Why Every Enterprise Needs an AI Governance Framework

Organizations are continuing to embed artificial intelligence in business operations, from third-party applications to internally developed tools. As adoption grows, so does the need for oversight. Without a defined approach to AI governance, organizations expose themselves to compliance gaps, reputational damage, and operational failures.

Whether you are building AI models in-house or relying on vendor solutions, a consistent governance framework is essential to identify, manage, and address risk across the enterprise.

Internal AI Solutions: More Control, More Risk

Enterprise teams are increasingly developing custom AI solutions to accelerate business outcomes, improve operational efficiency, and gain competitive insights. These internal innovations drive significant value but also introduce governance challenges that require proactive management.

Without structured oversight, internal AI development can create blind spots in your risk profile. Risks include:

• Unintended bias that skews results or reinforces inequalities

• Amplified data quality issues that impact decision-making

• Compliance gaps when models operate outside established frameworks

• Lack of visibility into how AI is being built, deployed, and monitored

  
  

Without effective internal AI governance, organizations cannot:

• Maintain a clear understanding of how AI is being used

• Keep accurate AI inventories across business units

• Ensure alignment with emerging regulatory requirements such as the EU AI Act

  
  

Building powerful AI systems is only part of the equation. Promoting ethical practices and ensuring responsible use must be central to every AI initiative.

Third-Party AI Tools: Accountability Still Falls on You

Using vendor software with embedded AI does not eliminate responsibility. Even when development happens outside your organization, you remain accountable for how these tools perform within your environment.

Most vendors do not provide full transparency into how their AI models are trained or how outputs are generated. That lack of visibility makes it essential to evaluate external AI tools before and after adoption. Establish a standard set of review criteria that includes:

• How data is collected, stored, and secured

• How models are monitored and updated

• How outputs are explained and validated

  
  

The EU AI Act reinforces this shared responsibility. While obligations apply to AI developers and providers, organizations are equally accountable for how systems are deployed and used. You may not control how an external model was built, but you are responsible for monitoring its outcomes and ensuring its use complies with regulatory requirements.

This is not about avoiding AI. It is about using it responsibly and with a clear understanding of your obligations.

AI Governance: From Policy to Program

Effective governance cannot be achieved through ad hoc efforts. As AI expands across the enterprise, organizations need a programmatic approach that establishes process, ownership, and accountability. This requires more than a policy. It requires cross-functional engagement, defined roles, and clear responsibilities.

The goal of AI governance is to reduce uncertainty. With a program in place, organizations can confidently adopt AI, knowing it is being managed responsibly, ethically, and in compliance with applicable laws and regulations.

Learn More

Archer and EY have come together to delivery this insightful webcast on ‘The EU AI Act in Focus: Ecosystem-Wide Strategies for Responsible AI’. The discussion will explore why AI governance must go beyond just meeting EU AI Act requirements and become a core, sustainable process within organizations. This session will foster important conversations for developing an AI governance strategy that adapts and grows with your organization’s needs.

Discover how Archer and EY are helping enterprises use AI responsibly. Watch the webcast here.