As artificial intelligence increasingly mediates decisions in public administration, finance, security, and regulatory enforcement, accountability gaps emerge where responsibility becomes diffused across socio-technical systems. This article examines how AI-assisted governance reshapes decision authority, often creating the illusion of neutral delegation while obscuring answerability. By analysing automation bias, institutional over-reliance on algorithmic recommendations, and the erosion of clear responsibility chains, the article argues that accountability in AI-assisted governance cannot be reduced to compliance roles or technical controls. Instead, accountability must be treated as a reconstructible governance process, capable of tracing decisions across data, models, human interventions, and institutional incentives. Drawing on contemporary AI governance frameworks and policy-oriented research, the analysis highlights the distinction between governing of AI systems and governing with AI systems, showing how automated outputs increasingly participate in institutional decision-making. Without explicit responsibility architectures, such as traceable decision paths, justification requirements, and contestability mechanisms, automation risks accelerating not only efficiency, but also normative fragility and democratic accountability loss. The article concludes that effective AI-assisted governance depends on preserving human judgment, institutional responsibility, and decision legitimacy, positioning accountability as a structural prerequisite rather than an afterthought in automated governance environments.