How a lack of AI training can harm strategic decision-making in companies

Artificial intelligence already influences how companies organize tasks, analyze information, and plan operations. However, to take advantage of these opportunities, leaders need to understand how the technology works and what changes it requires in team routines. Without this knowledge, decisions about investments and priorities may be disconnected from the business’s actual needs.

A 2025 study by the Organisation for Economic Co-operation and Development (OECD) found that the supply of AI training may still be insufficient to meet the demand for basic knowledge about the technology.

The challenge of the knowledge gap in senior management

The so-called skills gap—an English term for a competency gap—can make it difficult to choose projects and set realistic expectations. Without familiarity with AI’s capabilities and limitations, executives risk approving initiatives without clear goals or expecting tools to solve problems that require process changes.

The challenge extends to people management. If the board encourages technology use but doesn’t provide guidance, training, or rules for handling confidential data, adoption tends to be uneven. Some employees may move forward on their own, while other teams remain unsure how to incorporate these tools into their work.

Essential skills for leaders in the age of AI

The first step is to understand the basics of generative AI, experiment with applications relevant to one’s own field, and learn to verify the answers produced by these tools.

Analytical thinking and data interpretation help evaluate information before making decisions. Knowledge of privacy, security, and governance enables the establishment of limits for corporate use.

Communication and change management, meanwhile, are essential for explaining goals, preparing teams, and monitoring the adoption of new processes.

Training should align with the challenges of each role. A marketing director might explore applications for campaign research and analysis, while a financial leader could study tools for organizing documents and supporting information interpretation.

From training to concrete results: a practical approach

One way to start is by selecting a specific problem and testing an AI application on a small scale. Before the experiment, the team should define the expected outcome, quality criteria, and precautions regarding the data used. Afterward, they need to compare the results with the previous process and identify necessary adjustments.

Training should also be continuous. New tools emerge, companies’ needs change, and knowledge gained from a training session may require updating.

For senior management, the urgency lies in developing sufficient expertise to ask the right questions, prioritize investments, and guide teams. The advantage doesn’t come from adopting every new innovation, but from knowing where AI can contribute and how to monitor its impact on the business.