The integration of various AI tools creates a complex socio-technical environment where employee-customer interactions form the core of work practices. This study investigates how customer service representatives (CSRs) at the power grid service customer service call center perceive AI assistance in their interactions with customers. Through a field visit and semi-structured interviews with 13 CSRs, we found that AI can alleviate some traditional burdens during the call (e.g., typing and memorizing) but also introduces new burdens (e.g., earning, compliance, psychological burdens). This research contributes to a more nuanced understanding of AI integration in organizational settings and highlights the efforts and burdens undertaken by CSRs to adapt to the updated system.

While it might sound like something out of an episode of Black Mirror, scientists have warned that AI might be coming to take your job. Microsoft researchers have revealed the 40 jobs most likely to be pushed out by artificial intelligence - and the 40 most likely to remain human. And it's bad news for anyone who has been brushing up on their language skills, since interpreters and translators are right at the top of the list. Historians, writers and authors, political scientists, and journalists are also likely to face increasing automation in the coming years. However, it isn't just jobs involving reading and writing that could be on the chopping block.

A Los Angeles man said he recently missed his flight home after getting trapped on his way to the airport in a Waymo that wouldn't stop making circles in a parking lot. L.A. tech entrepreneur Mike Johns posted a video three weeks ago on LinkedIn of his call to a customer service representative for Waymo to report that the car kept turning in circles and that he was nervous about missing his flight. "I got a flight to catch. Why is this thing going in a circle? I'm getting dizzy," Johns said.

As modern Large Language Models (LLMs) shatter many state-of-the-art benchmarks in a variety of domains, this paper investigates their behavior in the domains of ethics and fairness, focusing on protected group bias. We conduct a two-part study: first, we solicit sentence continuations describing the occupations of individuals from different protected groups, including gender, sexuality, religion, and race. Second, we have the model generate stories about individuals who hold different types of occupations. We collect >10k sentence completions made by a publicly available LLM, which we subject to human annotation. We find bias across minoritized groups, but in particular in the domains of gender and sexuality, as well as Western bias, in model generations. The model not only reflects societal biases, but appears to amplify them. The model is additionally overly cautious in replies to queries relating to minoritized groups, providing responses that strongly emphasize diversity and equity to an extent that other group characteristics are overshadowed. This suggests that artificially constraining potentially harmful outputs may itself lead to harm, and should be applied in a careful and controlled manner.

Contacting customer service via chat is a common practice. Because employing customer service agents is expensive, many companies are turning to NLP that assists human agents by auto-generating responses that can be used directly or with modifications. Large Language Models (LLMs) are a natural fit for this use case; however, their efficacy must be balanced with the cost of training and serving them. This paper assesses the practical cost and impact of LLMs for the enterprise as a function of the usefulness of the responses that they generate. We present a cost framework for evaluating an NLP model's utility for this use case and apply it to a single brand as a case study in the context of an existing agent assistance product. We compare three strategies for specializing an LLM - prompt engineering, fine-tuning, and knowledge distillation - using feedback from the brand's customer service agents. We find that the usability of a model's responses can make up for a large difference in inference cost for our case study brand, and we extrapolate our findings to the broader enterprise space.

Artificial intelligence refers to the creation of intelligent machines capable of performing cognitive tasks. Their ability to think like humans will increase once they have enough data. Digital marketing is a key area where artificial intelligence, data, and analytics are important. Any online venture must be able to extract the right insights from data in order to succeed. It is therefore logical to assume that AI will become a key component of digital marketing.

ChatGPT, a cutting-edge language model developed by OpenAI, has been making waves in the tech and marketing industries for its ability to generate frameworks for recurring projects and tasks - and some are even using it as an extension of their executive team. "I ask ChatGPT to become aware of where my biases and blindspots might be, and the answers it gives are a really, really good starting point to check your thinking," Jeff Maggioncalda, CEO of Coursera. In 2022, the adoption of AI maintained a stable pace, up 4 points from the previous year, as 35% of businesses reported utilizing AI in their operations. The increased accessibility of AI played a key role in this growth, making it simpler for companies to implement AI throughout their organization, according to IBM's Global AI Adoption Index. Businesses are turning to AI to automate tasks and cut costs, contributing to its widespread adoption.

Consumer expectations are being set by voice assistants like Siri, Google Assistant, and Alexa. And we're coming to expect this same type of interaction and rapid response when we communicate with businesses. Given this, solutions like conversational AI will soon be a requirement for every company's contact center. Conversational AI enables consumers to interact with computer applications as they would with humans, similar to our experiences with digital assistants in the home. With conversational AI, organizations can replace inadequate chatbots and unwieldy interactive voice response (IVR) menus and simply ask customers, "How can I help?" -- and then get them to the right place.

Pressing, searching, and hunting for information is a thing of the past. Until recently, employees across industries had to scroll search engines, wait on co-worker responses, and scan through company memos and files just to find the answer to a simple question using NLQA. Specific machine learning and artificial intelligence techniques allow workers to proactively understand their information with the help of natural language question answering (NLQA). NLQA understands spoken or written verbiage to provide on-the-spot question answering. Subsets of NLQA, like natural language processing (NLP) and natural language understanding (NLU), have the ability to extract tone and intent behind all sorts of text.

The topic of how artificial intelligence can transform banking continues to get a massive amount of attention. With data in abundance, and the need to improve efficiency and create better customer experiences, every new evolution of AI creates opportunities, while also raising questions around privacy, biases, the impact on the human workforce, and changes in existing business models. One of the most talked about advances in the deployment of AI occurred on November 30, when OpenAI released ChatGPT, deemed "the most advanced, user-friendly chatbot to enter the public domain." ChatGPT can create high-level content, respond to customer inquiries, assist with research, and provide perspectives on current trends. OpenAI, a nonprofit company, was founded in 2015 by Sam Altman, Elon Musk and other Silicon Valley investors.