AI governance checklist: A guide to managing AI risk by establishing governance
- AI governance is the process of establishing rules to guide how your organization uses AI, with the goal of helping to spur innovation and protect against risks.
- An effective AI governance framework involves creating a list of policy pillars to govern areas like privacy and security, bias, compliance, transparency, reliability and oversight.
- Create a diverse AI governance team that may include leaders like your CFO, CIO and COO to develop your AI policies and provide ongoing oversight.
As organizations race to embrace AI agents, AI technology is more deeply embedded within core processes than ever. But too many companies still lack a formal AI governance framework.
AI governance is essentially a set of rules that govern how your organization uses AI. A clear governance framework can help you more effectively implement AI, while a lack of governance limits your ability to make the most of AI technology while exposing you to risk.
Keep reading to learn more about how to successfully establish AI governance within your business. This article also includes a downloadable checklist to guide your own governance efforts.
Why do companies need a strong AI governance framework?
A strong AI governance framework helps your team members understand how they should and should not use AI. Governance also helps reduce your AI-related risks, avoid redundancies or overspending on AI tools, facilitates organizational change and creates opportunities to think about how you can add AI into your core processes.
- Lays out dos and don’ts: Depending on how it’s used, AI can either help or harm your organization. Your team needs to know what constitutes acceptable use and what doesn’t. For example, you can clarify that you encourage team members to experiment with how AI can help reduce repetitive tasks while emphasizing that you don’t want to follow in the footsteps of one nonprofit that inadvertently angered donors by creating and sharing AI-generated images of a disaster.
- Educates your team: AI tools are rapidly changing. Governance offers a vehicle to educate your team on how AI is evolving, as well as about bias risks, government regulatory guidelines and more.
- Reduces AI risks: AI is not a risk-free tool. But by setting clear guidelines for your team, you can reduce or mitigate certain risks like data privacy issues, bias or using AI-generated content without checking for hallucinations.
- Manages your AI tool inventory: The number of AI tools on the market is exploding. By adopting a formal governance framework, you can create an approval process for adopting AI tools, making it less likely you’ll pay for redundant options or develop unneeded AI agents without oversight.
- Supports change management: There’s a big change management piece to AI adoption. A governance structure helps you establish a process to communicate around AI-related changes inside your business to make sure your team has the chance to talk about what’s happening and knows how to use new AI tools as you add them.
- Bolster organizational improvements: As you go through the effort of establishing an AI governance framework, you will naturally have the opportunity to identify areas to explore whether AI could help your organization operate more efficiently.
Creating an AI governance framework: Which pillars make sense for your business?
An AI governance framework typically involves developing policies and oversight structures around key pillars like fairness, reliability, privacy and risk. Your organization may not need to include every pillar in your own governance framework (and you may also create some not covered here), but consider this list a starting place:
Fairness and bias management
Depending on their training data, AI models can be at risk for showing bias towards certain groups based on characteristics like ethnicity or gender. Assess whether this could be a problem with AI tools you use, especially if those tools are trained on smaller datasets.
Reliability and validity
The reliability of AI outputs depends heavily on the quality of the inputs. Consider whether you are feeding clean, organized data into your AI tools and regularly evaluate the outputs for accuracy.
Privacy and security
Public AI models, especially, can represent major data privacy or security risks. Establish controls to make sure that sensitive or protected information doesn’t end up getting shared with AI systems that shouldn’t have access to it.
Transparency and explainability
Do you understand how your AI models work and are being used within your organization? Transparency and explainability standards make sure these questions are regularly considered.
Accountability and oversight
AI used without any human accountability or oversight can lead to serious risks in areas like bias, compliance and reputation. Set up guardrails to help ensure any AI use within your business is done in accordance with your governance framework and policies.
Inclusiveness
In addition to avoiding bias, are your AI tools actively inclusive? Depending on the type of AI you are using, this may involve using models trained on diverse datasets that take into account the wide range of the human experience, as well as making sure your tools are broadly accessible to users within your business.
Legal and regulatory compliance
Legal and compliance requirements around AI use will only continue to grow, especially in regulated industries. Make sure your organization understands the specific rules that affect AI use within your sector and takes ongoing action to help ensure you maintain (and can demonstrate) compliance.
Vendor and third-party risk
If your business is using AI, your vendors and other third parties you may share information with almost certainly are as well. This can be a significant data privacy and security risk area, especially in regulated industries like financial services, where customer information that is held securely by a financial institution may be shared with a fintech partner that doesn’t have the same safeguards in place.
Acceptable IP use
AI raises huge copyright and IP questions, many of which are unresolved. Be aware that AI usage that pulls from copyrighted or trademarked intellectual property could create liability for your business — even if done unintentionally.
Sustainability
AI tools are powered by resource-hungry data centers. Think about whether you want to consider your AI usage from a sustainability standpoint and take action like limiting AI for certain tasks or buying carbon offsets.
Again, you don’t have to use every pillar included on this list when creating your own AI governance framework. You may also wish to develop original pillars of your own. But a good governance structure will include at least some of the elements you see here.
Follow this AI governance checklist to establish your own oversight process
To create an AI governance structure that fits your organizational needs, you’ll need to assemble a diverse governance team or working group. This group should consider dos and don’ts for how you want your business to use AI, choose governance pillars, think about how AI will affect your processes and write a formal AI policy.
You may benefit from working with a third-party advisory firm here, as an advisor can help you think more deeply about how your organization could use AI most effectively and develop policies to support that effort.
Here are key steps to follow when establishing AI governance:
1. Choose your AI governance team
You’ll need leaders within your organization to develop and champion AI governance. This typically includes your CFO, CIO and COO, although larger organizations might delegate some of the responsibilities to an AI governance body or working group.
A diverse set of perspectives is really valuable here, as you want people who can help think about how to implement in different parts of your organization.
2. Level set for AI knowledge
Make sure everyone on your governance team understands AI, including the different types of AI like generative AI or agents, how the technology has evolved over the last few years and challenges or limitations like hallucinations. You want everyone working from the same common knowledge base.
3. Talk about AI dos and don’ts
Within your governance team, think about your AI dos and don’ts. What do you want to use AI for — and what do you not want it used for?
For example, your do’s might include automating certain highly repeatable tasks or helping speed up the creation of sales proposals, while your don’ts could include using AI to replace person-to-person relationships or writing completed blog posts for your website.
4. Decide which AI governance pillars fit your needs
Within your governance team, create a list of your AI governance pillars (like the examples provided in the previous section of this article). When building this list, account for your dos and don’ts, the compliance requirements of your industry and other important factors like bias, security or sustainability.
Look at the big picture. Think about both AI horror stories and success stories. How do you want to innovate here?
5. Collaborate with other stakeholders
Your AI governance process should be centered around your governance team or working group, but you should also be sure to bring other stakeholders within your organization into the conversation. Again, a wide, diverse range of perspectives is incredibly valuable here, as is open communication between your governance team and the rest of your organization.
6. Talk about any new processes you may need
In addition to governance pillars, you’ll likely also need to develop specific processes around areas like AI tool approval. Who signs off on implementing a new AI tool your team is interested in trying?
7. Clarify which AI tools are acceptable to use
Which specific tools do you want your team to use — and which you don’t want them to use? Clarify that, for example, your team is allowed to use Microsoft Copilot but not DeepSeek, as you’ve put data privacy safeguards in place with the former but not for the latter.
8. Write your formal AI policy
Pull together the work done by your governance team into a formal AI use policy. This can be as simple as downloading a sample policy and adjusting the details to fit the governance pillars and guidelines you’ve identified, but you can also write your own policy from scratch.
H2: How to use this AI governance checklist
Download a PDF version of this AI governance checklist to use as a planning document. Share it with colleagues, adjust based on your organizational requirements and make it your own.
Download the AI governance checklist
Effective AI governance is an ongoing effort
Creating an AI governance framework should be the beginning, not the end, of your governance work. AI will continue to change and so will your organization’s needs. Maintain an ongoing governance team that meets regularly to discuss how your organization is using AI, assess your existing policies and make changes if necessary.
How Wipfli can help
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