Artificial intelligence is already part of how many businesses operate, whether they planned for it or not. Tools that summarize information, generate content, analyze data, and interact with customers can save time and improve efficiency. Used well, AI can absolutely support growth.
Used carelessly, it can create real problems.
What I’m seeing more often is not resistance to AI, but adoption without guardrails. Employees experiment with tools on their own. Vendors roll out new features faster than companies can evaluate them. Sensitive data gets uploaded or shared without anyone stopping to ask whether it should be.
And in many cases, there’s no formal policy guiding any of it.
That gap matters more than most owners realize. AI is no longer limited to drafting emails or marketing copy. Newer tools can make decisions, trigger actions, and operate with minimal human input. While many business leaders are comfortable trusting these systems with internal data, customers are far less confident and regulators are paying close attention.
This creates a trust issue, but it also creates a financial and compliance issue.
An AI governance policy doesn’t need to be complicated, but it does need to be intentional. At its core, it’s a written framework that defines how your company uses AI responsibly and where the limits are. It clarifies who is accountable, what data can be used, how decisions are reviewed, and how risks are managed.
This isn’t something one department should handle alone. Leadership, IT, and outside advisors all need a seat at the table. From there, the work becomes practical.
Start by taking inventory. Many companies are surprised by how many AI tools are already in use once they slow down and look. Marketing platforms, hiring software, customer support tools, financial reporting systems all of these may rely on AI in some form. Understanding what’s being used, by whom, and with what data is the foundation of any policy.
From there, ownership matters. Someone needs responsibility for oversight, whether that’s a small internal committee or a designated compliance lead. Without clear ownership, policies tend to exist only on paper.
Your policy should also reflect your values. Fairness, transparency, privacy, and accountability aren’t abstract ideas they influence how data is handled, how decisions are made, and how mistakes are addressed. These principles should guide both internal use and vendor relationships, especially when third party tools are involved.
One point I emphasize with clients is the importance of human oversight. AI can assist, but it shouldn’t operate unchecked. Financial reports, customer communications, hiring decisions, and strategic recommendations all require human review. Judgment still matters, and accountability can’t be outsourced to a system.
Because the technology is evolving quickly, governance can’t be static. Regular reviews at least annually are essential. New tools, new regulations, and new risks emerge faster than many policies are updated.
Finally, none of this works if employees don’t understand it. Training, clear communication, and documented acknowledgment help set expectations and reduce misuse. Just as important, employees should feel comfortable asking questions or raising concerns before small issues turn into bigger ones.
From a financial perspective, AI decisions don’t exist in a vacuum. Costs, tax implications, data exposure, and compliance risks all affect the bottom line. Before investing heavily or scaling usage, it’s worth stepping back to understand not just what AI can do, but what it could cost if something goes wrong.
Thoughtful AI governance isn’t about slowing innovation. It’s about protecting the business while allowing it to move forward with clarity and control.
