Real-Life Examples Of AI In Customer Service

From MIT SMR - how 14 companies across a wide range of industries are generating value from generative AI today: McKinsey built Lilli, a platform that helps consultants quickly find and synthesize information from past projects worldwide. The system integrates with over 40 internal sources and even reads PowerPoint slides, leading to 30% time savings and 75% employee adoption within a year. Amazon deploys AI across multiple divisions. Their pharmacy division uses an internal chatbot to help customer service representatives find answers faster. The finance team employs AI for everything from fraud detection to tax work. In their e-commerce business, they personalize product recommendations based on customer preferences and are developing new GenAI tools for vendors. Morgan Stanley empowers their financial advisers with a knowledge assistant trained on over a million internal documents. The system can summarize client video meetings and draft personalized follow-up emails, allowing advisers to focus more on client needs. Sysco, the food distribution giant, uses GenAI to generate menu recommendations for online customers and create personalized scripts for sales calls based on customer data. CarMax revolutionized their car research pages with GenAI, automatically generating content and summarizing thousands of customer reviews. They've since expanded to use AI in marketing design, customer chatbots, and internal tools. Dentsu transformed their creative agency work with GenAI, using it throughout the creative process from proposals to project planning. They can now generate mock-ups and product photos in real-time during client meetings, significantly improving efficiency. John Hancock deployed chatbot assistants to handle routine customer queries, reducing wait times and freeing human agents for complex issues. Major retailers like Starbucks, Domino's, and CVS are implementing GenAI voice interactions for customer service, moving beyond traditional phone menus. Tapestry, parent company of Coach and Kate Spade, uses real-time language modifications to personalize online shopping, mimicking in-store associate interactions. This led to a 3% increase in e-commerce revenue. Software companies are integrating GenAI directly into their products. Lucidchart allows users to create flowcharts through natural language commands. Canva integrated ChatGPT to simplify creation of visual content. Adobe embedded GenAI across their suite for image editing, PDF interaction, and marketing campaign optimization. For more information on these examples and to gain insight into how companies are transforming with GenAI, read the full article here: https://lnkd.in/eWSzaKw4 images: 4 of the 20 I created with Midjourney for this post. #AI #transformation #innovation

🧠 Is Generative AI Just Cool, or Does It Really Have an Impact? That's the big debate in tech circles these days. A study led by researchers from Stanford University, MIT, and the National Bureau of Economic Research (NBER) sheds light on this question by examining the real-world impact of deploying generative AI in a customer support environment. Their analysis offers empirical evidence on how AI tools, specifically those based on OpenAI's GPT models, are transforming customer service operations at a Fortune 500 software company. The researchers employed a mix of methodologies: a randomized control trial (RCT) and a staggered rollout, encompassing around 5,000 agents over several months. By analyzing 3 million customer-agent interactions, the study assessed metrics such as resolutions per hour, handle time, resolution rates, and customer satisfaction (Net Promoter Score). To understand the AI's impact over time, dynamic difference-in-differences regression models were used. Here is what they found: 1. 𝐒𝐢𝐠𝐧𝐢𝐟𝐢𝐜𝐚𝐧𝐭 𝐁𝐨𝐨𝐬𝐭 𝐢𝐧 𝐏𝐫𝐨𝐝𝐮𝐜𝐭𝐢𝐯𝐢𝐭𝐲: The AI tool led to a 13.8% increase in the number of customer queries resolved per hour, particularly benefiting less experienced agents. 2. 𝐍𝐚𝐫𝐫𝐨𝐰𝐢𝐧𝐠 𝐭𝐡𝐞 𝐏𝐞𝐫𝐟𝐨𝐫𝐦𝐚𝐧𝐜𝐞 𝐆𝐚𝐩: AI tools accelerated the learning curve for newer agents, allowing them to reach the performance levels of seasoned employees more quickly. 3. 𝐈𝐦𝐩𝐫𝐨𝐯𝐞𝐝 𝐂𝐮𝐬𝐭𝐨𝐦𝐞𝐫 𝐒𝐚𝐭𝐢𝐬𝐟𝐚𝐜𝐭𝐢𝐨𝐧: The AI deployment resulted in higher customer satisfaction scores (as shown by improved Net Promoter Scores) while maintaining stable employee sentiment. 4. 𝐋𝐨𝐰𝐞𝐫 𝐀𝐭𝐭𝐫𝐢𝐭𝐢𝐨𝐧 𝐑𝐚𝐭𝐞𝐬: Interestingly, the AI support led to reduced attrition rates, especially among new hires with less than six months of experience. 5. 𝐎𝐩𝐭𝐢𝐦𝐢𝐳𝐞𝐝 𝐖𝐨𝐫𝐤𝐟𝐥𝐨𝐰𝐬: The AI system reduced the need for escalations to managers, improving vertical efficiency. However, its impact on horizontal workflows, like transfers between agents, showed mixed results, suggesting more refinement is needed in AI integration. 6. 𝐂𝐮𝐬𝐭𝐨𝐦𝐢𝐳𝐞𝐝 𝐀𝐈 𝐌𝐚𝐭𝐭𝐞𝐫𝐬: The software wasn’t off-the-shelf; it was a custom-built solution tailored to the company’s needs using the GPT family of language models. This emphasizes the importance of context-specific AI applications for effective outcomes. For leaders, managers, and AI practitioners, these insights are invaluable—highlighting not just the potential of AI, but also the nuanced ways it reshapes workflows, impacts employee dynamics, and transforms customer experiences.So, does generative AI really make a difference? According to this study, the answer is a resounding yes—but it depends on how thoughtfully it is deployed. Link 🔗 to the paper: https://lnkd.in/ejhUfufz

Came back from vacation Monday. Inbox? On fire.🔥 Buried in the chaos: a customer story that stopped me in my tracks (and made me so happy). A Customer Support leader at a fast-growing financial services company used AI to transform his team - in just a few weeks. This leader works for a financial services company that’s in high-growth mode. Great news, right? Yes! For everyone except his Customer Support team… As the business grew faster, they were bombarded with repetitive questions about simple things like loan statuses and document requirements. Reps were overwhelmed. Customers faced longer response times. The company has been a HubSpot customer for nearly 10 years. They turned to Customer Agent, HubSpot’s AI Agent, and got to work: - Connected it to their knowledge base → accurate, fast answers - Set smart handoff rules → AI handles the simple, reps handle the complex - Customized the tone → sounds like them, not a generic bot (you know the type) In a short space of time, things changed dramatically: - Customer Agent now resolves more tickets than any rep - 94.9% of customers report being happy with the experience - For the first time, the team can prioritize complex issues and provide proactive support to high-value customers It’s exciting to see leaders using Customer Agent to not just respond to more tickets, but to increase CSAT and empower their teams to drive more impact. 2025 is the year of AI transformed Customer Support. I am stunned by how quickly that transformation is playing out!

What if someone who doesn't speak your language handled your next customer service call—but you never noticed? Alorica, a customer service company based in California, has introduced an AI-driven translation tool that allows their representatives to communicate with customers in over 200 languages and 75 dialects. This innovation means that a rep who only speaks Spanish could assist a customer in Hong Kong speaking Cantonese. As AI continues to develop, its impact on jobs is a hot topic of debate. While some fear widespread job losses, companies like Alorica are showing that AI can enhance productivity without necessarily cutting jobs. This raises important questions about the future of work in an AI-powered world. 🌍 AI Translation: Alorica's new AI tool allows reps to communicate with customers in over 200 languages, making global service easier. 🛠️ Efficiency Gains: AI is helping companies like Alorica improve call handling times and customer satisfaction instead of reducing jobs. 📈 Job Evolution: AI isn't just about replacing jobs—it's also about transforming them, with new roles emerging as technology advances. 🤝 Human-AI Collaboration: AI tools are proving valuable assistants, especially for newer employees, boosting their productivity. 🔄 Workforce Dynamics: The rise of AI is prompting a shift in job skills and roles, but fears of mass unemployment have yet to materialize. #AI #CustomerService #LanguageTranslation #FutureOfWork #TechInnovation #JobEvolution #AIandJobs 

Can AI Grow Your KPI? (super short answer: Yes!) I am often asked how exactly Gen AI can improve productivity. And which tools are ideal to start implementing first. Departments set Specific Key Performance Indicators (KPIs) To be in line with their company’s objectives and goals. The easiest tools are often data AI tools: - The data team is not customer-facing. - Productivity is easier to quantify in code. - Coding related KPIs can grow quickly with AI tools. However, the biggest ROI on AI tool investments Is seen in Customer Service enhancing tools: - Customer Support Agents who use AI tools work faster. - Multiple academic studies find quantitative support. Sometime ago, I worked with a client to reduce waiting times For their customers by providing faster service. I created this example to demonstrate how the KPI for Customer service can improve with AI tools. ----- Example of IMAGINARY Company, Inc. Employee type: Customer Service Representatives (CSR) Company Objective: Helping more customers without compromising quality. KPIs: 1. Average Service Time (in minutes) = AST 2. First Call Resolution 3. Customer Satisfaction Score Focusing ONLY on AST right now: --> 10 CSRs given access to AI virtual assistants. --> AI offered real-time information. --> AI suggests responses during customer calls to CSR. --> 4 week testing period. --> Before AI and After AI service time per call measured. Results: * AST before AI = 8.6 minutes per call. * AST after AI = 6.4 minutes per call. * Mean Difference = 2.2 minutes less per call. * Paired Differences t-test score = 4.71. * P-value = 0.001 implies significant change. * Total customers served per hour before AI = 70. * Total customers served per hour after AI = 94. ______________________________________________ Results indicate that 26% of time was saved, 35% more customers were served each hour by the CSRs, After a robust implementation of AI Tools to assist them. _______________________________________________ Actionable Insights: 1. Other KPIs also need to be tracked. 2. AI training and ongoing support are essential. 3. Call volume and other variables need to be included. 4. Adopting relevant AI Tools can improve productivity. 5. Track CSR performance to identify bottlenecks. Follow Dr. Kruti Lehenbauer & Analytics TX, LLC on LinkedIn #PostItStatistics #DataScience #AI insights. ------------- P.S.: What is your experience with an AI tool implementation?

Learnings from transforming CX with Gen AI for a Financial Services giant in APAC 🚀 One of the largest Financial Services players in the APAC recently leveraged Verloop to transform its contact center. The outcomes? Transformational change in customer support experience which not only drove CSAT up but also helped them bring efficiency into their CX Ops. Here is a snapshot of outcomes and learnings Outcomes -------------- 1. About 30% increase in Customer Satisfaction score 2. 43% fewer tickets assigned to their support desk 3. 70% Reduction in Average Response Time 4. 30% Cost Savings by CX efficiency Learnings -------------- 1. Effort - Easier said than done; most models are great for building demos but a nightmare when implementing large complex scenarios 2. Focus - Niche-trained LLMs work better than a large model 3. Latency - Latency in response especially in audio calls is a deal breaker. 4. RAG + LLM - Balancing when to refer to RAG vs when should LLM handle the task takes a while 5. Cost - Models cost significant amount of money to run; attach and focus on business outcomes 6. Data Quality - Investing time in data cleansing and organization pays off massively 7. AI + Human - AI handles the repetitive tasks, while AI-assisted human agents are required for empathy and complex problem-solving 8. Keep Building - Continuous improvements and training of flows is critical more so in the first few months of launch Implementing Guardrails --------------------------- 1. Focus on Ethical AI usage with strict guidelines to ensure AI operates within ethical boundaries, maintaining transparency and customer trust. 2. Adhere to rigorous data privacy regulations to protect customer information. Protecto works like a charm! 3. A key trait of any such implementation is AI knowing when to hand over Launch Experience -------------------- 1. Collaborative Approach - Everyone is learning in this journey; engage early and frequently with all stakeholders 2. Stay Agile - Launch iteratively and keep improving instead of one big bang launch 3. Human training - Focus on training all stakeholders; things are different vs structured data We started Verloop with the idea that the future of contact centers is AI-first, human-assisted. These engagements help us stay on the course and keep building towards our vision. We are already living in the future and it is slowly spreading everywhere! 🌟 #contactcenter #GenAI #CXTransformation #transformation Verloop.io CA. Ankit Sarawagi Melisa Vaz Nikhil Gupta Urvashi Singh Kiran Prabhu Ravi Petlur Kumar Gaurav