Companies of all sizes are considering the implementation of artificial intelligence (AI) to optimize their organizational structures, including reducing the number of middle management positions. According to Gartner, by the end of the year, 20% of organizations may cut more than half of their middle managers.

However, completely eliminating the middle management layer may be an excessive measure. A more effective approach is to retain middle management as a key link between leadership and employees, provided these specialists acquire the necessary AI skills to achieve better business results in the new technological era.

New Competency Requirements for Middle Management

Modern managers are no longer just executors or technical specialists. Their main task is to organize processes and ensure business goals are met. To do this, they need:

• Understanding AI capabilities and limitations. Managers must be familiar with existing AI tools and know how to apply them in their industry. For example, in the financial sector, AI can automate tax reporting preparation, but it is not always suitable for complex audit tasks. In manufacturing, AI helps predict maintenance needs but does not replace inspections.

• Developing business acumen. It is essential to identify where AI can deliver the greatest value: increasing revenue, reducing costs, boosting productivity, retaining clients, or accelerating time-to-market. A clear understanding of business objectives should precede the implementation of AI for process automation or solving specific tasks.

• Ensuring quality control of AI-driven results. Managers must make sure employees remain responsible for the accuracy and quality of outcomes achieved with AI. It is unacceptable to justify mistakes by blaming the AI system. This approach fosters a culture of accountability when using new technologies.

• Prompt engineering skills. To obtain relevant and useful results from AI, it is important to know how to formulate queries and adjust parameters correctly. For instance, instead of a general question about the best clients, it is better to specify criteria such as region, revenue level, or activity over a certain period. Iterative work with prompts allows for more precise data.

The Evolving Role of Middle Management

In the era of AI adoption, managers must be able to manage not only people but also processes. It is crucial to analyze key performance indicators (KPIs), assess AI potential, and integrate it into workflows. Leadership should guide and support the team to ensure AI delivers the expected results.

This transformation is especially relevant as the gap between organizational needs and current outcomes becomes increasingly apparent. Research shows that many companies struggle to turn AI investments into significant business impact. The reason often lies not only in technical aspects but also in the need to rethink job roles and adapt corporate culture. Middle management is precisely the group that can put these changes into practice.

When managers possess skills in technology understanding, process restructuring, and mentoring, AI becomes not just a matter of implementation but a strategic asset for the company.

As a result, the focus shifts from the question “What should we do with AI?” to “What goals do we want to achieve with AI?”, which aligns with the demands of modern organizations.