Let me first of all introduce myself. My name is Dmitry Kindria and I'm a chief operations officer at company called Tu Delivery. Tu is one of the largest delivery services in Saudi Arabia with millions of monthly served customers and hundreds of thousands monthly processed customer complaints. Today, I'd like to talk about how large language models help us serve our customers and process their complaints faster. With a better user experience serving as the main line of communications between business and their clients, customer service has no longer been considered as an essential component of corporate operations. Large language models, or llms, are one of the newest technologies making waves in customer support systems. Over time, technology advancements have completely changed the customer service spam. These llms, such as the OpenAI develop GPT four, have shown promise in revolutionizing customer interactions with their superior reasoning and natural language processing skills. Large language models are AI driven systems capable of generating human like text. In the realm of customer support, they have emerged as powerful tools reshaping traditional methods. LLM models have significantly transformed customer support operations. They enhance efficiency by swiftly addressing queries and improving accessibility through round the clock availability. Moreover, their scalability enables handling large volumes of inquiries seamlessly. The role of LLM models in customer support has sparked debate. While proponents argue they revolutionize support by offering instant and accurate responses, detractors express concerns about their potential to undermine traditional methods, fearing the loss of human touch and empathy in interactions. LLM models, exemplified by chat, GPT, and Lama, are cutting edge AI systems designed to mimic human like interactions. They possess the capability to understand and generate text with remarkable fluency, revolutionizing the way we interact with machines. These models excel at generating responses that closely resemble those of humans, providing a natural and seamless experience for users. By analyzing vast amounts of text data, LLM models have acquired the ability to comprehend context, anticipate user needs, and craft tailored responses that simulate authentic human conversation. Traditional customer support methods rely heavily on human involvement and personalized interactions. These methods typically involve direct communication channels such as phone calls, emails, and face to face interactions where trained representatives address customer queries and concerns in real time. Human agents play a pivotal role in traditional customer support, offering empathy, understanding, and personalized solutions tailored to each customer's unique needs. This personal touch fosters trust and strengthens the relationship between the customer and the brand, leading to enhanced satisfaction and loyalty over time. LLM models are fundamentally transforming the landscape of customer support by introducing unparalleled efficiency and accessibility. Their ability to generate human like responses at at scale revolutionizes the way businesses interact with their customers. LLM models excel in automating responses and handling a wide range of customer inquiries swiftly and accurately. By leveraging advanced natural language processing algorithms, they can understand the context of queries and provide relevant and helpful responses in real time. This automation not only reduces responses times, but also frees up human agents to focus on more complex issues, ultimately enhancing overall efficiency of customer satisfaction. We can see that large language models are transforming customer service operations in several impactful ways. First of all, it's instantaneous responses. Llms can analyze and generate responses to customer inquiries in real time. This significantly reduces waiting times for customers and enhances the overall customer experience, consistency and accuracy. LLMs provide consistent and accurate responses, ensuring that customer receives correct information every time, regardless of the time of the day or agent availability. Third, handling multiple channels LLMs can be integrated across various customer communication channels, including websites, chatbots, social media, and more. This omnichannel approach ensures seamless interaction regardless of the platform. 424 seven availability automated LLM driven systems can operate around the clock, providing assistance to customers in different time zones without the need to shift or breaks cost efficiency. By automating routine customer interactions, companies can significantly reduce operational costs allocated within hiring, training, and maintaining a large customer service team. And the last one, personalization. At scale, llms can analyze customer data and interactions to offer personalized responses, recommendations, and solutions, creating a tailored experience for each customer. While LLM models offer numerous benefits, it's essential to acknowledge their limitations and potential drawbacks in the realm of customer support. First of all, it's a potential loss of personal touch. LLM models lack the human touch and empathy that human agents bring to customer interactions. Customers may feel disconnected or unsatisfied with responses generated by machines, especially in sensitive or emotionally charged situations. Misinterruptions queries LLM models may struggle to accurately interpret nuanced or ambiguous queries, leading to irrelevant or incorrect responses. Without human intervention, there is a risk of misunderstanding customer needs and providing inadequate support. Number three is ethical concerns. There are ethical considerations surrounding the use of LLM models in customer support, particularly regarding data privacy and algorithm bias. Issues such as data security breaches or biased responses could undermine customer trust and tarnish the reputation of the organization. And the last one, dependency on technology. Over reliance on LLM models may lead to decreased problem solving skills among human agents and hinder their ability to handle complex issues independently. Acknowledging these drawbacks is crucial for implementing LLM models responsibly and effectively in customer support operations. Balancing the advantages with the limitations ensures a more holistic approach to delivering exceptional customer service. Okay, now let me introduce you the hybrid approach hybrid approaches represent a fusion of the capabilities of LLM models with the indispensable human touch in customer support. These strategies aim to harness the strengths of both automation and human interaction to deliver exceptional customer experience. By integrating LLM models with human intervention, businesses can capitalize on the efficiency and scalability of automation while preserving the empathy and problem solving skills inherent in human interaction. This hybrid model enables the seamless handling of routine inquiries by LLM models, freeing up human agents to address complex issues and provide personalized support where it matters most. The result is a harmonious blend of technology and human expertise that optimizes customer satisfaction and enhances brand loyalty. The future of customer support is poised for future evolution, with LLM models playing a pivotal role in reshaping the landscape. As technology continues to advance, these models will become increasingly sophisticated, offering even more seamless and intelligent interaction with customers. As for predictions, anticipate significant advancements in LLM models, enabling them to handle an even broader range of inquiries with procedures and nuance. Moreover, we foresee deeper integration of LLM models into support systems, leveraging data analytics and machine learning to enhance predictive capabilities and personalize customer interactions. This congregants of technology and customer service will redefine the standards of excellence in support, driving unprecedented levels of efficiency, satisfaction, and loyalty. Customer support practices are constantly evolving in response to technological advancements, changing customer expectations, and market dynamics. Businesses must adapt and evolve their support strategies to stay relevant and competitive in today's dynamic landscape. Continuous improvements and innovations are imperative in customer support. Businesses must constantly assess and refine their processes, leveraging emerging technologies such as LLM models, automation, and data analytics to enhance efficiency, accuracy, and customer satisfaction. By embracing change and fostering a culture of innovation, organizations can stay ahead of the curve and deliver exceptional support experiences that drive loyalty and growth. Again, in the age of automation, maintaining customer trust is paramount for businesses as LLM models and other automated systems become integral for customer support. Ensuring transparency and reliability in interactions is crucial to preserving trust. To build and maintain trust in customer relations, we businesses must prioritize transparency and authenticity. This can be achieved by clearly communicating the use of automation and human intervention in customer support processes, providing accurate and helpful information even when utilizing automated systems, offering avenues for customers to escalate issues to human agents when necessary, ensuring personalized assistance when needed, and finally, regularly seeking feedback from customers and using it to improve support processes and enhance the overall experience. By implementing these strategies, businesses can foster trust and confidence in their customer support practices, leading to stronger customer relationship and loyalty. Now, let's look at the question how do stakeholders perceive the role of LLM models in customer support? Main points from customer perspective, there are increased expectations. Customers expect faster response times and personalized interactions. Then there is an acceptance of automation. Many customers appreciate the convenience of automated support but value human intervention for complex issues. And finally, importance of transparency. Customers desire transparency regarding the use of LLM models in their interactions. From support agent perspective, first of all, it's efficiency and workload, so support agents appreciate the efficiency of LLM models bring, but they may feel concerned about job security and increased workload. From the role of human touch, agents emphasize the importance of human empathy and understanding in customer interactions, especially in sensitive issues, issues and from the collaboration with automation perspective, agents recognize the potential of collaborating with LLM models to enhance support processes rather than being replaced by them. And from the industry expect perspective, experts acknowledge the transformative potential of LLM models in customer support and anticipate further advancements. They emphasize the need for businesses to adapt these technological changes to remain competitive in the evolving market landscape. At the same time, experts highlight the importance of addressing ethical concerns surrounding the use of AI in customer support, including data privacy and algorithmic okay. Some final thoughts as businesses navigate the evolving landscape of customer support, the integration of LLM models presents both opportunities and challenges. While these models offer efficiency and scalability, stakeholders express varying perspectives on their role and impact. Customers seek faster responses and personalized interaction, but value transparency and the human touch support agents appreciate the efficiency of LLM models but emphasize the importance of empathy and collaboration. Industry experts they foresee continued advancements but stress the need for ethical considerations and adaptation. Amidst these perspectives, an intriguing question arises. Are LLM models truly revolutionizing customer support, or do they risk undermining the essential elements of human connection and trust? As we embark on this journey of innovation, finding the balance between automation and human interaction will be crucial in shaping the future of customer support. Thank you for your attention. Hopefully it was very interesting for.