AI assistant for retail: best shopping assistant tools

AI shopping assistants are poised to change online shopping — what retailers must know

AI shopping assistants are poised to change how customers research and buy products. First, the market data shows rapid growth: the U.S. AI shopping assistant market was roughly USD 1,020.6 million in 2024 and could reach about USD 7,548.9 million by 2033, a projection that signals fast adoption across retail channels U.S. AI Shopping Assistant Market | Industry Report, 2033. Next, consumer habits already reflect this shift. For example, 19% of consumers reported using AI as their primary research tool in 2025, and 38% said they trust AI for general shopping help AI Assistant Statistics 2026: Adoption & ROI Data – Index.dev. These figures matter because they show AI moving from novelty to expectation.

How does this affect retailers? In short, it touches both front-line SALES and back-office operations. Shoppers tell us digital assistants save time in store, and 54% of buyers agree they speed shopping trips—so AI reduces friction and supports better product discovery The Future of AI In Ecommerce: Walmart Report. At the same time, executives are rethinking business models around AI to improve efficiency and launch new revenue streams: 76% of retail leaders say they are transforming their operations to leverage AI Retail and consumer products in the AI era – IBM. Therefore, AI is strategic and requires planning, not an experimental sidebar.

Retail teams must balance personalization and governance. For example, precision marketing can influence purchases, but fewer than 15% of consumers use retailer-specific assistants today, which means brands must earn usage and trust AI is Retail’s New Gatekeeper According to Acosta Group Study. Consequently, retailers should set budgets, KPIs, and pilot plans now. Also, teams should plan for cross‑channel consistency so product search behaves the same online and in-store. Finally, if your ops team faces heavy email loads, you can see how AI email agents save time and reduce errors by grounding replies in ERP and logistics systems; learn more about applying AI for logistics and customer service in our guide on improving logistics customer service with AI how to improve logistics customer service with AI.

AI shopping assistant tools: 10 Best AI options and chatbot examples

Retailers need a fast way to compare vendors. Below are practical tool types and examples, presented so you can match a solution to a business priority. First, remember that the best AI depends on your goal: support, search, recommendations, or visuals. Also, this list uses one-line pros and cons and highlights common platform integrations.

1) Ada — conversational customer support and AI chatbot. Pros: quick setup and strong conversational flows. Cons: requires data to train complex policies. Integrations: helpdesk and CRM platforms. 2) Klevu — search & discovery for product search and relevance. Pros: strong natural language processing and analytics. Cons: tuning needed for niche catalogues. Integrations: major e-commerce platforms. 3) LimeSpot — real‑time recommendations and personalized merchandising. Pros: proven lift in personalized product recommendations. Cons: pricing grows with SKU count. Integrations: email and storefront. 4) Vue.ai — visual AI for styling and visual search. Pros: great for fashion and visual product discovery. Cons: image tagging needs good photo quality. Integrations: PIM and catalog feeds. 5) Clerk.io — personalisation for product ranking and emails. Pros: easy personalization templates. Cons: smaller marketplaces may need custom rules. Integrations: email platforms and storefronts. 6) Walmart’s Sparky — retailer-specific assistant example; useful as a case study for brand AI. 7) Amazon’s Rufus — another retailer-specific assistant that shows scale and cross-channel data use. 8) Intercom AI — conversational workflows embedded in messenger environments. 9) Tidio AI — small-business friendly chat and bot automations. 10) Manifest AI approaches — vendor-neutral design patterns that combine generative AI with structured product data.

Use this quick list as a decision scaffold. If you need 24/7 customer support, choose a chatbot like Ada or Intercom AI. If you want to optimize discovery, pick Klevu or Clerk.io. For fashion and styling, Vue.ai leads with visual search and personalized product suggestions. For email and ops automation, virtualworkforce.ai offers no‑code email agents that draft accurate, context‑aware replies from ERP and WMS data; see our case studies on automating logistics correspondence for examples automated logistics correspondence. Finally, remember to map the tool to a clear use case, then test and iterate.

Best AI shopping assistant and AI chatbot — compare top AI tools for e-commerce businesses

Picking the right vendor starts with a simple comparison framework. First, score each tool on accuracy of recommendations, integration effort, data needs, customisation, cost per conversion, and privacy controls. Second, weight the criteria to match your priorities. For example, for a large fashion brand accuracy and visual search matter most. For a marketplace, low integration effort and catalog scale matter more.

Practical example: a mid-size retailer wants styling advice for customers. The right choice is a visual AI like Vue.ai that tags images, recommends coordinating items, and creates personalized product suggestions. Alternatively, if the retailer needs around the clock responses to customer query, choose a conversational chatbot like Ada or Intercom AI that can escalate to humans when intent is unclear. Also, your contact centre may want an ai-powered chatbot that integrates with the helpdesk and knowledge base to reduce handling time and improve customer satisfaction—virtualworkforce.ai helps teams automate order-related emails using data from ERP and WMS so replies stay grounded in accurate information; read about ERP email automation for logistics to see implementation notes ERP email automation for logistics.

Comparison criteria explained. Accuracy of recommendations depends on data quality and machine learning models. Integration effort measures time to connect product feeds, CRM, and helpdesk. Data needs include behavioral logs, catalog metadata, and images. Customisation assesses how much you can tune language, templates, and business rules. Cost per conversion captures total cost divided by incremental conversions. Privacy controls evaluate data residency, redaction, and consent. Use simple KPI targets: conversion lift, average order value, customer satisfaction (customer satisfaction and customer satisfaction metrics), response time, and reduced support costs. For instance, measure conversion rates and average order value before and after a recommendation engine test. Then run A/B tests for three to six weeks to collect statistically meaningful results.

Using AI to recommend the right products and boost conversions

AI helps match shoppers to the right products by combining behavioral signals, visual matching, and contextual intent. First, systems analyze clickstreams, searches, and purchases to infer preferences. Then, they use visual search to find similar items for shoppers who start with an image. Also, generative AI can create tailored style prompts that suggest complete outfits or complementary accessories. For example, precision marketing and product recommendation engines raise average order value by surfacing higher‑margin items at checkout; Acosta Group described AI as “retail’s new gatekeeper,” where personalization and precision marketing are competitive table stakes AI is Retail’s New Gatekeeper: Personalization and Precision ….

Playbook: collect clean data, then test quickly. Start by instrumenting site search and product feeds. Next, set personalization thresholds so recommendations match a clear intent signal. Then design a simple A/B test: control shows static recommendations; treatment shows AI-driven personalized recommendations. Track conversion and average order value as primary KPIs, plus customer reviews and repeat purchase rates. Also measure product discovery metrics like clicks on product suggestions and downstream conversion.

Technical notes: combine collaborative filtering with visual search and rules. Use natural language processing to interpret queries and product descriptions. Also include inventory signals so recommendations are in-stock and priced correctly. For operations teams drowning in order emails, using AI to automate repetitive replies both speeds responses and improves accuracy; virtualworkforce.ai’s no-code agents ground every reply in ERP and email history to reduce errors and time per email. As a result, teams can focus on exceptions, which improves turnaround and helps improve sales through faster, reliable communication. Finally, remember to test models frequently because product assortments and sales trends change quickly.

How to integrate AI assistant and shopping assistant tools for e-commerce

Integration requires a clear rollout checklist. First, choose a pilot use case such as site search, chat support, or personalized emails. Then map data flows between your PIM, CRM, helpdesk, and analytics. Next, select vendor(s) and choose whether to integrate through APIs, platform plug‑ins, or middleware. For email and logistics teams that need fast results, consider a no‑code AI email agent that connects to ERP/TMS/WMS, SharePoint and mailboxes for grounded replies; see our automated logistics correspondence guide for implementation details automated logistics correspondence.

Integration tips: prefer realtime product search and recommendation APIs for responsiveness. For catalogs that update often, use realtime feeds; for slower operations, batch syncs work. Always include fallbacks to human agents for ambiguous queries. Also, run privacy and consent checks during design to comply with regional regulations. For example, redact sensitive order numbers in public chat logs and require consent before using purchase history for personalization. Finally, test guardrails to avoid biased or incorrect suggestions; include accuracy guardrails and clear user prompts explaining the AI role.

Quick risk checklist: secure data retention policies, bias testing on recommendation outputs, and escalation paths to human support. Also evaluate vendor SLAs for uptime and data deletion. If you need to scale operations without hiring more agents, read our guide on how to scale logistics operations with AI agents for tactics that cross over into retail ops how to scale logistics operations with AI agents. Lastly, ensure roles for product, CX, and engineering owners so integration runs smoothly.

Choose the right AI: selection rules, governance and next steps for retail teams

Choose the right AI by following simple rules. First, start with outcomes: define KPIs like conversion lift, average order value and reduced support costs. Second, prefer composable tools that let you swap modules rather than lock you in. Third, require measurable metrics and SLAs tied to uptime and accuracy. Also insist on privacy transparency and clear data handling policies. For governance, assign product owners and CX leads and set weekly metric reviews so teams can respond quickly to changing sales trends.

Vendor selection checklist: business case, pilot scope, vendor shortlist, integration plan, KPI dashboard, and rollout timeline. Also demand demo data and a short pilot contract that lets you evaluate accuracy and integration effort. For ops teams, no-code solutions reduce time to value and limit the need for prompt engineering; virtualworkforce.ai provides no‑code email agents so business users can configure tone, templates, and escalation without heavy IT work. This model speeds pilots and reduces risk by keeping data connections under IT control.

Organisational tips: train staff on new workflows and include subject‑matter experts in tuning personalization rules. Create an escalation path for unclear customer interactions and set feedback loops so AI models learn from corrections. Finally, track both operational KPIs and qualitative signals like customer reviews and customer satisfaction. To choose the right vendor, look for transparency about machine learning methods and a roadmap for new features like voice assistants or deeper analytics. By planning governance and practical pilots now, retail teams will position themselves to improve sales, provide personalized experiences, and streamline operations as AI continues to mature.

FAQ

What is an AI shopping assistant and how does it help retailers?

An AI shopping assistant is a software agent that helps shoppers find products, answer questions, and complete purchases. It can offer personalized product suggestions, speed product search, and automate routine customer support tasks to improve conversion and customer interactions.

Which metrics should I track when testing an AI assistant?

Track conversion lift, average order value, response time, reduced support costs, and customer satisfaction scores. Also watch product discovery metrics like clicks on recommendations and repeat purchase rates to measure long‑term impact.

How do visual search tools like Vue.ai improve product discovery?

Visual search tools analyze images to find similar or complementary products, which helps shoppers who start with a photo. This method especially benefits fashion and home goods categories by offering matching suggestions and creating personalized product suggestions.

Can AI assistants handle order and logistics emails?

Yes. No‑code AI email agents can draft replies grounded in ERP, TMS, WMS and email history to reduce manual lookup time. For logistics and order queries, these agents streamline responses and lower handling time while keeping answers accurate and auditable.

Are retailer-specific assistants worth building?

Retailer-specific assistants can drive loyalty by using first‑party data for personalization, but adoption is still growing and fewer than 15% of consumers use brand-specific AIs today. Therefore, build when you can offer clear value and maintain transparency to earn trust.

How should I start a pilot for an AI shopping assistant?

Pick a narrow use case, map data flows, choose a vendor with quick integration options, and set KPI targets for a limited test period. Then iterate on rules and models based on results and user feedback.

What privacy safeguards are important for AI assistants?

Implement consent management, data minimisation, role‑based access, and retention policies. Also provide clear user messages about the AI role and offer escalation to human agents when needed.

How do AI tools improve average order value?

By surfacing complementary items, upsells, and higher‑margin alternatives at the right moment, AI recommendations nudge shoppers toward larger baskets. A/B testing shows personalized recommendations often increase average order value and repeat purchases.

Do small e-commerce businesses benefit from AI?

Yes. Even small merchants can use search tuning, simple recommendation widgets, or chatbots to automate customer support and improve discovery. Many AI offerings scale to fit smaller catalogs and budgets.

Where can I learn more about automating logistics and customer emails with AI?

Explore resources on using AI for logistics email drafting and automated logistics correspondence to see practical implementations. For example, our article on automated logistics correspondence explains how no‑code AI agents connect to ERP and email systems to speed replies and cut errors automated logistics correspondence.