Customer service departments across industries are facing increased call volumes, high customer service agent turnover, talent shortages and shifting customer expectations. (View Highlight)
Customers expect both self-help options and real-time, person-to-person support. These expectations for seamless, personalized experiences extend across digital communication channels, including live chat, text and social media. (View Highlight)
Despite the rise of digital channels, many consumers still prefer picking up the phone for support, placing strain on call centers. As companies strive to enhance the quality of customer interactions, operational efficiency and costs remain a significant concern. (View Highlight)
To address these challenges, businesses are deploying AI-powered customer service software to boost agent productivity, automate customer interactions and harvest insights to optimize operations. (View Highlight)
In nearly every industry, AI systems can help improve service delivery and customer satisfaction. Retailers are using conversational AI to help manage omnichannel customer requests, telecommunications providers are enhancing network troubleshooting, financial institutions are automating routine banking tasks, and healthcare facilities are expanding their capacity for patient care. (View Highlight)
With strategic deployment of AI, enterprises can transform customer interactions through intuitive problem-solving to build greater operational efficiencies and elevate customer satisfaction. (View Highlight)
By harnessing customer data from support interactions, documented FAQs and other enterprise resources, businesses can develop AI tools that tap into their organization’s unique collective knowledge and experiences to deliver personalized service, product recommendations and proactive support. (View Highlight)
Customizable, open-source generative AI technologies such as large language models (LLMs), combined with natural language processing (NLP) and retrieval-augmented generation (RAG), are helping industries accelerate the rollout of use-case-specific customer service AI. According to McKinsey, over 80% of customer care executives are already investing in AI or planning to do so soon. (View Highlight)
For satisfactory, real-time interactions, AI-powered customer service software must return accurate, fast and relevant responses. Some tricks of the trade include: (View Highlight)
RAG frameworks connect foundation or general-purpose LLMs to proprietary knowledge bases and data sources, including inventory management and customer relationship management systems and customer service protocols. Integrating RAG into conversational chatbots, AI assistants and copilots tailors responses to the context of customer queries. (View Highlight)
Human-in-the-loop processes remain crucial to both AI training and live deployments. After initial training of foundation models or LLMs, human reviewers should judge the AI’s responses and provide corrective feedback. This helps to guard against issues such as hallucination — where the model generates false or misleading information, and other errors including toxicity or off-topic responses. This type of human involvement ensures fairness, accuracy and security is fully considered during AI development. (View Highlight)
The return on investment of customer service AI should be measured primarily based on efficiency gains and cost reductions. To quantify ROI, businesses can measure key indicators such as reduced response times, decreased operational costs of contact centers, improved customer satisfaction scores and revenue growth resulting from AI-enhanced services. (View Highlight)
For instance, the cost of implementing an AI chatbot using open-source models can be compared with the expenses incurred by routing customer inquiries through traditional call centers. Establishing this baseline helps assess the financial impact of AI deployments on customer service operations. (View Highlight)
To solidify understanding of ROI before scaling AI deployments, companies can consider a pilot period. For example, by redirecting 20% of call center traffic to AI solutions for one or two quarters and closely monitoring the outcomes, businesses can obtain concrete data on performance improvements and cost savings. This approach helps prove ROI and informs decisions for further investment. (View Highlight)
Modern shoppers expect smooth, personalized and efficient shopping experiences, whether in store or on an e-commerce site. Customers of all generations continue prioritizing live human support, while also desiring the option to use different channels. But complex customer issues coming from a diverse customer base can make it difficult for support agents to quickly comprehend and resolve incoming requests. (View Highlight)
CP All, Thailand’s sole licensed operator for 7-Eleven convenience stores, has implemented conversational AI chatbots in its call centers, which rack up more than 250,000 calls per day. Training the bots presented unique challenges due to the complexities of the Thai language, which includes 21 consonants, 18 pure vowels, three diphthongs and five tones. (View Highlight)
To manage this, CP All used NVIDIA NeMo, a framework designed for building, training and fine-tuning GPU-accelerated speech and natural language understanding models. With automatic speech recognition and NLP models powered by NVIDIA technologies, CP All’s chatbot achieved a 97% accuracy rate in understanding spoken Thai. (View Highlight)
With the conversational chatbot handling a significant number of customer conversations, the call load on human agents was reduced by 60%. This allowed customer service teams to focus on more complex tasks. The chatbot also helped reduce wait times and provided quicker, more accurate responses, leading to higher customer satisfaction levels. (View Highlight)
Infosys, a leader in next-generation digital services and consulting, has built AI-driven solutions to help its telco partners overcome customer service challenges. Using NVIDIA NIM microservices and RAG, Infosys developed an AI chatbot to support network troubleshooting. (View Highlight)
By offering quick access to essential, vendor-agnostic router commands for diagnostics and monitoring, the generative AI-powered chatbot significantly reduces network resolution times, enhancing overall customer support experiences. (View Highlight)
To ensure accuracy and contextual responses, Infosys trained the generative AI solution on telecom device-specific manuals, training documents and troubleshooting guides. Using NVIDIA NeMo Retriever to query enterprise data, Infosys achieved 90% accuracy for its LLM output. By fine-tuning and deploying models with NVIDIA technologies, Infosys achieved a latency of 0.9 seconds, a 61% reduction compared with its baseline model. The RAG-enabled chatbot powered by NeMo Retriever also attained 92% accuracy, compared with the baseline model’s 85%. (View Highlight)
While customers expect anytime, anywhere banking and support, financial services require a heightened level of data sensitivity. And unlike other industries that may include one-off purchases, banking is typically based on ongoing transactions and long-term customer relationships. (View Highlight)
At the same time, user loyalty can be fleeting, with up to 80% of banking customers willing to switch institutions for a better experience. Financial institutions must continuously improve their support experiences and update their analyses of customer needs and preferences. (View Highlight)
Many banks are turning to AI virtual assistants that can interact directly with customers to manage inquiries, execute transactions and escalate complex issues to human customer support agents. According to NVIDIA’s 2024 State of AI in Financial Services report, more than one-fourth of survey respondents are using AI to enhance customer experiences, and 34% are exploring the use of generative AI and LLMs for customer experience and engagement. (View Highlight)
Bunq, a European digital bank with more than 2 million customers and 8 billion euros worth of deposits, is deploying generative AI to meet user needs. With proprietary LLMs, the company built Finn, a personal AI assistant available to both customers and bank employees. Finn can answer finance-related inquiries such as “How much did I spend on groceries last month?” or “What is the name of the Indian restaurant I ate at last week?” (View Highlight)
Plus, with a human-in-the-loop process, Finn helps employees more quickly identify fraud. By collecting and analyzing data for compliance officers to review, bunq now identifies fraud in just three to seven minutes, down from 30 minutes without Finn. (View Highlight)
By deploying AI tools that can use data to protect customer transactions, execute banking requests and act on customer feedback, financial institutions can serve customers at a higher level, building the trust and satisfaction necessary for long-term relationships. (View Highlight)
