Client-Service Representatives (CSRs) are vital to organizations. Frequent interactions with disgruntled clients, however, disrupt their mental well-being. To help CSRs regulate their emotions while interacting with uncivil clients, we designed Pro-Pilot, an LLM-powered assistant, and evaluated its efficacy, perception, and use. Our comparative analyses between 665 human and Pro-Pilot-generated support messages demonstrate Pro-Pilot's ability to adapt to and demonstrate empathy in various incivility incidents. Additionally, 143 CSRs assessed Pro-Pilot's empathy as more sincere and actionable than human messages. Finally, we interviewed 20 CSRs who interacted with Pro-Pilot in a simulation exercise. They reported that Pro-Pilot helped them avoid negative thinking, recenter thoughts, and humanize clients; showing potential for bridging gaps in coworker support. Yet, they also noted deployment challenges and emphasized the irreplaceability of shared experiences. We discuss future designs and societal implications of AI-mediated emotional labor, underscoring empathy as a critical function for AI assistants in front-office roles.

Human resources professionals and job seekers alike may soon be able to better understand a company's unique organizational culture thanks to a new machine-learning approach. Developed by Georgia Tech researchers, the approach is the first of its kind to computationally model organizational culture using publicly available anonymized data sources – including Glassdoor user reviews. These models are illustrated using heat maps that reveal positive and negative sentiment for a company and its business units across 41 dimensions of organizational culture. The heat maps give a "cloud-contributed" sense of what the culture is like in a particular workplace and can provide actionable insights to HR teams, unit managers, and job seekers, according to the researchers. "Right now, to get a measure of organizational culture, companies rely on internal surveys, which are difficult to scale. It's also unlikely that they are getting true responses given factors like organizational bias or employee concerns about anonymity," said Vedant Das Swain, a second-year Ph.D. student studying human-computer interaction at Georgia Tech.

Human resources professionals and job seekers alike may soon be able to better understand a company's unique organizational culture thanks to a new machine-learning approach. Developed by Georgia Tech researchers, the approach is the first of its kind to computationally model organizational culture using publicly available anonymized data sources – including Glassdoor user reviews. These models are illustrated using heat maps that reveal positive and negative sentiment for a company and its business units across 41 dimensions of organizational culture. The heat maps give a "cloud-contributed" sense of what a particular workplace culture is like and can provide actionable insights to HR teams, unit managers, and job seekers, according to the researchers. "Right now, to get a measure of organizational culture, companies rely on internal surveys, which are difficult to scale. It's also unlikely that they are getting true responses given factors like organizational bias or employee concerns about anonymity," said Vedant Das Swain, a second-year Ph.D. student studying human-computer interaction at Georgia Tech.