Today’s customers expect instant answers, personalized recommendations, and 24/7 customer support. For any growing business, meeting these expectations at scale can seem impossible without a massive support team. But what if technology could provide a solution?
In this deep dive, we break down how 15 different companies are using AI-generated support to do just that. From fashion brands like Victoria’s Secret to beauty retailers like Sephora, we’ll show you who is doing it right and the tangible results they’re achieving.
However, to begin, let’s walk through the core insights covered:
- AI is no longer a basic support tool. It works like a senior expert, handling complex tasks once limited to top agents.
- The AI support shift goes beyond websites. It powers multilingual voicebots, built for weak networks and low-end devices to reach more customers.
- Top companies treat AI support as part of a unified AI engine that handles tickets, writes marketing content, and manages inventory.
15 companies that use AI-generated customer support
1. Decathlon
Global sports retailer Decathlon struggled with a high volume of technical questions about its 10,000+ products. This overwhelmed their support team, leading to four-hour response times and customers abandoning carts due to poor customer service experiences. The team felt less like sports experts and more like a human FAQ page, answering the same questions repeatedly.
To solve this, Decathlon used Chatty’s AI to learn its entire product catalog, including all technical specifications and compatibility details. The AI provided instant, expert answers 24/7 and smoothly handed off complex cases to human agents, ensuring customers always received the help they needed without delay.
The results they achieved were very impressive:
- Over 500 conversations were handled automatically in the first 7 days.
- The AI achieved a 98.47% resolution rate.
- It generated €578.39 in revenue from its recommendations.
- The chat-to-sales conversion rate was 0.76%, surpassing industry averages.
2. Yoeleo Bikes
Yoeleo Bike sells high-performance cycling components where technical precision is critical. Customers needed absolute certainty about compatibility before making expensive purchases, but only senior staff could answer these complex questions. This created a major support bottleneck, causing hesitant customers to leave the site without buying.
Yoeleo leveraged Chatty’s AI to act as an on-demand technical specialist. The AI mastered every specification and compatibility chart in its catalog, giving customers instant and accurate answers to build their purchasing confidence. For more unique inquiries, the AI provided a seamless handoff to a human expert with all the necessary context.
Their success was reflected in the following metrics:
- The AI handled 90.38% of all technical conversations.
- It achieved an impressive 98.94% resolution rate.
- It assisted in $3,496.50 of revenue.
- The team saved 19 hours and 22 minutes daily on research.
3. ATK
Premium gaming gear retailer ATK realized its customers shopped primarily during late-night gaming sessions when the support team was offline. During peak hours, traffic would spike, but with no one available to answer urgent technical questions, cart abandonment rates soared to 60%. The company was missing its most valuable sales opportunities.
ATK deployed Chatty’s AI to serve as a 24/7 gaming expert. The AI was trained on all product specifications and gaming terminology, allowing it to provide instant support during the hours its customers were most active. This allowed ATK to engage its community around the clock and successfully capture sales that would have otherwise been lost.
The data demonstrated the value of 24/7 support:
- The AI handled 1,963 conversations, mostly during off-hours.
- It generated $8,163.99 in assisted revenue.
- It achieved a 66.57% resolution rate for complex gaming questions.
4. Meesho
E-commerce platform Meesho faced overwhelming demand in customer service, with tens of thousands of inquiries each day. Many customers were in smaller cities, where noisy environments and limited devices made call centers inefficient. Response times grew longer, and service costs climbed, a clear sign that teamwork in customer service needed to improve.
To address this, Meesho introduced a generative AI voicebot for after-sales support. It used large language models with in-house ASR, NLP, and TTS to understand regional contexts and run smoothly on low-end smartphones. The bot launched in Hindi and English, with plans to add six more Indian languages.
The results highlighted the impact of AI-driven voice automation:
- The AI handled around 60,000 calls per day.
- Call center costs per interaction dropped by 75%.
- Resolution rate reached 95%, with customer satisfaction improving by 10 points.
5. Temple & Webster
Australian online furniture retailer Temple & Webster struggled with a surge in repetitive pre-purchase queries about product dimensions, materials, and styling advice. The volume overwhelmed the support team and slowed responses, reducing conversion opportunities.
The company implemented generative AI to streamline customer engagement. ChatGPT was integrated into live chat to handle roughly one-quarter of all inbound queries. At the same time, AI also generated product descriptions for over 200,000 SKUs and powered on-site mood board recommendations. An in-house AI team was established to scale automation across multiple service channels.
The results showed a measurable improvement in efficiency and growth:
- Customer support costs as a percentage of revenue dropped by 50%.
- Annual revenue reached AUD 600.7 million & net profit grew fivefold to AUD 11.3 million.
6. Amarra
Rapid growth at prom and eveningwear brand Amarra led to a flood of repetitive questions about orders, sizing, and returns. The support team struggled to keep pace, resulting in long wait times and missed opportunities to convert new shoppers.
Amarra deployed an AI-powered chatbot to manage high-volume inquiries in real time. The system provided instant answers to frequently asked questions, generated product descriptions, and analyzed customer feedback to refine its responses and recommendations.
The outcomes demonstrated significant time and cost savings:
- The AI handled 70% of customer inquiries without human intervention.
- Time spent creating product descriptions was reduced by 60%.
- Excess inventory levels dropped by 40%, supported by AI insights.
7. Amazon
With millions of shoppers browsing daily, Amazon faced an enormous volume of pre-purchase queries, from product comparisons to usage advice. Traditional search forced customers to sift through reviews and Q&A threads, limiting efficiency and lowering conversion rates.
Amazon introduced Rufus, a generative AI shopping assistant embedded directly in its app and website. Rufus was trained on Amazon’s full product catalog, customer reviews, Q&A content, and supplemental web data, enabling it to answer natural language questions, provide comparisons, and deliver personalized recommendations. In July 2024, it was rolled out to all U.S. customers.
The performance impact was substantial:
- Response generation speed doubled during Prime Day 2024 after infrastructure optimizations.
- Internal forecasts project Rufus will drive more than $700 million in operating profit in 2025.
8. Flipkart
India’s e‑commerce giant Flipkart struggled to scale customer support across hundreds of millions of users. A surge in queries across purchasing, product discovery, and returns overwhelmed human agents, leading to slower responses and missed opportunities.
Flipkart deployed “Flippi,” a generative AI shopping assistant integrated into its app and website. Flippi leverages the full product catalog, user data, chat logs, and browsing behavior to provide conversational, real‑time product discovery, recommendations, and query resolution. Alongside, AI‑powered chatbots manage peak volume and personalize customer journeys through ML‑based segmentation and recommendation systems.
The results demonstrate improved performance and reach:
- Flippi covers services for ~600M registered users.
- AI enhancements in recommendations and segmentation increased click‑through rates and personalized engagement.
9. Louis Vuitton
Global luxury brand Louis Vuitton aimed to preserve its high‑touch, personalized service while scaling support globally. Diverse customer languages, time zones, and expectations strained traditional agent workflows.
The company introduced AI‑driven chatbots that deliver 24/7 support, using generative AI to reflect the brand’s exclusive tone, and seamlessly escalate complex questions to human advisors. Additionally, a pilot program uses generative AI to draft bespoke “thank you” messages tailored to each client’s profile, freeing advisors from administrative work.
The impact has been measured clearly:
- Response times for customer queries dropped by over 60%.
- Advisors can now redirect significant time from administration to high‑value client interactions.
10. Procosmet
Italian beauty and haircare retailer Procosmet struggled with fragmented service tools and inefficient lead generation, limiting growth and customer tracking.
By switching to Tidio’s AI‑enabled chatbot platform, Procosmet unified its support tools and automated customer interactions. The chatbot answers FAQs, captures leads directly via friendly prompts, and drives newsletter sign‑ups. The streamlined setup significantly boosted performance:
- Overall sales rose by 23% after implementing chatbots.
- Monthly lead generation increased fivefold, from an average of 10–30 leads to over 100.
- Monthly conversions stabilized and increased by 27%.
11. Kiehl’s
Kiehl’s, well known for in‑store skincare consultations, needed a digital equivalent to offer personalized advice when customers weren’t able to visit a location.
They introduced Eve, an AI‑powered digital human, deployed in physical kiosks. Eve engages shoppers using conversational dialogue, assesses skin type, and recommends tailored skincare routines to mirror a Kiehl’s in‑store advisor.
The launch has generated strong early performance:
- Eve engaged in 2,000+ customer interactions within her first 2 weeks of deployment.
- Digital humans like Eve delivered more than 5 times the conversion rate compared to non‑interactive tools.
12. Victoria’s Secret
Victoria’s Secret set out to bring its one-to-one, in-store style of assistance to a digital audience that now reaches hundreds of millions of visits each year. Meeting that bar online required faster product discovery and more personal guidance at scale.
The brand partnered with Google Cloud to build a virtual shopping assistant on Vertex AI and rolled out AI visual search on the web and app. Shoppers can upload a photo and receive relevant recommendations, while the assistant guides conversations with suggestions that reflect each customer’s needs.
The early outcomes are notable:
- Visual search launched in 2023 on the site and app, offering picture-to-product recommendations.
- The site gets 500 million visits a year, giving AI a wide role in discovery.
- The company runs 1,350+ stores in 70 countries, extending AI’s reach.
- Around 30,000 associates use AI tools that cut routine work and free up time for service.
13. Sephora
Sephora needed to bring the expertise of its in-store consultants into the digital journey. Many shoppers hesitated online when choosing shades and finishes, which reduced confidence and lowered conversions.
The company launched Virtual Artist with ModiFace to enable realistic try-ons across thousands of products, tuned for lighting and skin tone. It promoted the feature in key markets and complemented it with targeted in-app education to raise adoption.
The measurable improvements were significant:
- Between 2016 and 2018, Virtual Artist saw 200 million try-ons across 8.5 million visits.
- In Southeast Asia, a Braze campaign boosted AR adoption by 28%, usage per user by 16%, and overall traffic by 48%.
- In August 2017, the Swatch Me update enabled swatching for 300+ palettes on the forearm, speeding evaluation and purchase.
14. Swarovski
Swarovski sought to modernize its fragmented customer and marketing operations by unifying data and deploying AI across its customer journey. Legacy systems had slowed campaign execution and personalized engagement.
The company implemented Google Cloud technologies and BigQuery to consolidate data and power generative AI for customer service and marketing. AI now triages support tickets in real time, assists agents with smart responses, personalizes email campaigns, enables localization, and accelerates workflows through an internal AI platform called “Genie.”
The measurable impact includes:
- AI‑personalized email campaigns achieved 17% higher open rates and 7% higher click‑through rates.
- Campaign localization became 10x faster via AI‑assisted translation and asset adaptation.
- AI now helps support teams triage tickets and assist agents during live interactions.
15. Caleres
Caleres needed to overhaul its online discovery experience across more than 600,000 products spanning 13 branded websites. Relying on manual search rules limited agility and personalization.
To address this, Caleres deployed Coveo’s AI-powered search and experience platform. Coveo replaced manual indexing with machine learning that refines search, navigation, and recommendations in real time.
The results were strong and data‑driven:
- Search-driven conversion rate increased by 25%.
- Faceted navigation engagement reached between 30% and 50% of visitors.
- Shoppers who used facets converted at twice the rate of those who did not.
How do chatbots for customer service work?
Customer service chatbots operate through a sophisticated, three-step process designed to provide instant and relevant support.
1. They understand the customer’s intent
A chatbot’s first job is to figure out what you are asking for. It does this using a technology called Natural Language Processing (NLP), which helps it understand human language. The chatbot analyzes your words to identify your main goal, known as your “intent.”
For example, if you type, “I want to send back the shoes I bought,” the chatbot recognizes the intent is to “initiate a return,” not just to have a general chat about shoes. This allows it to trigger the correct process.
2. They find the right information
Once your intent is clear, the chatbot searches for the best answer. For general questions, it pulls information from a “knowledge base,” which is like a digital library of company policies and product details.
For personal requests, it securely connects to other business systems. For instance, after identifying your intent to “initiate a return,” the chatbot can access the order management system to ask, “Are you referring to order #12345?” before guiding you through the next steps.
3. They manage the conversation flow
Chatbots are programmed to handle conversations that require multiple steps. Using what’s called dialogue management, they can ask follow-up questions to gather necessary information.
For example, a chatbot processing a return might ask for the reason you are sending the item back. If you select “damaged item,” a predefined rule might trigger a seamless handoff to a human agent, who will receive the full conversation history so you don’t have to explain everything all over again.
How can your business adopt AI-generated support?
Step 1: Audit your current support process.
Start by analyzing your support tickets from the last 30 days to pinpoint your biggest bottlenecks. Identify the top three to five most frequently asked questions and measure how long it takes your team to resolve them. This data provides a clear baseline and shows you exactly where AI can make an immediate impact.
Step 2: Identify the best inquiries for AI.
Create a list of high-volume, low-complexity tasks that are ideal for automation. Good candidates include processing order status lookups, answering return policy questions, and guiding users to specific pages on your site. These are the repetitive tasks that an AI chatbot can handle instantly, freeing up your team.
Step 3: Choose a tool that fits your workflow.
Select an AI platform that integrates directly with your existing systems, like your e-commerce site or helpdesk. For Shopify stores, a tool like Chatty is a strong choice, while platforms such as Gorgias or Zendesk offer broader capabilities. The key is to ensure data flows seamlessly between your AI and your other business tools.
Step 4: Launch a hybrid AI and human workflow.
Begin by setting up your chatbot to answer the top five questions you identified in your audit. Design a clear escalation path that automatically transfers a conversation to a human agent if the AI cannot resolve the issue. This hybrid approach ensures customers always get the help they need without frustration.
Step 5: Track your key performance metrics.
Once live, monitor your progress by tracking KPIs like First Response Time, Resolution Time, and Customer Satisfaction (CSAT) scores. Review these metrics weekly to identify areas for improvement. Use this data to refine your chatbot’s answers and adjust your escalation rules, ensuring a continuous cycle of optimization.
FAQs
What’s the difference between AI support and human support?
AI support excels at instantly handling high volumes of simple, repetitive questions with 24/7 availability. Human agents, however, are essential for resolving complex or emotionally charged issues that require empathy and creative problem-solving.
Can AI support work in multiple languages?
Yes, modern AI support platforms are multilingual, supporting 100+ languages. They detect a customer’s language automatically and reply without extra setup. This lets businesses offer fast, consistent support worldwide in the customer’s native language.
How affordable is AI support for small businesses?
Many platforms offer free plans or subscriptions starting at about $50 per month. These tools cut manual work, save time, and let even small companies use AI without big upfront costs.
Will AI completely replace customer service agents?
No, AI will not replace human agents in customer service. It handles routine tasks so people can focus on complex and sensitive issues. The future is a hybrid model where AI supports agents, making them more effective.
H2. Final thought
To sum up, from Temple & Webster slashing support costs by 50% to Amazon projecting a $700 million profit boost from its AI assistant, the financial impact is impossible to ignore. These results demonstrate that AI has moved beyond answering simple FAQs to become a strategic tool for scaling operations and creating new revenue streams.
As we’ve seen, failing to leverage this technology means you’re not just missing out on efficiency gains. You’re actively missing revenue opportunities for more forward-thinking competitors to claim.