AI Large Models Reshape the Customer Service Industry: New Opportunities for Intelligent Customer Service Startups
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Since ChatGPT went viral and broke into the mainstream, a wave of AI large models has swept across the globe. More and more companies are entering the field of AI large models, with many launching their own products to lead this AI wave. Among these, both general-purpose and vertical large models have emerged.
After the birth of AI large model products, their practical implementation and commercial applications have become a key focus. Meanwhile, companies releasing AI large models have accelerated their exploration of deployment and applications. The intelligent customer service sector is widely regarded as one of the best fields for AI large model implementation.
Can AI Large Models Transform Intelligent Customer Service?
Most people are familiar with intelligent customer service. As user habits increasingly shift online, the demand for customer service has grown significantly. With this surge, intelligent customer service has emerged to assist human agents, providing users and consumers with better and more efficient service. Today, the intelligent customer service industry has developed rapidly and is thriving. So, why is intelligent customer service considered one of the best fields for AI large model implementation?
First, AI large models excel in content generation and semantic understanding, making them highly compatible with the intelligent customer service industry. Observing various AI large model products, most possess capabilities like text generation, language understanding, knowledge-based Q&A, and logical reasoning. Combining deep learning and natural language processing technologies, AI large models exhibit exceptional language understanding and content generation abilities.
Intelligent customer service leverages AI technologies like speech recognition and natural language processing to identify customer needs and provide targeted answers, resolving queries while enhancing service efficiency. The language understanding and content generation capabilities of AI large models align perfectly with the needs of intelligent customer service, making implementation more feasible.
Second, AI large models can significantly improve the intelligence level of customer service. While intelligent customer service has alleviated some of the pressure on traditional human agents and improved efficiency, complaints about its limitations—such as being "not smart enough" or "unable to understand human language"—are common.
With the advent of the digital era, vast amounts of data are being generated. AI large models, trained on massive datasets, continuously improve their language understanding and ability to process complex information. Intelligent customer service enhanced by AI large models can more accurately understand context and user intent, delivering more reliable service.
China Mobile's Rapid Advance
The popularity of AI large models remains high, with more participants joining the race, including telecom operators. For instance, China Unicom launched the "Honghu" text-image large model 1.0, China Telecom released the TeleChat large language model, and China Mobile introduced the "Jiutian" AI industry large models, including the Jiutian·Haizheng Government Model and the Jiutian·Customer Service Model. China Mobile's early focus on customer service models is driven by multiple factors.
On one hand, China Mobile has years of experience in customer service, with deep expertise and business knowledge, laying a solid foundation for its customer service model. As one of the top three telecom operators, China Mobile has long maintained close ties with users. When users encounter issues with mobile services, wireless internet, or broadband, they often turn to customer service for help. Over the years, China Mobile has accumulated vast data resources, rich service experience, and professional knowledge, all of which are invaluable for developing its customer service model.
It is reported that the Jiutian·Customer Service Model can not only parse user queries in natural language and provide answers but also collaborate with human agents by analyzing historical conversations to summarize key points and offer response suggestions.
On the other hand, China Mobile's customer service model enhances its own operations, improving efficiency and quality. As mentioned earlier, China Mobile's deep connection with users and its accumulated data and experience serve as critical training materials for its model. Similarly, the model can be applied to its own business, boosting service efficiency, quality, and user experience.
For example, when faced with complex user queries, the Jiutian·Customer Service Model can collaborate with human agents, analyzing historical conversations to summarize key points and provide response suggestions, reducing response times and improving both agent efficiency and user satisfaction.
Ronglian Cloud's Swift Progress
Not long ago, Ronglian Cloud officially released its vertical industry-specific large language model, the 'Chitu Model,' designed for enterprise applications. According to reports, the Chitu Model focuses on four core capabilities: understanding communication, performing analysis, possessing knowledge, and executing tasks, providing robust support for scenarios like intelligent customer service and marketing.
First, Ronglian Cloud has long been exploring AIGC, which laid a solid foundation for its AI large model product. It is reported that Ronglian Cloud's AI team began researching key AIGC technologies early on, achieving breakthroughs in dialogue response generation, automatic question generation, and SQL statement generation. Their core technologies have secured top rankings in authoritative competitions and evaluations. These technological advancements have become a key driver behind Ronglian Cloud's large language model product.
Second, Ronglian Cloud has deep expertise in the intelligent customer service domain, enabling it to develop AI large model products with superior customer service capabilities. For years, Ronglian Cloud has been deeply involved in the intelligent customer service field, launching products like intelligent customer service robots. This extensive experience has given the company profound insights into the industry. Leveraging its accumulated vertical industry resources and deep understanding of intelligent customer service, Ronglian Cloud has successfully developed its current large model product.
For example, while current intelligent customer service systems can accurately recognize and respond to common sentence structures, they often struggle with complex queries, leading to misunderstandings or irrelevant answers. The Chitu Model, however, excels in 'performing analysis,' enabling higher-dimensional reasoning and problem-solving in customer service and marketing scenarios.
Third, Ronglian Cloud's industry-specific large model facilitates easier commercialization. Although AI large models have vast potential, they are also highly resource-intensive, making commercialization a critical focus. Ronglian Cloud's industry-specific model combines its understanding of industry needs with its existing strengths, making commercialization more feasible. For instance, enterprises can use the Chitu Model to build their own intelligent customer service and digital marketing systems, transitioning from 'cost reduction and efficiency improvement' to 'value creation.'
Challenges Remain
Currently, AI large models and intelligent customer service are highly compatible, making the latter a natural application scenario. However, despite the high value of AI large models in this field, challenges persist.
First, the quality of content generated by AI large models is inconsistent, often failing to meet user expectations. Unlike other industries, customer service demands accurate and reliable answers. A major criticism of current intelligent customer service systems is their inability to understand user intent or provide relevant responses.
Due to limitations in training data quantity and quality, AI large models sometimes produce unstable or incorrect results. If the model generates wrong answers, it not only fails to achieve the goal of cost reduction and efficiency improvement but may also degrade the customer experience.
Second, the customer service industry covers a wide range of sectors with rapidly evolving information, posing higher demands on AI large models. Intelligent customer service is used across industries like e-commerce, hospitality, and banking, each with unique characteristics and terminologies. The fast-paced updates in these sectors mean AI large models require extensive training data to maintain accuracy. However, more data also means higher training costs.
In summary, the application of AI large models is already underway, with more companies exploring suitable scenarios. Intelligent customer service, given its inherent value and compatibility with AI large models, has become a key area for implementation. Although challenges remain, these obstacles will likely be overcome, paving the way for truly intelligent and personalized customer service experiences.