Shaping Early-Career Success in the Age of AI
A University of Texas at Austin and KPMG LLP field study of 523 US-based early-career professionals finds that uniquely human skills create value only when they are applied to direct and improve AI.
July 23, 2026 — NEW YORK, NY and AUSTIN, TX — A field study of 523 early-career professionals working with AI agents reveals that employees with nearly identical knowledge and skill can produce dramatically different results once AI enters the workflow, offering organizations greater insight into developing early-career talent in the AI era.
The joint study by KPMG LLP, the U.S. audit, tax, and advisory firm, and the McCombs School of Business at The University of Texas at Austin, published today in Harvard Business Review, finds that as AI grows more capable, performance depends just as much on how they direct AI and evaluate its work as what they know. The encouraging news for organizations: many employees who don’t yet outperform AI already have what it takes to become high performers with the right support.
What Distinguishes Employees Who Create Value Beyond What AI Can Do Alone?
In the study, early-career professionals completed work using an AI agent that closely mirrored client work they would perform in their area of focus. The research team first established an AI-only baseline by asking agents to complete the work without any human involvement. Then, they measured how these early-career professionals performed when collaborating with the same AI. The analysis surfaced three distinct performance profiles:
- AI Amplifiers (50.1%) outperformed the AI baseline.
- AI Delegators (25.8%) produced results comparable to AI alone.
- AI Apprentices (24.1%) performed below the AI baseline.
The surprise: traditional measures of capability didn't explain the gap. Those who outperformed AI looked nearly identical on paper to those who didn't.
"We weren't simply looking for people who knew how to use AI," said Ashish Agarwal, professor at The University of Texas at Austin and co-author of the study. "We wanted to understand what enables some individuals to consistently create value beyond what AI can produce on its own."
AI Apprentices matched Amplifiers and scored higher than Delegators on every foundational skill, despite landing below the baseline. Apprentices also critiqued AI's output, however, the critiques rarely improved it, often chasing irrelevant issues or steering the AI the wrong way. That makes them the biggest pool of untapped potential: if Apprentices can pair their traditional capability with more sophisticated AI use, they will outperform AI. This cohort underscores what we learned in our first research collaboration with UT Austin – organizations need to invest in continuous learning and development to help employees become more effective and sophisticated users of AI.
AI Delegators scored lowest on foundational skills but weren't the weakest performers, because AI already produces competent output. They accepted it with little scrutiny and added little of their own. Many typical low performers likely land in this category, which requires organizations to implement new approaches to performance assessment that evaluate critical thinking and judgment in how work is done, not just the outputs delivered.
AI Amplifiers turned capability into performance, orchestrating the workflow, framing problems to guide the AI, anchoring the work in real domain frameworks, and refining results across multiple rounds. They treated AI as a collaborator that needed direction, oversight, and judgment. This underscores that performance can be scaled by elevating these individuals into coaches who continuously raise how teams work with AI.
KPMG is bringing these insights into the work the firm is doing to help clients redesign workforce development, learning programs, and role design for an AI-enabled future. As AI handles more of the baseline of early-career knowledge work, the organizations that adapt best won't just build AI-literate employees. They'll build new operating models where value comes down to how well people apply judgment inside well-designed AI workflows.
How KPMG is Applying the Insights to Develop Talent
The study focused on early-career professionals, but the implications run across the whole workforce, and they're reshaping how KPMG is approaching employee development.
For example, this summer, KPMG launched You Can with AI: Next Level Learning, a firmwide initiative to grow AI Amplifier behaviors for all levels, including partners. Employees start with a skills check that assesses how they interact with AI and approach problem solving, then follow personalized pathways that blend coursework, simulations, on-the-job practice, and a growing AI champions network. Much of the learning happens in the flow of work, with simulation exercises that mirror real client scenarios. This same approach is reshaping National Intern Training at KPMG Lakehouse, starting with Audit interns this summer and expanding across Tax and Advisory.
“This is the most AI-native generation entering the workforce, so if fluency with the tools isn't what sets the top performers apart, that tells us something about our entire workforce," said Rahsaan Shears, AI enterprise transformation leader at KPMG US. "How people applied their knowledge and skill is what made the difference, and that gap is coachable. The opportunity for organizations is to build the training and workflows that enable far more people to turn their knowledge and skill into impact, at every level.”
This is the second study in collaboration with the University of Texas at Austin to identify the skills, behaviors, and capabilities that enable people to create value in the age of AI.
About KPMG LLP
KPMG LLP is the U.S. member firm of the KPMG global organization of independent member firms providing audit, tax and advisory services. The KPMG global organization operates in 138 countries and territories and has more than 276,000 people working in member firms around the world. Each KPMG firm is a legally distinct and separate entity and describes itself as such. KPMG International Limited is a private English company limited by guarantee. KPMG International Limited and its related entities do not provide services to clients.
KPMG is widely recognized for being a great place to work and build a career. Our people share a sense of purpose in the work we do, and a strong commitment to increasing access to education and opportunity, advancing mental health, and supporting community vitality. Learn more at www.kpmg.com/us.
Q&A
| Question | Answer |
| What is the main finding of the KPMG and University of Texas at Austin study on early-career professionals and AI? | The study found that employees with nearly identical knowledge and skills can achieve significantly different results when working with AI. Performance depends not only on what employees know, but also on how effectively they direct AI, evaluate its outputs, and refine results. |
| How was the research conducted? | The field study involved 523 US-based early-career professionals at KPMG LLP who completed work using an AI agent that closely mirrored real client work in their areas of focus. Researchers first established an AI-only baseline and then compared the performance of employees collaborating with the same AI. |
| What percentage of participants outperformed AI working alone? | Just over half of participants (50.1%) were classified as “AI Amplifiers,” meaning they delivered results that exceeded the performance of the AI-only baseline. |
| What are the three performance profiles identified in the study? | Researchers identified three groups: AI Amplifiers (50.1%), who outperformed the AI baseline; AI Delegators (25.8%), who produced results comparable to AI alone; and AI Apprentices (24.1%), who performed below the AI baseline. |
| What distinguishes AI Amplifiers from other employees? | AI Amplifiers effectively orchestrate the workflow, frame problems for AI, apply domain expertise, critically evaluate outputs, and refine results through multiple iterations. They treat AI as a collaborator that requires direction, oversight, and judgment. |
| Why is the AI Amplifier group important for organizations? | AI Amplifiers demonstrate how human judgment can create value beyond what AI can achieve independently. The study suggests organizations can scale performance by elevating these individuals into coaching and mentoring roles that improve AI collaboration across teams. |
| What did the study reveal about traditional measures of capability? | Traditional indicators of capability did not explain the performance gap. Employees who outperformed AI looked remarkably similar on paper to those who did not, suggesting that AI collaboration skills are becoming a critical differentiator. |
| Who are AI Apprentices, and why do they matter? | AI Apprentices accounted for 24.1% of participants and performed below the AI-only baseline despite demonstrating strong foundational skills. They often critiqued AI output but focused on issues that did not improve results. The study identifies this group as a major source of untapped potential because better AI collaboration skills could help them outperform AI. |
| What challenges do AI Apprentices face when working with AI? | While AI Apprentices actively engaged with AI outputs, their feedback often steered AI in unproductive directions or focused on less relevant issues, preventing them from translating strong foundational capabilities into stronger outcomes. |
| What are AI Delegators, and how do they typically work with AI? | AI Delegators represented 25.8% of participants and generally accepted AI-generated outputs with limited scrutiny or refinement. Although they scored lowest on foundational skills, they still achieved results comparable to AI alone because the technology already produces competent output. |
| What does the study suggest about evaluating employee performance in the AI era? | The findings suggest organizations should assess not only final outputs but also how employees apply critical thinking, judgment, and oversight when working with AI. Existing performance measures may not fully capture these capabilities. |
| How can organizations help more employees become high performers with AI? | The study suggests many employees already possess the foundational skills needed for success. Organizations can unlock greater performance through continuous learning, practical AI training, coaching, experimentation, and workflow designs that strengthen AI collaboration skills. |
| What implications does the research have for workforce development? | As AI increasingly handles baseline knowledge work, workforce development should focus on teaching employees how to guide, evaluate, and improve AI outputs rather than simply use AI tools. This requires new learning models, role designs, and performance frameworks. |
| How is KPMG applying these findings internally? | KPMG has launched “You Can with AI: Next Level Learning,” a firmwide initiative designed to build AI Amplifier behaviors across all levels of the organization, including partners. The program combines assessments, personalized learning pathways, simulations, on-the-job practice, and support from an AI champions network. |
| How does KPMG’s AI learning approach differ from traditional training programs? | KPMG’s approach emphasizes learning in the flow of work through simulations based on real client scenarios, personalized development pathways, and practical application rather than relying solely on classroom-style instruction. |
| How are these insights influencing KPMG’s intern development programs? | The firm is incorporating this approach into National Intern Training at KPMG Lakehouse, beginning with Audit interns and expanding across Tax and Advisory, helping early-career professionals build effective AI collaboration skills from the start of their careers. |
| What does the study suggest about AI-native generations entering the workforce? | The findings indicate that familiarity with AI tools alone does not distinguish top performers. Instead, the ability to apply judgment, direct AI effectively, and translate knowledge into impact is what separates higher-performing employees from their peers. |
| Why is this research significant for business leaders? | The study provides evidence that competitive advantage in an AI-enabled workplace comes from how people work with AI, not simply from access to AI tools. Organizations that develop employees’ judgment, oversight, and AI collaboration capabilities may be better positioned to unlock workforce productivity and value creation. |
| What broader message does this research send about the future of work? | As AI becomes more capable, human value increasingly comes from directing, evaluating, and improving AI-enabled work. Organizations that redesign learning, talent development, and workflows around these capabilities are likely to be better prepared for the future of work. |
| Is this KPMG’s first collaboration with the University of Texas at Austin on AI workforce research? | No. This is the second study conducted jointly by KPMG and the University of Texas at Austin aimed at identifying the skills, behaviors, and capabilities that enable people to create value in the age of AI. |
Media Contacts
KPMG: Olivia Weiss (oweiss@kpmg.com) and/or Alyssa Mora (alyssamora@kpmg.com).
UT Austin: Judie Kinonen (judie.kinonen@mccombs.utexas.edu)
