AI Skills and Promotions: The Gap Between Using AI and Earning Trust

In a November 10 Human Resources Director column, HiBob executive Anna Volkova says 74% of Australian organizations covered by the company’s research link AI skills to promotion criteria, while 24% provide training for managers to assess and coach AI use. [1]

I think any employer making AI proficiency a condition of advancement owes workers a clear account of what earns credit. Otherwise, employees could spend months learning tools without understanding whether their supervisor considers assisted work evidence of initiative or dependence.

What earns the promotion?

HiBob’s September 22 sponsored article in Charter describes a multinational survey of 1,200 business leaders, each involved in an organizational AI decision during the previous year. It reports that 67% linked AI skills to promotion criteria. [2]

Both articles present the vendor’s research. They provide reported practices, without establishing how actual promotions were decided. The Australian and multinational percentages also concern different populations; they cannot establish a change over time. [1] [2]

The training figure alone cannot tell us whether managers are competent evaluators. Nevertheless, I think an employer needs a credible assessment method before using a skill to decide someone’s advancement.

The career question is concrete: what behavior earns recognition? An employer might value speed, careful verification or the ability to work independently. Those expectations can coexist, but employees need to understand how they fit together before a review. A general instruction to become proficient leaves too much unresolved.

Trust can turn on how the work was done

In a November 9 research summary, American Friends of the Hebrew University describes an experiment in which participants acting as supervisors chose whether to accept an employee’s reported task time or check it. The reported probability of trust was 68% for independent work and 24% for AI-assisted work. Participants were not assessing the quality of the finished output. [3]

Those figures concern a particular verification decision. Other experiments linked lower trust with poorer performance and promotion-suitability ratings. The summary does not establish actual missed promotions. [3]

The absence of a quality assessment matters. These results cannot tell us whether the assisted work was better, worse or equivalent. My concern is that the method used could become a shortcut for judging the worker, before their contribution receives a proper examination.

I consider it reasonable for a manager to ask whether someone understands a conclusion well enough to defend it. Someone seeking greater responsibility should expect that scrutiny. But I want understanding assessed directly. Knowing that software helped is insufficient, in my view, to establish whether the person can exercise judgment.

The trust research and HiBob’s survey concern different populations. Together, they suggest a possible conflict between adoption expectations and managerial perceptions. They do not prove that the same employers systematically reward and penalize identical behavior. [1] [2] [3]

Encouragement needs to reach the reviewer

AFHU reports that explicit organizational encouragement reduced the experimental trust penalty until it was no longer statistically significant. That does not establish a zero effect or guarantee a remedy in actual workplaces. [3]

This is the most hopeful finding to me. Expectations surrounding AI assistance may be changeable. An employer can explain why it wants a tool used and what responsibility remains with the person using it.

I would expect managers to work through shared examples before evaluating employees. They should compare what they consider strong reasoning, adequate checking and readiness for greater responsibility. If their assessments differ sharply, that disagreement needs resolving before it affects someone’s career.

An announcement encouraging experimentation would not satisfy me. The people deciding advancement need to agree on what successful use demonstrates about the employee. Otherwise, a worker could meet the company’s adoption expectations while failing an unwritten test of personal effort. That is a risk I infer, rather than an outcome these sources document.

Judgment includes choosing when to leave AI out

Consider a hypothetical operations employee who uses AI to draft a supplier comparison, discovers unsupported claims and checks the final version against company records. A colleague completes an equally accurate comparison independently because checking a generated draft would take longer.

I would assess the quality, total effort and reasoning behind both approaches. The first employee deserves credit for effective verification. The second should be able to earn credit for choosing an efficient method. Counting how often each opened an AI tool would tell us little about which person is ready for a more demanding role.

Justified nonuse belongs in AI competence. That is my position, rather than a finding tested by this research. It should require an explanation tied to the task, just as using AI should. Neither enthusiasm nor reluctance demonstrates sound judgment by itself.

This matters for career development because the next useful skill depends on the responsibility ahead. Recognizing an unreliable recommendation, explaining a tradeoff and deciding when assistance adds value could all deserve recognition. An evaluation standard that counts only visible tool use would miss those contributions.

Clarify the standard before choosing the next skill

For someone planning a promotion, my advice is to bring one recurring responsibility into the next development conversation. Ask your manager to describe excellent assisted work, what you must still understand independently, and how a reasoned decision to avoid AI would be assessed.

That conversation can direct your learning. If the next role requires diagnosing errors and defending decisions, another introductory tool course may contribute less than practice in those responsibilities. If expectations remain vague, treat that uncertainty as part of evaluating your prospects with the employer. More tool use cannot reliably resolve an undefined standard.

This is a career-planning recommendation, not a proven strategy for securing promotion. The sources do not establish that discussing or disclosing AI use protects trust.

My prognosis is conditional. Clear expectations could let AI competence become credible evidence of readiness for advancement. Continued ambiguity could make promotion depend more on managerial preference, even when workers improve their skills. That would be a serious failure of management. Employees deserve to know how doing better work will count toward a better career.