Although he is an expert in artificial intelligence and its use and applications in companies and organizations, Hunter Phoenix Van Wagoner, is also a humanist at heart.<\/p>\n
So, when he studies and analyzes the implementation and effects of AI into the workplace, his concerns go beyond just the x\u2019s and o\u2019s \u2013 or the ones and zeros \u2013 of efficiencies to the human costs and benefits.<\/p>\n
\u201cI want to understand the employee side,\u201d Van Wagoner, assistant professor of Management for the College of Business and Economics at CSUF said. \u201cI worry about the employee side.\u201d<\/p>\n
There is no doubt that the world, and businesses and organizations in particular, are hurtling toward an AI-dominated future.<\/p>\n
According to a report<\/a> by Stanford\u2019s Human-Centered Artificial Intelligence research institute, global AI private investment hit a record high and 26% overall growth with $252.3 billion in 2024. And when the stats for 2025 and 2026 are tallied, they have been projected to continue the trend.In the shadow of these climactic shifts, there is a very human element. And Van Wagoner doesn\u2019t want humans to be forgotten in the rush to automate and turn decision-making over to machines.<\/p>\n Van Wagoner examined both the mechanisms and the implications of human-AI collaboration in organizations in a 2025 research article<\/a> for the \u201cJournal of Organizational Behavior.\u201d<\/p>\n In \u201cNavigating AI Convergence in Human\u2013Artificial Intelligence Teams: A Signaling Theory Approach,\u201d Van Wagoner and a German research team conducted a study with about 1,100 participants looking at how humans and AI worked together when analyzing facial recognition and hiring.<\/p>\n The reason for facial recognition and hiring as categories of study is that they contain \u201chigh-uncertainty\u201d in business parlance.<\/p>\n To do this, researchers studied their findings through signaling theory, which explains how individuals interpret, respond and make decisions based on AI signals \u2014\u00a0 recommendations, predictions and classifications \u2014 under conditions of uncertainty.<\/p>\n Researchers sought to understand \u201cwhen and why do humans align their decisions with AI recommendations.\u201d<\/p>\n This phenomenon \u2014 known as AI convergence \u2014 is critical for unlocking the full potential of human\u2013AI teamwork.\u201d<\/p>\n In other words, can humans and AI \u201cplay nice in the sandbox?\u201d<\/p>\n Many employees and companies find some sort of AI useful in their work. This work can range from chatbots, which can answer specific questions using existing data; to generative AI that can create new, original content by analyzing patterns; to AI agents that can act autonomously to achieve goals without constant human oversight.<\/p>\n