Build your AI governance framework: Practical first steps

By Theron Kelso

AI is becoming increasingly embedded in core operations as organizations accelerate their AI adoption and look toward AI agents. Yet many are moving forward without a defined approach for managing how AI is used.

A formal AI governance framework provides rules that can guide AI use across your organization. When implemented well, governance enables more effective AI adoption. Without it, organizations not only increase risk but also limit the potential of their investments. 

Below is a practical AI governance checklist to help you establish a framework that fits your organization.

Why you need stronger AI governance 

Effective governance provides staff with clarity on appropriate AI use, helping reduce risk and avoid redundancy or overspend. It’s also a powerful tool for supporting the necessary organizational changes and finding new ways to add AI into core processes. 

Here’s how AI governance can support your organization: 

• Defines clear boundaries for AI use: AI can either help or harm an organization depending on how it’s applied. Clear guidance helps teams understand what is and is not acceptable use. For example, you can encourage team members to experiment with ways AI can reduce repetitive work, while emphasizing the risks of using AI-generated content without human review.  
• Supports AI education: Governance creates a vehicle to educate teams and keep them current on topics such as responsible use, bias considerations and emerging regulatory expectations. 
• Mitigates AI-related risk: AI adoption isn’t risk-free. Establishing clear guidelines can help limit exposure related to data privacy, biased outputs or unchecked hallucinations. 
• Manages AI investment: With a growing number of AI tools entering the market, governance helps establish approval and evaluation processes, so organizations avoid duplicative investments or uncontrolled development of AI solutions.
• Supports organizational change: AI adoption often introduces a significant shift in the way people work. A governance structure helps ensure changes are communicated clearly and that employees understand how they can apply new tools.
• Highlights improvement opportunities: As governance frameworks are developed, organizations often surface additional areas where AI could improve efficiency or effectiveness. 

Building your checklist: What are your AI governance pillars? 

Most AI governance frameworks are built around a set of core pillars that define expectations and oversight, such as privacy, fairness and risk. 

The following pillars provide a useful starting point for shaping your approach.

Bias management 

AI systems can reflect bias in their training data. Your organization should evaluate what bias risks exist within your tools, particularly those trained on limited or specialized datasets.

Reliability  

AI outputs are only as dependable as the inputs and processes behind them. Regularly assess data quality and monitor results to help ensure output remains accurate and trustworthy.

Privacy 

Public and third-party AI solutions can introduce significant data privacy and security concerns. Controls should be put in place to prevent confidential or regulated data from being shared with systems that are not authorized to receive it.

Transparency 

Do you understand how AI is being used and how AI models work in your organization? Transparency standards can help ensure these critical considerations are reviewed regularly.

Accountability 

Using AI without human oversight or accountability can create serious compliance, bias and reputational risks. Your framework should include accountability guardrails to help keep usage aligned with your policies. 

Inclusiveness

Beyond avoiding biased outcomes, organizations should consider whether their AI tools are inclusive. This may include selecting models trained on diverse datasets and ensuring accessibility for users across the organization.

Compliance

Regulatory oversight of AI continues to expand, particularly in heavily regulated industries. Organizations should identify applicable requirements and make sure they can maintain and demonstrate compliance. 

Third-party risk 

Vendors and partners that use AI may expose your organization to additional AI risks. A third-party provider may handle sensitive data differently, creating downstream privacy or security concerns that need to be assessed and managed.

Intellectual property 

AI comes with extensive intellectual property (IP) and copyright questions, many of which remain unresolved. Be aware that AI usage that pulls from trademarked or copyrighted IP could expose your organization to liability. 

Sustainability

With AI models relying on energy-intensive data centers, some organizations may want to factor environmental impact into their decisions about how AI tools are used. 

How to create your governance framework 

Developing an AI governance framework starts with establishing a diverse governance team, deciding how you want your staff to use AI, identifying your governance pillars and writing a formal AI policy. In some cases, organizations may also work with external advisors to help assess AI opportunities and design policies to support those efforts. 

Here’s an overview of the key steps involved in building an effective framework:  

1. Establish your governance team 

AI governance requires leadership sponsorship. Governance leadership teams typically include CFOs, CIOs and COOs, although larger organizations can delegate some of those responsibilities to a dedicated governance committee or working group. 

When setting up your team, be sure to include a diverse set of perspectives so that you can look at AI considerations from across your organization. 

2. Build a common knowledge base 

All members of the governance team should share a baseline understanding of AI concepts, including different AI tools (such as generative AI or AI agents), recent advancements and recognized limitations (such as hallucinations or data dependency).

3. Define appropriate AI use 

Define how AI should and should not be used within your organization. This includes identifying acceptable use cases (such as task automation) alongside excluded uses (such as replacing human judgment or publishing unreviewed AI-generated content).

4. Decide which governance pillars fit your needs

Your team should select the governance pillars that reflect your organization’s needs. Account for your AI dos and don’ts, industry regulations and other considerations such as bias or environmental impact. 

5. Involve diverse stakeholders

While governance leadership may be centralized, input from across the organization is essential. Ongoing communication with additional stakeholders helps build trust, surface risks and ensure policies are practical for real-world work. 

6. Identify new processes 

Governance for AI initiatives is also an essential step. You’ll likely need to develop AI tool approval processes that clearly define how decisions are made and who is responsible. 

7. Determine which AI tools are acceptable 

Specify which tools are approved and which are prohibited. For example, an organization may allow secure enterprise platforms while restricting tools that lack sufficient data protection controls.

8. Write your formal AI policy

Document your governance decisions in a formal AI use policy. This may involve drafting a new document aligned with your chosen pillars and guidelines, or you can adapt a sample policy. 

Keep your governance ongoing 

Creating an effective framework isn’t the end of AI governance. It’s the beginning. 

As AI capabilities and organizational priorities evolve, governance practices must be revisited and refined. Maintaining an active governance team allows you to regularly assess AI use and policies and stay ready to take advantage of the latest AI advancements.

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