The Invisible Gap: How AI Productivity Masks Eroding Expertise in Knowledge Work – and what to do about it
Generative and agentic AI is rapidly reshaping how knowledge workers think, learn, and produce — lifting productivity substantially, with the largest gains concentrated among novices and lower-skilled workers (Brynjolfsson et al., 2025). Yet the same dynamic raises a deeper question: if AI absorbs the mundane tasks through which expertise is traditionally honed, what happens to the foundational skills workers need when AI is wrong, or absent? How do knowledge workers experience this shift in their daily practice? How do they acquire and apply expertise when routine work is increasingly delegated to agents, and how can organizations realize AI's productivity gains without eroding the human-capital base they depend on? We investigate how knowledge workers' routine use of AI changes skill application and skill acquisition in real-world corporate settings, anchoring our analysis in the "paradox of automation" (De Bruyn et al., 2020) — the observation that the mundane tasks easiest to delegate to AI are precisely those that hone the foundational skills required for the non-routine work AI cannot reliably perform. In this paper, we present findings from a series of 30 semi-structured interviews with working professionals aged 18 to 40+, spanning junior to leadership ranks and seven nationalities. In general, knowledge workers report substantial productivity, multitasking, and speed gains from delegating execution to AI; however, this poses challenges: junior and lower-skilled workers achieve strong early-career performance while missing the practice of mundane tasks that traditionally build expertise for complex, non-routine work. A key risk is invisibility — because AI can sustain high performance while offloading cognition, workers may not notice they are unable to become proficient without AI assistance, creating dangerous gaps when AI is wrong or absent. Beyond individual learning, this raises questions of corporate resilience, as firms that systematically displace mundane practice may erode the expert reserve they depend on when AI becomes unavailable. Corporate environments thus face a real trade-off between short-term productivity gains and long-term expertise sustainability — and may need to deliberately design conditions where AI is withdrawn, tasks are transferred for review, or workers must troubleshoot independently, to ensure knowledge workers can critically assess AI outputs and sustain expertise across generations of the workforce. This talk explores how companies train their workforce to build for a sustainable AI-leveraging future and what stance leading practitioners take on the automation paradox as a whole. It proposes a framework for assessing the independence of the corporate workforce from AI and the extent of its reliance on performance. Simultaneously, we show that workforces can excel at the balancing act of leveraging AI to build productivity skills while simultaneously preventing skill deterioration and building the skills required to contest and assess AI- and agentic-driven outcomes.