If You Don't Know How Much AI Your Company Uses, You Can't Manage It

A recent report's errors, generated with AI, have sparked debate. However, for businesses, the more critical question is whether such mistakes could happen internally. The core issue isn't the use of AI, but the lack of organizational awareness regarding its application.

Companies need to know where AI is deployed, what data it uses, its outputs, and who is responsible for verification. This understanding forms the basis of "AI Governance." Simply having a policy document is insufficient. Organizations must translate these policies into daily practices, specifying which AI models are permissible, for what tasks, what data can be shared, and when human review is mandatory.

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The problem starts when the organization doesn't know where AI is being used, what data is fed into it, what results it generates, and who is responsible for verifying them.

Visibility is key. Businesses should track who uses AI, which models they employ, for what purpose, and the associated costs. Implementing shared security rules and maintaining audit trails for errors or doubts is crucial. The greatest risk isn't AI's occasional mistakes, but an organization's inability to trace the error's origin, identify responsible parties, and determine accountability for the final outcome.

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The greatest risk is not that AI sometimes makes a mistake. The greatest risk is a situation where the organization cannot say how the mistake happened, who should have caught it, and who was responsible for the final result.

To address this, an "AI Governance Suite" has been developed. This managed layer acts as an intermediary between users, organizational applications, and AI models. It enforces security policies, monitors model usage and costs, links AI use to user identity, and creates audit trails. This approach not only ensures safe AI use but also contributes to digital sovereignty by developing local expertise and technologies, reducing dependence on external solutions.

The goal is not to block AI or scrutinize every user prompt. Instead, it's about establishing boundaries that allow employees to use AI efficiently while maintaining organizational control. Responsible AI adoption begins with understanding and control, not just permission.

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AI Governance becomes not only a way to safely use artificial intelligence but also an element of building digital sovereignty – local competencies, know-how, and technologies that reduce dependence on solutions developed outside Poland and Europe.