Generative Information Retrieval (GenIR) signifies an advancement in Information Retrieval (IR). GenIR encourages more sophisticated, conversational responses to search queries by integrating generative models and chat-like interfaces. However, this approach retains core principles of traditional IR and conversational information seeking, illustrating its capacity to augment current IR frameworks. In this chapter, we propose that introducing GenIR enhances traditional information retrieval tasks and expands their scope. This allows systems to manage more complex queries, including generative, critiquing, and extractive tasks. These advancements surpass traditional systems, handling queries with greater depth and flexibility. This sometimes speculative chapter suggests Generative Information Access (GenIA), a term that more accurately encapsulates the widened scope and enhanced functionalities of GenIR, particularly in how this relates to tasks. By investigating the impact of GenIR, this discussion aims to reiterate that generative research should not abandon traditional interactive information retrieval research but rather incorporate it into future research and development efforts.