AI for Customer Support: Case Studies and Results for SMEs

When it comes to resolving customer issues, what’s more important: speed or accuracy? In truth, both are equally crucial. Customer service directly impacts revenue, reputation, and customer loyalty. Yet many CS teams struggle with overwhelming inquiry volumes, fragmented systems, and limited staffing. These challenges slow down response times and reduce service quality.  The core issue is inefficient access to information. Agents spend valuable time switching between tools to find what they need. In high-turnover environments, the lack of experienced staff adds even more friction. Why is AI essential for customer service teams, and how can it help? Enterprise AI Search gives agents instant access to accurate, cross-system data. It improves speed, enhances service quality, and enables teams to focus on solving meaningful problems. In this post, we explore how AI is reshaping customer service through real enterprise use cases. Enterprise Case: How AI for Customer Support Boosted Performance 📌 Vodafone: Improving Support Efficiency with AI Search Challenge:Vodafone’s customer service center faced a major bottleneck due to fragmented data across multiple systems. By leveraging AI for customer service, Vodafone aimed to streamline data access and improve overall support efficiency. Customer contract information, pricing plans, and technical support data were all stored separately, making it difficult and time-consuming for agents to retrieve what they needed. This led to longer wait times and reduced operational efficiency. AI Adoption: Vodafone addressed this by implementing IBM Watson Assistant to develop and integrate the chatbot into its agent support system. AI analyzes customer queries in real time and delivers relevant information, such as contract details and pricing options. Directly to agents. By performing unified searches across multiple databases, AI presents the necessary data through a single interface, eliminating the need to switch between systems. Key Result of AI adoption: Customer satisfaction increased by 50% with GenAI-enhanced AI interactions Net Promoter Score (NPS) improved by 20% compared to the previous AI system First-time resolution rate increased from 15% to 60% Shorter customer wait times and improved service quality AI enabled agents to access information quickly and efficiently, reducing call durations and freeing them to focus on tasks that required human judgment and problem-solving. Rather than replacing agents, AI functioned as a real-time assistant that enhanced productivity and decision-making.(Vodafone to boost TOBi with GenAI, The Mobile Network 2024) 📌Lufthansa: Reducing Wait Times through AI Search Challenge: Lufthansa’s customer service center, Lufthansa InTouch, faced mounting operational pressure due to high volumes of inquiries related to booking changes, refund requests, and flight details. Agents had to manually retrieve data from various systems—including reservation records, account details, and flight schedules—leading to delays and inefficiencies. AI Adoption: Lufthansa adopted the AI platform to implement a conversational AI system, which was integrated into the agent support workflow. This AI solution analyzed customer queries in real time and retrieved relevant data, such as booking information, flight schedules, and refund policies, from multiple backend systems (e.g., CRM, reservation platforms). It also suggested optimal next steps, like proposing alternative flights or guiding refund procedures, to support faster decision-making. Key Result of AI Adoption:  Reduced customer wait times and improved service quality. AI agents support 16 million customer interactions per year, with peaks up to 375,000 per day. The AI streamlined access to complex, distributed data, helping agents resolve inquiries faster and more accurately. By automating repetitive lookups and offering context-aware guidance, it allowed agents to focus on more nuanced customer interactions such as explaining refund policies or negotiating travel changes. The implementation also led to a significantly lower Average Handling Time (AHT), allowing agents to resolve inquiries more efficiently. It served as a reliable productivity partner rather than a replacement.(16+ Million AI Conversations Yearly With Self-Service AI Agent) How AI Boosted CS Team Performance in SMEs 📌 Avetta: Shortening Call Handling Time with AI Search Challenge: Avetta’s customer service team faced operational inefficiencies caused by data silos across multiple systems. Information related to supply chain contractors, compliance documentation, and past customer inquiries was scattered, making it time-consuming for agents to locate and process the data. This led to longer customer wait times and prevented agents from focusing on complex support requests. AI Adoption:To address these issues, Avetta integrated a conversational AI solution to support its customer service team. The AI analyzed customer inquiries and retrieved relevant information—such as contractor certification status, compliance documents, and historical tickets—in real time, displaying it through a unified interface. AI-generated videos were also used to streamline agent onboarding and training processes, helping reduce data handling time. Additionally, the AI provided real-time guidance to agents, offering suggestions such as how to navigate compliance procedures. Key Result of AI adoption: Reduced average call handling time by 16 seconds Increased onboarding efficiency by 20% Increased agent retention by 8%   AI eliminated the need for agents to manually navigate fragmented data systems, allowing them to access key information quickly and efficiently. This significantly shortened handling times and enabled agents to devote more attention to complex issues, such as contractor compliance resolution. AI-powered video training further enhanced agents’ ability to process data effectively. By automating repetitive tasks, the AI served as a productivity partner that elevated both decision-making and customer engagement quality. (Synthesia Case Study – How Avetta is using AI video to boost the productivity of 150 support agents 2024) 📌AI Is Vital for SMEs and CS Outsourcers Too We’ve seen how companies of all sizes are using AI to transform their customer service operations. But in many ways, AI can make an even bigger impact for smaller teams, where every second counts and resources are limited. For SMEs and outsourcing providers, faster response times and higher accuracy are not just goals. They are essential to business success. Want practical insights into how SME support teams use AI? 🔗See how customer support teams use AI in practice 🔗Explore ready-to-use 12 AI prompts for support teams Key Considerations for AI Adoption in SMEs AI has proven to be a powerful enabler for SMEs and customer service outsourcing teams, driving both efficiency and quality in customer