A customer wants its AI governance to scale smoothly as it adds more teams and more models. What should a consultant recommend it establish first?
Select an answer to reveal the explanation.
Short Explanation
You want governance to scale without chaos, so define ownership, policies, workflows, and evidence requirements once and apply them the same way to every team and model. That beats hiring more people to chase inconsistent work.
Full Explanation
The right recommendation is to establish defined ownership, policies, workflows, and evidence requirements that apply the same way regardless of which team or model is involved. That structure lets new teams and models be onboarded consistently without governance effort growing linearly with headcount.
A larger governance team hired in proportion to the number of new models is wrong because simply adding headcount does not fix inconsistent process; it just applies more people to inconsistent work.
A rule that only the most experienced team may build generative models is wrong because restricting who may build models does not itself create ownership, policy, or workflow definitions.
A freeze on new model development until existing models are fully documented is wrong because freezing development is disruptive and does not address the underlying lack of structure.
For the exam, recommend defining the governance structure itself as the scalable fix, not adding people or restricting work.