When Optimization Replaces Knowing (www.aivojournal.org)

Enterprises are rapidly advancing Generative Engine Optimization (GEO) and Answer Engine Optimization (AEO) to enhance their visibility in AI-generated outputs across various sectors such as finance and healthcare. However, a significant issue has emerged: many organizations are conflating optimization with governance, believing that improving AI output consistency and sentiment reduces enterprise risk. This misunderstanding risks increasing exposure without ensuring proper knowledge control, as current optimization metrics fail to capture critical aspects of AI operations, such as the retrievability and accuracy of the information provided. The article argues that while GEO and AEO improve visibility, they do not guarantee evidentiary support for the claims made by AI systems. Effective governance requires the ability to trace and reconstruct AI outputs within their contextual framework, which many enterprises currently lack. For effective risk management, companies must establish a robust control layer that addresses these gaps, ensuring that optimization efforts are grounded in a framework that allows for thorough auditing and accountability. Without this alignment, organizations may amplify narratives they cannot substantiate, potentially leading to significant liabilities in decision-making situations.