The expert named the main mistakes of Russians when discussing salaries at an interview

While looking for a new job, many professionals accept salaries below what they could receive based on their experience and market situation. The reasons are often related to lack of confidence in their own skills, lack of information about the relevance of the profession, and mistakes during negotiations with the employer. Denis Solomatin, head of the Personnel Training and Development Department at Rosgosstrakh Life, told Izvestia about this on August 5.

According to him, even qualified specialists can underestimate themselves. Often, a person compares their achievements with the most successful representatives of the profession and concludes that their competencies are not valuable enough. Another factor is the long—term job search. When income is urgently needed, applicants tend to get offers faster and less often analyze conditions or try to improve them.

"In addition, many people rely on their previous salary and consider it an indicator of their market value. However, the salary level is changing along with the labor market: some specialists are becoming more in demand, and the requirements for other professions may decrease," the expert explained.

He noted that the usual calculation does not always work, when a certain percentage is simply added to the previous salary. This approach does not take into account the real changes in demand for skills.

Mistakes often occur during salary discussions. For example, the candidate may be the first to name the desired amount and thus limit the possibilities for negotiations. If expectations are set too broadly, the employer usually focuses on the lower end of the range.

Also, Solomatin added, experts often estimate only the salary amount, without taking into account bonuses, bonuses, additional payments and social conditions. Because of this, the offer may look less profitable or, conversely, more attractive than it actually is.

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Some formulations during the interview may also indicate the candidate's uncertainty. For example, a link to the previous salary or a request to the employer to independently determine the "market value" of a specialist. Solomatin advises that when discussing income, one should rely not on personal circumstances, but on professional results. It is important to show what tasks the candidate is able to solve and what benefits he can bring to the company.

To determine an adequate salary level, before looking for a job, it is worth exploring the market: compare vacancies, study company offers, talk to recruiters and go through several interviews. It helps to understand the real requirements of employers and the cost of specific competencies.

The expert stressed that the past salary is not always an objective guideline. This is especially true for professionals who have been working in the same organization for a long time and may not notice how the conditions in the industry have changed.

If the employer is not ready to offer the desired amount, this does not always mean that you need to immediately turn down the job. Additional bonuses, salary review after the probation period, or other compensation terms can be discussed. According to the expert, confidence in their own skills and understanding of the market situation help specialists negotiate more calmly and not settle for an income below their capabilities.

Oksana Semenova, director of Financial Institutions at Cornerstone, a career consultant, told Izvestia on July 19 that the use of artificial intelligence in job search had led to changes in the labor market and increased distrust between employers and candidates. According to her, applicants are increasingly faced with doubts about the reliability of their resumes, and employees are faced with the inconsistency of companies' promises with real conditions. The expert cited the spread of generative AI as one of the reasons: according to an internal assessment by Cornerstone consultants, more than 60% of resumes applying to work in 2026 have signs of using neural networks.