Why AI Should Take Hours, Not the First Step on the Career Ladder

In a recent LinkedIn post, Yonathan Cohen examines where AI can support a business career and where it should leave room for human experience. Drawing on the 15 Nike roles listed on CEO Elliott Hill’s LinkedIn profile, Cohen describes asking an AI employee called Viktor to assess how it could contribute at each stage. This article reports on Cohen’s post and analysis; it was not written by Cohen.

“This is the level where I could do the most and probably should do the least.”

That response, Cohen writes, came when Viktor considered the sales internship Hill held in 1988. Cohen frames the distinction as a question of how companies introduce AI: automating an entry-level role may reduce routine work, but it could also remove an early rung on the career ladder.

AI’s strongest fit: repeatable sales work

According to Cohen, Viktor identified the sales representative role as one where it could help most. The examples in the post are practical: prepare a brief before each store visit, send same-day follow-ups, update the customer relationship management system and recommend which accounts to call each Monday.

“every Monday: which accounts to call, and why.”

As Cohen presents it, these tasks take time but rely on information that can be organized and surfaced consistently. The value lies in preparing a salesperson to act, rather than replacing the customer-facing work that builds commercial judgment.

Digital data makes executive support easier

Cohen also describes Viktor as useful in a former vice president of U.S. commerce role, where daily monitoring of website traffic, sales and stock-outs could trigger Slack alerts when a metric falls. In another example, the AI would standardize weekly country-by-country sales briefs for a regional leadership role.

Preparation has limits

The post distinguishes those information-heavy duties from managing people. Cohen says Viktor could prepare a Monday team report and one-on-one meetings for a sales manager, but rated that role among its weaker fits because “this is where the job becomes people, not customers.”

At the CEO level, Cohen says, Viktor could assemble a morning brief, help prepare for earnings calls and track follow-ups. But the post draws a boundary around what that assistance can accomplish:

“I can make his hours better prepared. I can’t give him more hours.”

The career ladder remains part of the argument

Cohen’s central point is that AI can absorb tasks across seniority levels, but organizations should consider what happens when the work assigned to beginners disappears. He notes that Hill retired in 2020 and returned to Nike in 2024, when the company brought him back to address a slump. Cohen uses that career arc to emphasize the value of experience and judgment that an AI tool cannot replicate.

His conclusion is direct: “AI should take the hours. Not the first step of the ladder.” The post was published in partnership with Viktor and includes a product offer; its broader argument, however, is about balancing efficiency with the entry-level experiences that help people develop into leaders.