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PwC Finds AI Will Break Workers Into Four Categories
Split and labeled // Image by Kait Cunniff with DALL-E
A major PwC survey found that corporate AI education and adoption are splitting workers into four distinct categories.
The Big Picture: Companies are embedding AI into their organizations at different speeds, making it staggering how quickly some employees are becoming accustomed to the tech. The category you find yourself in may help shine a light on your ability to avoid layoffs, angle for a promotion, or score a raise where you work.
Behind The Cubicles: PwC’s “2026 Global Workforce Hopes and Fears Survey” studied roughly 50,000 workers across 48 countries and 29 sectors earlier this summer.
Here are the four categories they slotted workers into:
The Front-Runners: Making up 14% of the workforce, these employees “have in-demand skills and reported getting strong benefits from AI in their work.” Additionally, 80% have access to “learning and development resources.”
Engine-Room Workers: Making up 56% of the workforce, these are a company’s largest share of employees, and most don’t use AI. Only 40% say they have the opportunity to access resources to learn it if they want to.
AI Insurgents: Making up roughly 20% of the workforce, these employees have “skills that are less in demand than those of front-runners, but they’re ambitious in how they use AI.” In other words, these are the AI hustlers.
Indispensables: This final 10% of the workforce is described as those “who have scarce skills that are highly valued by employers.”
Closing Thoughts: No matter what category workers ultimately fall into, 64% said they’ve used AI at their jobs over the past year — 10% more than the year prior, according to Insider. Even more astounding, 22% report using generative AI every day.
The Future: With nearly every job being disrupted by AI in some way, expect an increase in interest in specialized roles that put people into the “indispensable” category.
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Today’s email was written by David Vendrell.
Edited by Nick Comney. Polled and Copy-edited by Kait Cunniff.
Published by Darline Salazar.