Generative AI is transforming how we work, but it also presents a paradox: While increasing the demand for judgment, it simultaneously erodes the experiences that build it. This article explores the challenges and implications of this trend, particularly for junior employees, and offers insights on how organizations can navigate this new landscape.
When I began experimenting with generative AI a few years ago, I observed a significant difference in how it benefited me compared to my less-experienced colleagues. I found that AI accelerated my work in familiar areas like research synthesis and analysis, providing directionally accurate results that were easily refined. However, newer analysts experienced a different outcome.
While they produced output quickly, the quality wasn't significantly improved, and they struggled to evaluate its merit or guide it effectively. Some even faced difficulty in getting started. This contradicted my initial expectation that generative AI would empower less-experienced workers, acting as an instant capability boost. Instead, I witnessed the tools amplifying existing judgment rather than compensating for its absence. This observation illuminates a broader unease among managers across various industries: AI-assisted work, though fast and polished, is challenging to assess for quality, making it difficult to decide whether to act on it. This is understandable when examining the process of achieving good results with AI tools, which involves continuous evaluation and iterative refinement. Users prompt the AI, assess the output, and then re-prompt it with course corrections. Historically, this type of judgment is not a direct product of using AI but rather a result of experience, built through repetition, responsibility, and learning from mistakes. This leads to a paradox: AI simultaneously increases the demand for judgment while eroding the experiences that cultivate it. Experts like Amy Edmondson and Tomas Chamorro-Premuzic have cautioned about the risks of eliminating entry-level roles entirely with AI. This article explores the implications when junior roles remain, but their developmental value undergoes a fundamental shift. To clarify, let's define what I mean by judgment. Judgment is a trait leaders frequently invoke but rarely define explicitly. It's often a critical factor for promotion and becomes increasingly vital at higher levels. I've experienced both sides: needing to demonstrate judgment to advance and later evaluating it in others. Judgment can be defined as the ability to act wisely in situations where rules are inadequate, by identifying key factors, weighing priorities, anticipating consequences, and taking ownership of decisions under uncertainty. This definition implies that leaders often bundle several distinct forms of judgment together. In practice, judgment manifests in at least five ways: Evaluative judgment, the ability to recognize whether something is good or bad, strong or weak, appropriate or inappropriate. Contextual judgment, understanding when general rules apply and when exceptions are necessary. Tradeoff judgment, weighing competing objectives when there's no clear right answer. Anticipatory judgment, seeing second-order consequences before they occur. Ownership judgment, deciding when to take personal responsibility for a decision and its risks. Consider a new product launch as an example. Two leaders reviewing the same data, recognizing the same risks and potential benefits, might still reach different conclusions. The difference lies in judgment. One leader might see supply chain risks as manageable and believe a delay would be detrimental, while the other might be more concerned about potential damage to brand trust. Neither is wrong; they are weighing trade-offs differently based on their experience. These forms of judgment are typically not taught directly but emerge from the structure of work. In most organizations, judgment arises as a byproduct of how work is structured. For example, junior consultants in professional services firms are often responsible for tasks like research, analysis, model-building, and communication preparation. The iterative process of these tasks, along with the responsibility for their outcomes, cultivates judgment over time. The development of judgment occurs through a process I call the judgment cycle. This cycle has four stages: First, an individual is assigned a task. Second, they grapple with it, using whatever tools are available. Third, they receive feedback on their output. Fourth, they reflect on that feedback, consider what worked and what didn't, and adjust their approach for the next task. This cycle repeats throughout a career, with each iteration refining judgment. In a pre-AI world, this cycle was fundamental to how organizations developed talent and built robust institutions. The junior consultant who has painstakingly assembled a model is more likely to question AI-generated models thoroughly, more likely to spot errors, and more likely to ask insightful questions. They also know what it feels like to make the mistakes that an AI might make. The rise of AI disrupts this cycle in several ways. The most immediate is that the tasks traditionally assigned to junior employees now become the domain of the AI. Rather than building the model, the junior consultant prompts the AI to build it. The time and cognitive effort required to complete the task are reduced. The AI often performs the 'grappling' stage, and the feedback the junior employee receives is the output of the AI itself. This leaves the junior employee in a difficult position. They now have an incomplete understanding of the processes and the potential for the AI to make errors. They lack a sense of how the output was produced and have less insight to evaluate it effectively. The 'grappling' is outsourced to the AI. Feedback on the AI's output is also less informative than feedback on an employee's work product. And the AI rarely if ever provides the kind of iterative learning loops that lead to improved judgment. The risk is that employees receive less experience and fewer opportunities to learn from their mistakes. The quality of judgment declines at the very moment when good judgment is most needed. This is not to say that AI has no place in the development of junior employees. It can be a powerful tool for accelerating the learning process, but it should be used judiciously. The best approach is not to eliminate junior roles but to redesign them to include elements of the judgment cycle. This means giving junior employees tasks that still require them to grapple with the underlying concepts, allowing them to make mistakes, and providing them with meaningful feedback. It also means using AI as a tool to support, not replace, their work. For example, instead of asking AI to create an entire model, junior employees could be assigned to refine a partially completed model, identify and correct errors, or test the model's assumptions. The goal is to create learning experiences that develop judgment and prepare junior employees for success. The organizations that embrace this approach will be the ones that thrive in the age of AI. They will be able to attract and retain the best talent, and they will be able to make better decisions. They will also be better prepared to navigate the challenges of the future. The shift towards AI-assisted work requires a deliberate effort to protect and cultivate judgment. This requires a re-evaluation of how tasks are assigned, how feedback is given, and how learning is structured. It is about creating environments where employees can develop the skills they need to succeed in the age of AI. Organizations need to balance the efficiency gains of AI with the need to develop the judgment of their employees. It's a critical balancing act that will determine their future success
AI Judgment Generative AI Workplace Employee Development Leadership Decision-Making Skill Development
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