Will AI mean the end of call centres? Ask ChatGPT whether AI will replace humans in the customer service industry, and it will offer a diplomatic answer, the summary of which is they will work side by side. Humans though, are not so optimistic. Last year, the chief executive of Indian technology firm Tata Consultancy Services, K Krithivasan, told the Financial Times that AI may soon mean that there is minimal need for call centres in Asia. Meanwhile, AI will autonomously resolve 80% of common customer service issues by 2029, predicts business and technology research firm Gartner.

This research proposes a systematic, large language model (LLM) approach for extracting product and service attributes, features, and associated sentiments from customer reviews. Grounded in marketing theory, the framework distinguishes perceptual attributes from actionable features, producing interpretable and managerially actionable insights. We apply the methodology to 20,000 Yelp reviews of Starbucks stores and evaluate eight prompt variants on a random subset of reviews. Model performance is assessed through agreement with human annotations and predictive validity for customer ratings. Results show high consistency between LLMs and human coders and strong predictive validity, confirming the reliability of the approach. Human coders required a median of six minutes per review, whereas the LLM processed each in two seconds, delivering comparable insights at a scale unattainable through manual coding. Managerially, the analysis identifies attributes and features that most strongly influence customer satisfaction and their associated sentiments, enabling firms to pinpoint "joy points," address "pain points," and design targeted interventions. We demonstrate how structured review data can power an actionable marketing dashboard that tracks sentiment over time and across stores, benchmarks performance, and highlights high-leverage features for improvement. Simulations indicate that enhancing sentiment for key service features could yield 1-2% average revenue gains per store.

In AI-powered e-commerce livestreaming, digital avatars require real-time responses to drive engagement, a task for which high-latency Large Reasoning Models (LRMs) are ill-suited. We introduce LiveThinking, a practical two-stage optimization framework to bridge this gap. First, we address computational cost by distilling a 670B teacher LRM into a lightweight 30B Mixture-of-Experts (MoE) model (3B active) using Rejection Sampling Fine-Tuning (RFT). This reduces deployment overhead but preserves the teacher's verbose reasoning, causing latency. To solve this, our second stage employs reinforcement learning with Group Relative Policy Optimization (GRPO) to compress the model's reasoning path, guided by a multi-objective reward function balancing correctness, helpfulness, and brevity. LiveThinking achieves a 30-fold reduction in computational cost, enabling sub-second latency. In real-world application on Taobao Live, it improved response correctness by 3.3% and helpfulness by 21.8%. Tested by hundreds of thousands of viewers, our system led to a statistically significant increase in Gross Merchandise Volume (GMV), demonstrating its effectiveness in enhancing user experience and commercial performance in live, interactive settings.

AI investments are pulling the US economy forward. Despite United States President Donald Trump's tariff and immigration policies roiling businesses, the US economy is relatively stable. Experts say the country can thank the artificial intelligence (AI) industry for that. "AI machines--in quite a literal sense--appear to be saving the US economy right now," George Saravelos of Deutsche Bank wrote to his clients at the end of September. "In the absence of tech-related spending, the US would be close to, or in, recession this year." AI companies are investing hundreds of billions of dollars into AI infrastructure and development, and other US companies are spending billions on AI products.

Electric power marketing telephone customer service primarily communicates with customers via phone calls to understand their electricity usage needs, provide consultations, process service applications, and handle complaints [1]. Ensuring timely and effective responses is essential throughout the service process. However, current systems (e.g., 95598, the customer service hotline of State Grid Corporation of China) often suffer from poor user experience, delayed responses, and inaccurate information[2] [3]. These traditional systems rely heavily on fixed procedures and templates, lacking the flexibility to address complex and diverse customer demands. This limitation is particularly pronounced in the highly specialized field of electric power marketing, where slow response times and insufficiently tailored solutions negatively impact service quality. Although human agents can complement these systems by managing more complex issues, they also face significant challenges, such as high workloads during peak periods, delayed response times, and inconsistent levels of professional knowledge and expertise. As a result, it is difficult to guarantee consistent and high-quality service for all customers.

High-quality dialogue is crucial for e-commerce customer service, yet traditional intent-based systems struggle with dynamic, multi-turn interactions. We present MindFlow+, a self-evolving dialogue agent that learns domain-specific behavior by combining large language models (LLMs) with imitation learning and offline reinforcement learning (RL). MindFlow+ introduces two data-centric mechanisms to guide learning: tool-augmented demonstration construction, which exposes the model to knowledge-enhanced and agentic (ReAct-style) interactions for effective tool use; and reward-conditioned data modeling, which aligns responses with task-specific goals using reward signals. To evaluate the model's role in response generation, we introduce the AI Contribution Ratio, a novel metric quantifying AI involvement in dialogue. Experiments on real-world e-commerce conversations show that MindFlow+ outperforms strong baselines in contextual relevance, flexibility, and task accuracy. These results demonstrate the potential of combining LLMs tool reasoning, and reward-guided learning to build domain-specialized, context-aware dialogue systems.

Fox News anchor Bret Baier examines the U.S. power supply on'Special Report.' POWER UP: A new White House study warns that electricity prices may spike due to artificial intelligence demand if the United States does not boost energy output. TURNED OFF: Google is making a push to ensure its AI, Gemini, is tightly integrated with Android systems by granting it access to core apps like WhatsApp, Messages, and Phone. The rollout of this change started on July 7, 2025, and it may override older privacy configurations unless you know how to disable Gemini on Android. Here's what you need to know. OPINION: DIGITAL DOMINANCE: The global race to harness the power of artificial intelligence (AI) has begun.

Fox News chief political anchor Bret Baier investigates concerns that artificial intelligence is becoming too advanced on'Special Report.' Every day, customers reach out to companies. They want to buy something, ask about an order, return a product or fix a payment issue. In the past, that usually meant talking to a real person on the phone or through a website. More often, the first reply comes from a chatbot.

Robot avatars for customer service are gaining traction in Japan. However, their acceptance in other societal contexts remains underexplored, complicating efforts to design robot avatars suitable for diverse cultural environments. To address this, we interviewed key stakeholders in Dubai's service sector to gain insights into their experiences deploying social robots for customer service, as well as their opinions on the most useful tasks and design features that could maximize customer acceptance of robot avatars in Dubai. Providing information and guiding individuals to specific locations were identified as the most valued functions. Regarding appearance, robotic-looking, highly anthropomorphic designs were the most preferred. Ultra-realistic androids and cartoonish-looking robots elicited mixed reactions, while hybrid androids, low-anthropomorphic robotic designs, and animal-looking robots were considered less suitable or discouraged. Additionally, a psycho-sociological analysis revealed that interactions with robot avatars are influenced by their symbolic meaning, context, and affordances. These findings offer pioneering insights into culturally adaptive robot avatar design, addressing a significant research gap and providing actionable guidelines for deploying socially acceptable robots and avatars in multicultural contexts worldwide.

Sahan Hewage Wewelwala School of Computing Informatics Institute of Technology Colombo 06, Sri Lanka sahanwewelwala@gmail.com T.G.D.K. Sumanathilaka Department of Computer Science Swansea University Swansea, Wales, United Kingdom deshankoshala@gmail.com Abstract -- This research presents a hybrid emotion recognition system integrating advanced Deep Learning, Natural Language Processing (NLP), and Large Language Models (LLMs) to analyze audio and textual data for enhancing customer interactions in contact centers. By combining acoustic features with textual sentiment analysis, the system achieves nuanced emotion detection, addressing the limitations of traditional approaches in understanding complex emotional states. Rigorous testing on diverse datasets demonstrates the system's robustness and accuracy, highlighting its potential to transform customer service by enabling personalized, empathetic interactions and improving operational efficiency. This research establishes a foundation for more intelligent and human - centric digital communication, redefining customer service standards. The capacity to identify and comprehend emotions effectively is an essential element of human - computer interaction, especially in spoken and written communication.