The integration of Artificial Intelligence (AI) in the software development industry has experienced a notable increase, with the emergence of Large Language Models (LLM). The adoption of LLM-Based Coding Assistance Tools (LBCAT) is still limited in the Software Maintenance (SM) field and the literature focuses on the initial stages of software development, showing inconclusive results. In this work, we identify categories of SM that can be supported by LBCAT and explore the critical factors that influence their use. We employed a mixed-method approach, beginning with a qualitative analysis using content analysis to categorize software developers’ perspectives on the applicability of LBCAT in SM. In this initial stage, we identified five potential dimensions. In the second stage, we conducted a survey to evaluate which of these dimensions contributed to better performance in SM: (1) Organizational Context and Business Strategy, (2) Innovation and technological evolution, (3) Security and Testing, (4) Technical aspects of maintenance, and (5) Human Factors Management. With these dimensions, we conducted a survey where 55% of developers use AI tools in their additive maintenance tasks. Of this percentage, 68% reported being more productive using LBCAT. These results lay the groundwork for identifying applicability factors of LBCAT that suggest levels of AI intervention to maximize their value in the SM process.