Clare Kitching on linkedin

tgroenwals shared this post · Apr 18

The fastest way to understand AI governance?
Stop thinking about data. Start thinking about decisions.

There's a lot of noise around governance in the age of AI.
And leaders cut through some of that noise by understanding the split between inputs and outcomes.

Data governance protects the inputs.
It’s about accuracy, privacy, lineage, access and compliance.
It answers: Can we trust the data going into our systems?

AI governance protects the outcomes.
It deals with fairness, explainability, drift and human oversight.
It answers: Can we trust the decisions made on outputs of AI systems?

Both matter.
Both are essential.
But they serve different purposes.

What I’ve seen inside organisations this year is that AI governance doesn’t sit neatly with one owner.
Legal, risk, product, ethics, engineering and the business all hold a piece.

That’s normal, because the outcomes affect so many parts of the organisation.

The companies making real progress connect the two:
They build strong data governance first. Clean foundations mean AI systems fail predictably, not catastrophically.

Then they layer AI governance on top as the mechanism that gives business leaders confidence to actually use what's been built.

Have a look at the two side by side to see where each plays a role.

What's your experience? Where does AI governance live in your organisation, and is it working?

♻️ Repost to help someone get their governance layers set.
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Ferdinand Macagba This is a useful distinction because a lot of governance conversations still get stuck too low in the stack.

Data governance tells you whether the system was fed responsibly. AI governance tells you whether the system is allowed to influence action responsibly. Once leaders see that split clearly, it gets easier to understand why good data alone does not create safe decisions.

The harder question is usually not “is the data clean?” It is “who is accountable for the outcome if the model is wrong?” Apr 18 1 like

Selva Kumar The ownership problem is the one we run into every time. Legal, risk, engineering all hold a piece but nobody wants to be the accountable party when an autonomous system makes a decision that goes wrong.

With agentic deployments this isn’t abstract. The agent is already acting. The question of who owns the outcome needs to be answered before deployment, not after the first incident. Apr 18 1 like