Introduction

People who are responsible for technology decisions but are not trying to be on the cutting edge. They are trying to keep things running. They are trying to protect their teams. They are trying to avoid being surprised by problems that could have been prevented with a little more foresight.

Over the last year, almost every one of those conversations has included AI.

Sometimes it comes up as excitement. Sometimes it comes up as concern. Most often, it comes up as pressure. A board member asks about it. A funder mentions it. A staff member starts using a tool quietly because it helps them move faster. A vendor pitches it as an add-on that sounds harmless.

What I notice is that very few organizations are against AI. What they are against is unnecessary risk.

That distinction matters.

AI tools are not just another category of software. They behave differently, they evolve faster, and they introduce forms of liability that are easy to miss if you evaluate them the same way you would evaluate a CRM or a file-sharing platform.

My goal here is not to slow you down or make AI feel intimidating. It is to help you see where the real risk lives, so you can make decisions that hold up six months from now, not just this quarter.

Because the hardest problems with AI rarely show up on day one. They show up later, when the tool is already embedded in workflows and the assumptions nobody questioned start to matter.

Why AI Changes the Risk Equation

Traditional software does what it is told. AI systems infer, predict, and generate. That difference sounds subtle until you are accountable for the outcome.

When an AI tool touches your organization, it is not just executing instructions. It is shaping decisions, summarizing information, prioritizing actions, and influencing how people think about their work. Even when it is marketed as “assistive,” it carries weight. People tend to trust outputs that sound confident and coherent, especially when they arrive quickly.

That trust is where risk begins to compound.

AI systems also tend to process more data than teams realize. Prompts, uploads, usage patterns, and background context all become part of how the system functions. In many cases, that data is stored, logged, or reused in ways that are not obvious unless you ask very specific questions.

And unlike traditional software, AI tools are rarely static. Models are updated. Training methods change. Terms of service evolve. A tool you approved under one set of assumptions can quietly shift under another.

None of this makes AI dangerous by default. It makes it different.

And different tools require different scrutiny.

The Liability Nobody Sees at First

When organizations think about AI risk, they usually think about data breaches. That is understandable, but it is incomplete.

The more common issues I see are quieter and harder to detect.

AI tools can create privacy exposure if sensitive information is processed outside the boundaries your policies or regulations allow. They can create intellectual property ambiguity if outputs are not clearly owned or protected. They can introduce compliance drift when automated systems start influencing decisions that fall under regulatory oversight.

They can also create operational dependency. Teams begin to rely on a tool’s outputs without fully understanding how those outputs are generated or how they might fail. When something goes wrong, responsibility becomes blurred. Was it the model, the vendor, the user, or the organization?

From a legal and governance standpoint, that ambiguity matters.

And then there is reputational risk. AI errors tend to surface publicly. A flawed summary, an incorrect recommendation, or an inappropriate automated response can undermine trust faster than a traditional system failure because it feels personal and avoidable.

Most of these problems do not come from bad actors. They come from good teams adopting tools faster than their risk models can adapt.