The next phase of the corporate AI boom: measuring which employees actually understand it

In the rapidly evolving landscape of artificial intelligence, corporate leaders are emphasizing their AI adoption rates in a manner reminiscent of quarterly revenue announcements. Recent data from AlphaSense reveals that CEOs have mentioned "early AI adopters" at least 60 times within the past three months during earnings calls, conferences, and speeches. This trend highlights their competitive drive to showcase advancements in AI utilization. However, a significant pivot is underway among major U.S. companies, as they transition from merely adopting AI technologies to evaluating their workforce's understanding of these innovations. Kian Katanforoosh, CEO and founder of Workera, a platform dedicated to business skills intelligence, remarked, "If you can measure people fairly accurately, you can actually do a lot of things better in society." He pointed out that this measurement can lead to improved hiring practices, better project alignment, and targeted learning opportunities within organizations. Workera currently collaborates with around 10% of Fortune 500 companies, aiding them in grasping AI concepts and enhancing employee skills from fundamental terminology to recognizing biases inherent in AI systems. Their comprehensive AI fluency framework extends beyond just knowing how to interact with tools like ChatGPT, encompassing three critical areas: AI, generative AI, and responsible AI. At its core, the framework evaluates employees' abilities to distinguish between machine learning, deep learning, and generative AI. It also includes the essential task of explaining the functions of an AI agent—a concept that can often seem elusive amidst the current rush in AI development. For those assessing generative AI fluency, the framework requires skills such as crafting basic AI prompts and identifying inaccuracies in AI-generated content. Additionally, employees must comprehend how large language models are trained and produce information. While responsible AI practices may not always be prioritized in large corporations, Workera's framework pushes employees to recognize various types of biases in AI systems, including algorithmic, data-related, and human biases, as well as to understand prevalent privacy risks associated with AI technologies. Initial assessments conducted by Workera reveal striking insights: only 11% of participants accurately gauge their proficiency in AI skills before taking an adaptive assessment. Meanwhile, a significant 32% overestimate their abilities, and 56% underestimate them. Katanforoosh emphasized that this movement toward assessing AI competencies marks a pivotal change in the tech industry, asserting, "The last decade in education was about access. The next decade is about measurement."