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Enabling value creation in an AI-driven era
Organizations must clearly define how employees can add value when working with AI, as well as how they can continuously build their capabilities as AI evolves.
Leading organizations recognize that positioning professionals for success in the AI era is a strategic imperative. This focus is essential not only for driving business value but also for enabling a future-ready workforce that can keep pace with technological change.
AI is magnifying differences in applied skills
To better understand what enables employees to create value in AI‑enabled workflows, KPMG LLP and the McCombs School of Business at The University of Texas at Austin conducted a large-scale field study of 523 early-career professionals who were asked to complete business-specific workflows using a domain-specific AI agent.
The findings uncovered three distinct performance profiles and revealed that foundational capabilities—such as critical thinking, AI literacy, and domain knowledge—were not clear signals of performance. Instead, what differentiated employees was how effectively they worked with AI.
Three ways employees perform with AI
Below, explore the defining behaviors of each performance profile and the opportunities they present for organizations to unlock greater potential.
AI Amplifiers outperform AI
Setting the standard for human-AI performance, Amplifiers apply their strong foundational skills throughout every stage of working with AI. They orchestrate workflows, guide analyses, define evaluation criteria, and iteratively refine results.
Growth opportunity: Scale Amplifier behaviors across the workforce by elevating these individuals into coaches. Continue to deepen their capabilities through more complex, high-value work.
AI Delegators produce results comparable to AI
Delegators score lowest on foundational skills yet are not the weakest performers. They maintain productivity but add little value beyond what AI produces alone. Because this behavior often goes unnoticed, underperformance can persist.
Growth opportunity: Strengthen their capabilities and ability to apply them when working with AI. Rethink performance assessment to evaluate critical thinking and judgment in how work is done, not just results.
AI Apprentices underperform AI
Apprentices represent untapped potential. They possess strong foundational skills, scoring comparably to Amplifiers and above Delegators. However, while they often critique AI responses, their feedback rarely improves outputs because it is frequently misdirected or unproductive.
Growth opportunity: Redesign workflows and provide clear guidance, practical use cases, and training to help them translate foundational capabilities into stronger AI-enabled performance.
AI Amplifiers outperform AI
AI Amplifiers outperform AI
Setting the standard for human-AI performance, Amplifiers apply their strong foundational skills throughout every stage of working with AI. They orchestrate workflows, guide analyses, define evaluation criteria, and iteratively refine results.
Growth opportunity: Scale Amplifier behaviors across the workforce by elevating these individuals into coaches. Continue to deepen their capabilities through more complex, high-value work.
AI Delegators produce results comparable to AI
AI Delegators produce results comparable to AI
Delegators score lowest on foundational skills yet are not the weakest performers. They maintain productivity but add little value beyond what AI produces alone. Because this behavior often goes unnoticed, underperformance can persist.
Growth opportunity: Strengthen their capabilities and ability to apply them when working with AI. Rethink performance assessment to evaluate critical thinking and judgment in how work is done, not just results.
AI Apprentices underperform AI
AI Apprentices underperform AI
Apprentices represent untapped potential. They possess strong foundational skills, scoring comparably to Amplifiers and above Delegators. However, while they often critique AI responses, their feedback rarely improves outputs because it is frequently misdirected or unproductive.
Growth opportunity: Redesign workflows and provide clear guidance, practical use cases, and training to help them translate foundational capabilities into stronger AI-enabled performance.
How to cultivate a workforce of AI Amplifiers
Our research points to a significant opportunity for organizations to rethink employee development, performance measures, and the work itself.
Employees with similar levels of foundational skills and knowledge can produce dramatically different outcomes when working with the same AI agent, and without deliberate intervention, these differences may widen over time. The encouraging news for organizations: many employees who are not yet outperforming AI already have what it takes to become high performers with the right support.
To help employees maximize value from human-AI collaboration, organizations should focus on three priorities:
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Enabling professionals in the age of AI requires a shift from treating AI as a tool for individual tasks to rethinking how work gets done. Organizations that adapt the most effectively will go beyond developing AI literacy, to building new operating models where value comes down to how effectively employees apply their foundational skills inside well-designed AI workflows.
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. 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.
Rahsaan Shears
AI Enterprise Transformation Leader, KPMG US
Methodology
Study setup
KPMG LLP and the McCombs School of Business at The University of Texas at Austin conducted a field study with 523 US-based early-career KPMG professionals with less than 18 months of tenure to understand how people create value when working with generative AI on realistic business tasks.
The tasks were designed by experienced KPMG managers and reflected common early-career work, including analyzing business, financial and operational information, identifying risks and opportunities, and developing recommendations.
Performance assessment
For each task, the AI also completed the assignment independently. The researchers compared each human-AI report with this AI-only benchmark to determine whether human involvement improved or reduced performance.
Workplace capabilities
The researchers measured both participants’ foundational capabilities and applied skills demonstrated during the work.
Foundational capabilities included critical thinking, domain knowledge, and AI literacy. Applied skills were assessed from participants’ interactions with the AI and focused on three areas:
- Planning: Structuring the task and directing the AI
- Monitoring: Evaluating, questioning, and refining AI outputs
- Integration: Combining AI-generated insights with professional judgment and domain knowledge
These measures captured both what professionals knew and how effectively they applied that knowledge when working with AI.
Findings published in HBR
For more on this study, read the Harvard Business Review article.
Sophisticated AI collaboration: An inside look at high-impact use
Discover more research from KPMG and The University of Texas at Austin. This landmark study of 1.4 million real workplace interactions with AI reveals the employee behaviors behind effective AI use—and how they can be taught at scale.
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