The real leadership challenge is not automation itself, but rebuilding the human proving grounds that help people become capable, purposeful, and necessary.
AI is gradually replacing jobs and positions across industries as leaders strive to make their operations more efficient. While it is true that AI can automate certain activities as effectively as its human counterparts, leadership strategists like Karen A. Gilhooly argue that this way must be done in ways that preserve the human experiences that create judgment, engagement, and purpose.
When AI Threatens More Than Jobs
Gilhooly notes that, for many people, work has been the main proof that they are needed. This isn’t glamorous, but it’s tangible—something to build a livelihood on.
When AI can perform the tasks that people do faster and cheaper, there is a risk that people lose the structure, identity, and sense of usefulness that work once gave them. According to Gilhooly, most organizations are not prepared to have their employees face those losses, putting them in a position where implementing policies designed to streamline operations could put real people at risk of losing the feeling of being needed.
How Efficiency Can Erase Development
Efficiency is often seen as a nearly universal positive, allowing people and companies to do more in less time for fewer resources. Gilhooly posits that important lessons can be lost in the pursuit of efficiency, however.
In many industries, repetitive work often served as a training ground. Reports, manual reviews, processing tasks, entry-level responsibilities: these were the tasks that helped people build judgment, pattern recognition, confidence, and professional instinct. As Gilhooly herself puts it, “AI isn’t just eliminating tasks. It is eliminating the career proving grounds where professionals historically learned how work actually gets done.”
When those aforementioned experiences disappear, organizations may lose the pipeline that creates future leaders, stymying long-term progress in the process.
The Processes Involved in Creating Real Judgment
AI programs are primarily designed to come off as “yes-men.” This programming ignores most operational realities, namely those that state that nobody can be 100% confident or correct all the time.
In other words, AI has not had to live with or learn from its mistakes in a meaningful way. “Human judgment is formed through responsibility, error, reflection, and accountability, qualities that most AI programs have in short supply. This lack of lived experience means AI hasn’t developed the instincts that come from learning through failure,” Gilhooly says.
Meeting People Where They Are Emotionally
Effective leadership is never wholly a matter of logistical or operational expertise; rather, good leaders know how to tell when employees are feeling engaged and, if they are not, how to motivate them when possible.
Gilhooly frames this decision-making process as a “three buckets” dilemma that identifies employees as being in a want-to state, autopilot, or have-to state. She explains that leaders should protect motivated people, reengage those on autopilot, and recognize that they cannot force someone out of deep resentment or disengagement. She also notes that AI, being devoid of emotion, has little to no role in these interactions whatsoever.
Building Meaning Intentionally
As AI removes tasks, leaders can no longer rely on work itself to create structure and purpose. They must instead deliberately create learning, ownership, reflection, and connection so people understand why their work matters and how they remain necessary.
According to Gilhooly, “Every business problem is a people problem. Always. The loan portfolio that underperforms, the branch numbers that are soft, the compliance audit that came back ugly—trace any of them back far enough and you hit a human decision, a broken communication, or a person who was never given a reason to care.”
This description highlights the urgency of ensuring workforces are not hollowed out before AI becomes even more prevalent. If the people and teams overseeing AI feel that their roles are meaningless, their work will reflect that, no matter how efficient a given AI program may be.
To remedy this, Gilhooly recommends that leaders rebuild the proving grounds employees need to determine who they are and hone their instincts. More importantly, leaders should find ways to answer why their employees’ efforts matter. These are among the most challenging questions a leader can work through, but if they do not answer them, the automation will.
Written in partnership with Tom White
