Research: Why Some Junior Employees Work Well with AI-and Others Don’t
A study by KPMG and the University of Texas at Austin examines how early-career professionals can maintain value in the workplace as AI takes over routine analytical tasks. The research highlights the need for organizations to intentionally develop human judgment and decision-making skills to complement AI workflows.
Why it matters
As AI automates entry-level knowledge work, companies must rethink how they train junior staff to ensure long-term talent development.
Entry-level employees are on the front lines of a rapid shift in knowledge work. These jobs used to be full of tasks that created an onramp into an industry and helped junior employees start building expertise. But now, many of these tasks are at risk of being delegated to AI-powered workflows, which are increasingly capable of handling analytical, information-intensive assignments. And as AI continues to improve, research has shown it is quickly resetting the baseline, with the standard for acceptable output rising as models improve. In this context, organizations need to understand how individuals create value beyond the AI baseline in real organizational settings, particularly in higher-stakes professional work requiring judgment, domain expertise, and decision-ready outputs. That raises two big questions: 1) What enables early career employees to add value in AI‑enabled workflows?
Get smarter about the news
Sign up free for a feed built around what you actually care about, Dive Deeper research on any story, and the full text of every article.
Create free accountAlready have an account? Sign in