Many organizations are questioning whether to act now on AI governance or wait for final clarity on enforcement dates, particularly with the EU AI Act. The proposed delays in enforcement have introduced hesitation, as organizations are uncertain about the final requirements and timelines. However, the underlying governance expectations are not going to change. Developing a quality management system (QMS) for high-risk AI is a process that requires slow and steady work. Evidence must be accumulated, roles must mature, and cross-functional routines need to be established. None of these foundational elements can be rushed in the final months before an enforcement deadline. 

Although the consequences may feel distant and abstract, this blog outlines the risks of inaction and the tangible benefits of starting early. 

Understanding the High-Risk AI QMS Standard 

The High-Risk AI QMS Standard, part of the EU AI Act, demands structured, repeatable, and risk-based practices across the entire AI lifecycle. It requires clear documentation of decisions, complete traceability from data to model to deployment, and a controlled workflow. This controlled flow ensures that all reviews, evaluations, approvals, and monitoring activities leave a clear, auditable trail. These are fundamental management responsibilities, not simply technical add-ons. You cannot meet these rigorous expectations with last-minute documentation or a single, frantic compliance sprint. You meet them by building consistent habits, which only form when governance and engineering teams work together long before any regulation takes effect. 

Why waiting is a flawed strategy 

When leaders hear about a proposed regulatory delay, they often assume they have gained time. In reality, the workload remains constant. The only thing that changes is the cost and pressure of completing it. 

Waiting to establish AI governance creates three predictable problems: 

1. Lack of evidence for regulators and customers 

Imagine a financial services firm using a credit decision model across multiple markets. A supervisor requests the model’s evaluation record, but the team can only produce a single performance chart with no version history, no justification for the dataset used, and no record of who approved its deployment. The risk officer is now facing a regulatory issue that cannot be fixed retroactively. This scenario will become common for unprepared organizations. 

2. Lost revenue from procurement failures 

By 2026, large buyers in regulated industries will require their vendors to provide an AI system inventory, documented controls, and a clear governance narrative. A health tech firm, for example, might be disqualified from a bid because it cannot demonstrate that its diagnostic models were developed under a controlled process. A competitor that invested in governance earlier will win those contracts. 

3. Technical teams hitting a maturity wall 

Engineers who have never operated under a controlled development regime need time to adjust. If you introduce process discipline and documentation requirements late, teams will likely push back. This resistance can slow down delivery at the exact moment when compliance pressure is at its peak. These failures are not hypothetical; they follow the same pattern seen in every other regulated domain. Organizations that wait inevitably end up with rushed documentation, repeated rework, and expensive remediation projects. 

Delivering value before enforcement deadlines

Executives often ask about the immediate business case for investing in AI governance. The benefits arrive long before any regulatory deadline. 

• Faster procurement cycles. Complete enterprise procurement questionnaires more efficiently. 
• Higher investor trust. Address board-level questions about AI exposure with confidence. 
• Better regulatory preparation. Be ready for questions from regulators before formal supervision begins. 
• Stronger engineering discipline. Improve system reliability and reduce unplanned incidents. 
• A compelling narrative. Position your company as a prepared and responsible leader, not a reactive follower. 

These benefits are not tied to an enforcement date; they are directly linked to the maturity of your management system. 

How ISO 42001 provides a foundation 

ISO 42001 provides the essential foundation for this work, serving as a blueprint for responsible and scalable AI compliance across organizations. The standard requires organizations to define their context, roles, risks, and controls, ensuring a structured approach to AI governance. It also mandates performance measurement and a commitment to continuous improvement, enabling organizations to build trust and demonstrate ethical AI practices. 

The High-Risk AI QMS Standard builds directly on this structure. Think of ISO 42001 as the scaffolding for your AI management system. The High-Risk AI QMS Standard then defines the specific operating procedures for those systems that carry the most significant risk. Together, they form a comprehensive system of control. Neither can be implemented effectively if introduced late in the game. 

What your organization should do now 

A strong start doesn’t require a massive, complex program. It begins with clarity and ownership. 

Your 90-Day plan 

First, focus on creating a solid foundation. 

• Create a provisional AI system inventory. List all the AI systems currently in use or development. 
• Classify AI systems by risk. Pinpoint two or three systems that are likely to qualify as high-risk under upcoming regulations. 
• Assign ownership. Appoint a single, accountable executive for each of these high-risk systems. 
• Implement change control. Establish a basic process for managing model updates. 
• Create a minimum record set. Start documenting data decisions, evaluation choices, and deployment approvals to ensure traceability. 

This initial work provides the groundwork needed to align with both ISO 42001 and the High-Risk AI QMS Standard. 

Your 12-Month plan 

After the first 90 days, you can expand these initial efforts into a fully functional AI management program. 

• Formalize governance. Develop and approve official policies and governance charters. 
• Build cross-functional workflows. Create integrated processes for risk assessment, model evaluation, and approvals involving all relevant teams. 
• Train your teams. Educate engineering, product, and risk teams on documentation discipline and lifecycle control. 
• Strengthen supplier oversight. Develop processes for managing third-party risks from foundation models, hosted services, and data pipelines. 
• Conduct a mock assessment. Run a full internal audit against ISO 42001 and the High-Risk AI QMS Standard to identify gaps. 
• Mature your processes. Use the findings from your assessment to improve monitoring, incident response, and performance measurement. 

This structured approach creates a living governance environment that can be audited with confidence. 

While ISO 42001 is an ideal first step toward holistic AI compliance, not every organization may feel ready to pursue a full certification. For those seeking more tailored or incremental approaches, there are options to address specific needs: 

• AI Model Audit: For organizations needing focused assurance on a specific AI product, a model audit offers independent validation of its performance, testing, and system-level controls. It is a faster, more targeted attestation that demonstrates due diligence without the complexity of a full certification.   
• HITRUST AI: For organizations in healthcare and other sectors handling sensitive data, HITRUST offers AI-specific assessments and certifications. These add-ons help validate that security controls and processes are tailored to protect data within an AI environment. 

The leadership decision 

Many organizations believe they can delay action on AI governance, but this approach will inevitably lead to rushed audits, lost deals, and unnecessary compliance costs. By starting now, leaders can distribute the workload over a manageable timeline, building competence and confidence instead of scrambling under pressure. Organizations that act early will be ready to meet regulatory standards with evidence that naturally emerges from their daily operations.  

Deadlines may shift, but expectations will not. Success will belong to those who prepare steadily and proactively.