“This creates a bigger audit trail that security and compliance teams must review to understand how decisions were made,” Jain added. “It also increases cost and latency, since one question can trigger many model calls. Another challenge is accountability. If something goes wrong, it’s harder to know which part failed, like the generator, the reviewer, or the system managing them.”
Analysts say this will require enterprises to rethink governance frameworks around AI deployment.
“Enterprises must prioritize governance of the model to the output selection process, and the refinement of how multiple responses are blended or selected,” Shah said. “This continuous monitoring and calibration will become a fundamental part of Process Quality Management.” Enterprises will also need structured mechanisms to evaluate outputs and their real-world impact, ensuring traceability across the decision-making process and improving how multi-model systems are managed over time, Shah added.
