on what I bought last year, what should I restock this Black Friday?”

Here the shopper shares history and expects personal relevance. An AI shopping assistant connected to loyalty and CRM data, with the customer’s consent, can propose replenishment lists and bundle offers. First-party data becomes a strategic asset, and consent management becomes a design priority. Retailers with unified customer profiles can deliver this experience inside their own branded assistant.

Also Read: How to Build an AI Voice Assistant App Like Alexa

Prompt 9: Match Size and Fit Before Buying

“I am 5 feet 9 inches tall and usually wear a medium. Which of these jackets will fit best?”

Fit questions sit close to the point of purchase, and the answer shapes both conversion and returns. Agents rely on size charts, reviewer fit notes and garment measurements. Detailed measurements per product, consistent size labeling and a feedback loop from past orders give the agent reliable material. Accurate fit guidance supports higher confidence at checkout and healthier return rates.

Checkout and Fulfillment 

Prompt 10: Stack Coupons, Loyalty Points and Card Offers

“Apply the best combination of coupon, loyalty points and card offer, then check out.”

Shoppers want the best price assembled for them. Agents read promotion terms and test combinations, so clear and consistent rules matter. Define stacking logic in structured form, keep expiry dates visible and align loyalty and payment-partner offers in a single source of truth. Retailers who make the best price easy to calculate become the easiest recommendation. 

Prompt 11: Confirm Delivery Dates and Find In-Stock Alternatives

“Confirm this arrives in Chicago before December 20, and show an in-stock alternative that ships faster if one exists.”

Real-time inventory and delivery data turn this request into a quick win. Agents query stock by location and compare promised delivery windows. Live inventory feeds, accurate cutoff dates and store-pickup options place your products on the shortlist. Accurate availability builds the trust that repeat AI recommendations depend on. 

Prompt 12: Ship One Order to Several Addresses With Gift Wrap 

“Send one gift each to three addresses, gift-wrapped, with a message card.” 

Gifting season multiplies addresses. A checkout that handles several recipients, wrapping options and personal messages in one flow works well for agents and for people alike. Clear APIs for address handling and gift options let an agent complete the task smoothly. Retailers who streamline this flow capture the high-value baskets that gift shoppers build. 

We have built a Flutter-powered mobile commerce platform enabling self-checkout, real-time inventory tracking, AI chatbot assistance, and seamless online-offline shopping experiences for our client Leroy Merlin. Read our case study to know about how we developed this platform.

After the Sale and Shopper Values

Prompt 13: Explain the Return Policy and Set a Reminder

“Summarize the return policy for this order and remind me a week before the window closes.” 

Agents quote policy language directly, so consistency across product pages, checkout and help content matters. Clear return windows, conditions and refund timelines written in plain language let the agent answer correctly and build shopper confidence. A shared policy source that feeds every channel keeps the answer uniform, whichever assistant the shopper uses. 

Prompt 14: Track an Order and Request a Delay Credit 

“Where is my order, and request a delivery credit if it arrives after the promised date.” 

Post-purchase service becomes a conversation between agents. Salesforce reports that agents handled a 142% surge in tasks such as returns and shipping updates during the 2025 holiday season. Order-status APIs, clear service commitments and a smooth route to a human specialist prepare your support operation for this volume. Strong after-sale service turns a seasonal buyer into a repeat customer. 

Prompt 15: Filter by Values, Packaging and Regional Shipping 

“Show me brands with recyclable packaging that offer free two-day shipping to my ZIP code.” 

Values-based filters reward detailed attributes. Packaging materials, sourcing claims, shipping coverage by ZIP code and delivery speed work as searchable fields for an agent. English and Spanish product data side by side widens your reach across the US market. Verified sustainability information, backed by certifications, makes your claims easy for agents to confirm. 

What These Prompts Ask of Your Ecommerce AI Stack

These prompts and capabilities form the foundation for AI agents for ecommerce, whether the agent belongs to a shopper, a marketplace, or your own brand.

• Structured product data. Titles, attributes, identifiers, pricing, availability and policies in clean, consistent fields, published in English and Spanish where your customer base calls for both. 
• Real-time commerce APIs. Live access to inventory, pricing, orders, returns and delivery estimates, so an agent can verify an answer at the moment it gives it. 
• Trusted retrieval. Catalog, policy and review content organized so that a branded AI shopping assistant answers from your single source of truth. 
• Governed action. Permissions, spending limits, audit trails and a smooth route to a human specialist. McKinsey describes an agentic AI mesh that coordinates memory, orchestration, traceability and governance at scale. 

Retailers generally choose among three paths: appear well inside the assistants shoppers already use, build a branded assistant on their own channels, or combine both. The combined path secures visibility in external assistants and ownership of the customer relationship at home. 

Black Friday AI Readiness Plan: Now and Into 2027

Black Friday is around the corner, and it’s time for your current stack. 

• Audit the top 100 products. Confirm complete titles, attributes, images and specifications.
• Align policies. Match shipping, delivery cutoff and return terms across product pages, checkout and help content
• Validate feeds. Check inventory and price accuracy, and publish promotion start and end dates in structured data.
• Test the fifteen prompts. Ask the major AI assistants, then record how your products and policies are described.
• Brief support teams. Prepare answers for AI-referred shoppers, from delivery dates to return windows.

The 2027 roadmap builds on that foundation: a branded AI shopping assistant, agent-ready APIs, personalization on first-party data, support for emerging agentic checkout protocols and analytics that track AI-referred traffic. Adobe’s September data shows why that investment deserves priority, with AI traffic producing 83% higher revenue per visit than other sources. 

How TechAhead Builds AI Agents for Ecommerce and Retail

TechAhead is an AI-native app and enterprise software development company, an OpenAI Services Partner and a Claude partner, with ISO/IEC 42001 certified AI governance, SOC 2 Type II and ISO 27001 certifications. More than 1,200 businesses rely on our US-headquartered team and our engineering talent across the globe. We deliver ecommerce AI development services that connect product data, commerce APIs and governance in a single architecture, so every answer an AI agent gives matches your live inventory, pricing and policies.

Our work includes custom AI agent development, conversational commerce experiences and the web and mobile applications that carry them. Explore our AI development services to see how we approach each stage, from strategy and design to deployment and ongoing optimization.

Proven in production: Heady, the conversational assistant inside Headlyne.ai

Headlyne.ai is an AI news app we built with Flutter, Node.js, and OpenAI language models. Its chatbot, Heady, lets readers ask questions about any story, request deeper context or background, and receive answers personalized by the app’s summarization and sentiment systems.

Heady runs alongside AI summaries, instant translation into 8+ Indian languages, audio briefs and an age-appropriate Junior Mode. The app earned Google’s Best App Award for 2024 in the Personal Growth category.

Read the full case study of the AI-driven new platform we developed. 

News and retail share the same core pattern: a person asks a question in plain language, and an assistant answers from trusted content in real time. In retail, that becomes a shopper asking an assistant to compare products, confirm a delivery date, or explain a return policy, which covers the prompts in this guide.

What retailers gain from building with TechAhead

• One architecture: Product data, commerce APIs, trusted retrieval and governance are designed together, so your assistant answers with verified information.
• Frontier model expertise: As an OpenAI Services Partner and a Claude partner, our engineers build on the models retailers want to use.
• Governance from day one: ISO/IEC 42001, SOC 2 Type II and ISO 27001 credentials back the permissions, spending limits, audit trails and human handoff we build into every agent.
• Multilingual reach: Headlyne.ai’s instant translation shows the capability behind English and Spanish product data for the US market.
• Full-stack delivery: We build the assistant, the APIs behind it, and the web and mobile apps that carry it.

Prepare Your Store for the AI Shopper

Shoppers are already telling AI assistants what they want, and the retailers whose products are ready for those requests collect the visits. All fifteen prompts in this guide rest on the same four foundations: structured product data, real-time commerce APIs, trusted retrieval and governed action. Retailers with those in place give AI agents accurate answers about price, availability, delivery and returns, and agents recommend the stores they can verify.

Talk to the TechAhead AI team to turn this readiness plan into a working AI shopping assistant for your business.