Corporate leaders are increasingly crediting artificial intelligence over human employees for project achievements, inadvertently stalling promotions and suppressing compensation for knowledge workers. The trend has created a professional dilemma where disclosing AI assistance often triggers what researchers term the AI penalty, causing managers to assume technology executed the core work and devaluing the employee contribution. Anecdotal evidence highlights the career risks. A New York healthcare analyst reported that despite investing a year in developing a manufacturing process improvement, management insisted she attribute the breakthrough entirely to an AI chatbot during a leadership presentation. Her subsequent annual review reflected diminished performance metrics, while a Fortune 500 developer in India suspects automated coding agents have similarly derailed his promotion trajectory. These cases reflect a broader workplace dynamic where transparency regarding AI usage is penalized rather than rewarded. Academic research supports these observations. A Northeastern University meta-analysis of thirteen workplace studies found that managers consistently downsize employee contributions when AI assistance is disclosed. The assumption that AI performs the heavy lifting stems from a lack of granular reporting on human-machine collaboration. Current corporate tracking metrics, such as monitoring AI token consumption, fail to capture creative input or decision-making agency. Recognizing the distortion, Amazon recently discontinued an internal leaderboard that gamified token usage, with senior leadership explicitly discouraging performative AI adoption. To address the attribution gap, technology firms and academia are developing precision reporting frameworks. IBM introduced the AI Attribution Toolkit, a standardized system allowing employees to quantify machine-generated content versus human review and refinement. Similarly, Carnegie Mellon University researchers pioneered OpenHands, an open-source coding platform that tags AI-generated lines to clarify development responsibilities. Despite these innovations, early implementation faces pushback. An Adidas engineering executive noted that mandatory attribution quietly suppressed innovation, as developers avoided AI tools to prevent their work from being labeled as co-authored by algorithms. The underlying tension lies in accountability and trust. University of Arizona sociologist Oliver Schilke observes that mandatory disclosure erodes collegial trust and places the burden of moral and professional navigation on individual workers. Meanwhile, the accountability paradox remains acute: employees receive institutional praise for AI-driven efficiency but retain full liability when errors occur, a dynamic Amazon has already enforced in layoff decisions tied to automated agent failures. Learning platform CEO Alessio Artuffo argues that attribution models must shift from documenting tool usage to verifying human oversight, emphasizing that employees must remain capable of defending and rectifying AI-assisted outputs. Experts warn that without structural governance, the current trajectory risks capability regression. When workers produce higher volumes of output while experiencing diminished ownership, organizational efficiency masks a decline in human skill development. Industry leaders and researchers agree that sustainable AI integration requires clear corporate policies that recognize AI proficiency as a complementary skill rather than a substitution metric, ensuring human agency remains central to performance evaluation and career advancement.