I was reading that 43% of companies are planning to hire “AI governance” roles in 2025.
But when I asked people what “AI governance” actually means, nobody could give me a straight answer.
Is it compliance? Is it making sure models are fair? Is it preventing hallucinations? Is it security? Is it… something else?
Everyone I talked to said “we need governance” but they all meant something different.
I think this is because AI governance is actually really hard to define.
Traditional software has governance. You have code reviews. You have testing. You have deployment pipelines. You have rollback procedures.
But AI is different.
What do you govern? The model? The training data? The output? All three?
How do you know if a model is “governed properly”? You can test it. But AI models fail in weird ways. You can test for bias on one axis and miss bias on another axis.
How do you audit an AI decision? With traditional software, you can trace through the code. With AI, you can’t. The model is a black box.
So when companies say “we need AI governance,” I think they actually mean “we’re concerned about AI and we’re hiring someone to be concerned about it professionally.”
Which is… not nothing. But it’s also not a comprehensive approach to AI governance.
I think 2026 will be when this becomes obvious. When companies realize that hiring a Chief AI Officer doesn’t automatically make AI safe or compliant.
The real governance will come from:
- Better tooling for monitoring models
- Regulatory standards that actually mean something
- Insurance and liability frameworks
- Industry standards and certifications
Not from hiring one person to be worried about it.
The companies that figure out systematic AI governance (not just hiring someone) will have a huge advantage.
Everyone else will be vulnerable to the first AI failure that causes real damage.
And it’s coming. Someone’s AI model is going to make a big, public mistake. And then every company will scramble to actually implement governance, not just hire someone to oversee it.