Across industries, AI governance represents an urgent challenge for C-level executives and senior leaders today. Some of the most common questions I hear right now all lead back to a similar question: How do you manage the AI already used in your organization?
Don’t ask. Let’s say the AI is already in use, with or without your permission. The question is not whether AI is used, but whether it is used well and safely.
The biggest mistake leaders make is to treat AI governance as a future problem when it is already here. Without protocols in place, there is no visibility into how AI is being used or where it might create risks to your brand, privacy, or quality of work.
Your job is to understand how it is used, what tools are in play, and where that use creates risks for your organization.
To get a clear picture of your team’s use of AI:
- Conduct a survey to see which LLMs they use most often in their daily work (ChatGPT, Gemini, Claude, etc.) and their preferences.
- Identify whether specialized AI tools, such as AI agents, are used.
- Assess how comfortable people are with AI. Are people embracing its use, resisting it, or somewhere in between?
- Ask whether they have enough guidance to use AI confidently right now or whether they are largely figuring it out on their own.
What you learn here will help you determine next steps. The more information you have about how your teams actually use these tools, the better positioned you will be to create a governance framework that can spot problems before they escalate.
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You may already have a compliance and privacy issue
Large organizations, especially in regulated industries, can unknowingly expose themselves to significant risks when there is no clear oversight of the use of AI.
Without an AI governance policy, teams could feed private or sensitive information into LLMs whose chat logs could be used for model training, exposing the organization to liability risk for:
- Privacy issues arising from feeding proprietary or customer information into third-party models that train on data.
- Security risks arising from AI tools that have not been evaluated or vetted by security or IT teams.
- Legal exposure resulting from accepting third-party terms that give AI platforms rights to any data input.
- Risks arising from AI tools that maintain conversation history that can be accessed or sued in the event of a breach.
If you’re in a regulated industry and don’t have visibility into what’s being used or what data is being shared, implement a governance policy that puts your organization in control.
While the use of generative AI has grown rapidly in recent years, not all AI tools carry the same risk. An LLM chatbot that uses your data to train models carries a very different risk than an enterprise-grade AI tool with guaranteed privacy protection.
With a clear list of approved tools, your team can reduce exposure to risks with serious consequences. Address:
- Which tools meet compliance, legal, or security standards.
- Which platforms are authorized for daily use.
- Which tools can be used in limited or specific use cases.
- Which tools and platforms are not permitted under any circumstances.
- Whether subscription plans or free tiers are allowed.
- How tools are approved and which teams are responsible.
This is especially important if your organization operates in a regulated industry, where compliance standards around data management, privacy and security are more stringent.
Create clear barriers around data and privacy
Without explicit guidelines, people will make their own judgments about what is safe to share with AI tools, and those decisions may not always be correct. This lack of awareness creates human risks and exposes your organization to unnecessary data privacy breaches and security vulnerabilities.
Your data and privacy protections should cover:
- Which tools can be used with internal documents and sensitive data and which cannot.
- What categories of information are not allowed in any prompt, such as PII, internal documents, customer data, or financial information.
- How to manage confidential supplier or partner information.
- Requirements for anonymizing data before using artificial intelligence to analyze it.
- Compliance regulations specific to your industry, such as GDPR.
AI governance policies should clearly document these guidelines in a way that is easy to understand and practical to apply. For example, a one-page infographic is easier to remember than a 50-page policy that’s too dense to read.
Build a QA process before scaling up production
Another often overlooked risk is quality degradation, resulting from the assumption that AI can produce content at scale with little human oversight. When AI is used to produce content in large volumes without a quality control process in place, quality can degrade as production exceeds the ability to maintain brand standards.
Before resizing anything, define:
- The review process for all AI-generated content.
- What types of content require heavier editorial oversight versus lighter editing.
- What it looks like is pretty good.
- Who has final approval authority.
- Brand voice, tone and messaging guidelines for generated content.
- How ownership of quality issues is handled.
AI can be a powerful tool, but without a quality assurance protocol in place, the quality of output can quickly deteriorate and erode trust with stakeholders.
Create an AI governance policy that evolves with your organization
Establishing an AI governance policy should not be a one-off process. The space is evolving too quickly for rigid protocols. As tool functionality and usage evolve, use cases may expand and contract. As long as AI tools are in use, governance policy will need to be reviewed. Leaders writing policy will need to remain flexible and keep up with the pace of change.
To help governance policies evolve over time:
- Start a feedback process where employees can ask questions, share new tools, and discuss using AI.
- Schedule periodic reviews to check approved tools, update guardrails, and evaluate what works.
- Strengthen the good use of AI and work to mitigate poor use.
Don’t wait to build guardrails
An AI governance policy does not have to be complicated or dense, but it must exist. Start with how AI is already being used and understand how it is applied. Define what tools are allowed and not allowed, what the use cases should look like, and how to maintain quality standards when AI is part of content production.
Review your policy on a quarterly, semi-annual or annual basis to ensure teams have up-to-date guidance on using these tools safely and effectively.
