Automating entry-level tasks removes the repetitive, foundational work that historically served as an apprenticeship. This process, while inefficient, was crucial for junior employees to develop the judgment and pattern recognition needed to become senior experts.

While AI boosts efficiency, over-reliance creates a significant risk of weakening critical thinking and decision-making skills. This is especially dangerous for junior employees, who may use AI as a shortcut and miss the foundational experiences necessary to develop true expertise.

By replacing the foundational, detail-oriented work of junior analysts, AI prevents them from gaining the hands-on experience needed to build sophisticated mental models. This will lead to a future shortage of senior leaders with the deep judgment that only comes from being "in the weeds."

Anticipating that AI will automate baseline work of junior analysts, Temasek’s strategy is to push these employees to develop skills and perform at a level two grades above their current role. This preemptively adapts their talent development model for an AI-enabled world, focusing on higher-order thinking from day one.

By giving junior employees AI agents that "skip steps," companies risk stunting their professional growth. Without learning the foundational principles of a task, they can't develop the context or experience to innovate, troubleshoot, or improve the process, becoming mere tool operators.

Experts develop a "meta-level" understanding by repeatedly performing tedious, manual information-gathering tasks. By automating this foundational work, companies risk denying junior employees the very experience needed to build true expertise and judgment, potentially creating a future leadership and skills gap.

With AI absorbing the foundational research, drafting, and analysis that junior employees once used to build expertise, companies must create new 'apprentice' roles. This model focuses explicitly on developing human judgment, context, and discernment, which become the most valuable skills when execution is automated.

AI can perform tasks done by junior analysts, but this creates a long-term problem. If junior talent doesn't learn by building models and doing "grunt work," they may lack the fundamental skills and judgment needed to become effective senior leaders.

The true risk of AI isn't just automating entry-level tasks, but preventing new workers from developing 'discernment'—the domain-specific expertise to distinguish good output from bad. Without performing foundational tasks, junior employees may never acquire the judgment of a seasoned professional.