Human Judgment Over Algorithms: Dane Tang on Avoiding Self-Replacement in the Age of AI

In a recent LinkedIn post, Dane Tang explores the subtle yet significant ways leaders might be replacing their own judgment with artificial intelligence, even before AI fully takes the reins. Tang uses a personal hiring anecdote to illustrate his central argument: while AI is adept at processing data, it lacks the nuanced human capacity for discernment that is crucial for leading fragile teams.

Tang recounts a hiring decision made after a challenging reorganisation. He explains that the choice came down to two candidates: one with impressive credentials and polished interview answers, and another with fewer qualifications but a more grounded presence. Despite HR’s preference for the data-driven candidate, Tang opted for the latter, Alan, believing he was better suited to support a team in a vulnerable state.

We don’t need another poster boy or model answer right now. We need someone who can hold people together while we find our footing.

As Tang elaborates, this decision proved correct, with team engagement and sales improving over the following year. He posits that a hiring algorithm would likely have chosen the candidate with the more quantifiable track record, highlighting a key distinction between data processing and human intuition.

The Limits of Algorithmic Decision-Making

Tang argues that AI, by its nature, is limited to processing the data it is fed. This limitation becomes critical when leaders face situations that require more than just data analysis.

AI can only process data. It cannot sense what a fragile team needs. It cannot read a room.

According to Tang, these are uniquely human capabilities. He emphasizes that AI cannot understand the unstated needs of individuals or the overall atmosphere of a team, elements that are vital for effective leadership, particularly during times of uncertainty.

When Leaders Replace Their Own Judgment

The core of Tang’s message is a warning against a form of self-replacement. He suggests that when leaders abdicate their own instinct and judgment in favour of data or algorithmic recommendations, they are effectively diminishing their own role and effectiveness.

When we stop trusting our instinct about who to promote, who to listen to, what our team actually needs right now, we are not waiting for AI to replace us. We’ve already done it.

Tang identifies this reliance on quantifiable metrics over intuitive understanding as the true risk, not the advancement of AI itself. He proposes a simple yet powerful practice for leaders to counteract this tendency: pausing before significant decisions to ask two critical questions.

Cultivating Human Judgment

To foster the kind of judgment that AI cannot replicate, Tang suggests a brief but impactful reflection process.

The Two-Question Framework

Tang encourages leaders to take approximately thirty seconds before making important decisions to consider:

1. What does this situation need that the data can’t see?
2. What would I regret not noticing here?

He asserts that this practice moves leaders from merely finding the ‘right answer’ based on data to making the ‘right call’ that considers the human element. This ability, Tang notes, is not an innate skill but one that is cultivated over time through consistent attention and experience.

Judgement like that isn’t something you switch on. It’s built over years of paying attention.

Tang concludes by prompting readers to reflect on their own decision-making processes, challenging them to consider when they last made a call that transcended data, and whether that call proved to be the right one. He frames this continuous exercise of human judgment as essential for authentic leadership in an increasingly data-driven world.