Most organizations know the EU AI Act exists, but very few understand what it actually requires of them. A new compliance analysis found that 78% of enterprises across eight major industries have not taken meaningful steps toward meeting the regulation's obligations, even as enforcement deadlines approach . The problem isn't a lack of awareness,it's a fundamental gap in governance structures that organizations need to build before they can even begin compliance.

What Are the Three Critical Gaps Holding Back AI Compliance?

Vision Compliance, a European regulatory advisory firm, assessed organizations across financial services, healthcare, technology, manufacturing, energy, retail, telecommunications, and transport sectors. The analysis uncovered three consistent gaps that appear regardless of company size or industry :

• No AI System Inventory: 83% of organizations assessed had no formal inventory of the AI systems they use or deploy. Without knowing what AI systems exist in their operations, companies cannot determine which applications fall under the regulation's prohibited, high-risk, limited-risk, or minimal-risk categories.
• Missing Governance Leadership: 74% lacked a designated internal owner or governance body responsible for AI compliance. This means no single person or team is accountable for ensuring the organization meets regulatory requirements.
• No Technical Documentation Process: 61% had no process for generating the technical documentation required for high-risk AI systems, including data governance records, model performance metrics, and human oversight procedures.

These gaps reveal a deeper problem: organizations are treating AI compliance as a technical checklist rather than a governance responsibility.

"Most organizations are aware the AI Act exists, but very few understand what it actually requires of them. The regulation goes well beyond policy statements. It requires organizations to classify every AI system they operate, document how those systems were built and tested, and maintain ongoing human oversight," stated Robert Gelo, Senior Consultant at Vision Compliance.

Robert Gelo, Senior Consultant at Vision Compliance

Why Are GDPR-Compliant Companies Better Positioned?

The analysis found one bright spot: organizations already compliant with the General Data Protection Regulation (GDPR), the EU's data privacy law, were better positioned for AI Act readiness. These companies had stronger foundations in data governance, impact assessments, and documentation practices . However, the AI Act introduces requirements that go beyond data protection, including conformity assessment procedures and post-market monitoring obligations that are entirely new territory for most compliance teams.

This suggests that while GDPR compliance provides a useful starting point, it is not sufficient. Organizations need to build additional governance layers specifically designed for AI systems, which operate differently from traditional data processing.

How to Build an AI Governance Framework in Your Organization

For organizations facing the EU AI Act deadline, compliance requires moving beyond awareness to action. Here are the practical steps organizations should take now:

• Conduct a Complete AI System Audit: Map every AI system currently in use across your organization, including tools used by employees, customer-facing applications, and internal decision-making systems. Document what each system does, what data it uses, and what decisions it influences.
• Appoint an AI Governance Owner: Designate a senior leader or team responsible for AI compliance across the organization. This person or team should have authority to make decisions about which systems are deployed, how they are monitored, and what happens when problems arise.
• Develop Technical Documentation Processes: Create standardized procedures for documenting how AI systems are built, tested, and monitored. This includes maintaining records of training data, model performance metrics, testing results, and human oversight mechanisms.
• Establish Risk Classification Procedures: Develop a process for classifying each AI system according to the regulation's risk categories. This determines what compliance obligations apply to each system.
• Build Ongoing Monitoring Capabilities: Set up systems to continuously monitor AI applications after deployment, tracking performance, identifying potential harms, and documenting corrective actions.

What Does the EU AI Act Actually Require?

The EU AI Act entered into force in August 2024, with enforcement being applied in phases through 2027 . The regulation introduces mandatory requirements for organizations that develop or deploy AI systems within the European Union. These include risk classification, conformity assessments for high-risk applications, transparency obligations, and governance documentation. The phased approach gives organizations time to prepare, but the window is narrowing as enforcement deadlines approach.

The regulation's scope is broad. It applies not just to AI companies, but to any organization using AI systems in the EU, regardless of where the company is headquartered. This means that even organizations outside Europe need to comply if they deploy AI systems that affect EU residents.

Why Governance Matters More Than Technology

The compliance gap revealed in this analysis reflects a broader pattern in how organizations approach AI. Many companies focus on acquiring the latest AI tools and models, assuming that technology adoption equals compliance. But the EU AI Act makes clear that governance is the foundation. Without knowing what AI systems you operate, who is responsible for them, and how they are documented, no amount of advanced technology will help you comply .

This governance-first approach is spreading beyond Europe. Australia's government recently released its National AI Plan, which emphasizes that AI governance requires coordination across government and industry, with clear accountability structures and ongoing engagement with workers and society . Similarly, nonprofit organizations in the United States are recognizing that AI governance is not primarily a technical question but a question of power, accountability, and who gets to make decisions about how AI systems are deployed .

The broader lesson is clear: organizations that treat AI as a governance issue from the start will be better positioned to comply with regulations, manage risks, and ensure that AI systems align with their values and mission. Those that treat it as a technology problem will find themselves scrambling to catch up as enforcement deadlines approach.