Responsible AI Is a Product Responsibility
AI products can create risks around:
<ul>
<li>privacy and data leakage</li>
<li>bias and unfair treatment</li>
<li>unsafe or harmful outputs</li>
<li>copyright and provenance</li>
<li>misuse and unauthorized actions</li>
</ul>
These are not only legal issues. They shape user trust and whether the product should exist in its current form.

What is a primary governance concern for an internal assistant over company documents?

Responsible AI is only a legal or compliance problem after launch, not a product problem during design.

Governance Needs Mechanisms, Not Slogans
Practical governance often includes:
<ul>
<li>clear intended and prohibited use cases</li>
<li>data handling rules</li>
<li>review and approval processes</li>
<li>logging and auditability</li>
<li>red-team testing for failure and misuse</li>
<li>monitoring after launch</li>
</ul>

Which PM practice is strongest for a higher-risk AI feature?

Higher-risk use cases usually justify stricter approval, monitoring, and audit requirements.

Governance Can Increase Adoption
Good governance does not only reduce downside. It can increase adoption by making customers, internal teams, and leadership more confident that the product is safe to use at scale.

Before a broad rollout in a regulated domain, what is the strongest next step?

Good governance can increase adoption because it reduces organizational fear and ambiguity.