Customer complaints on online platforms significantly impact consumer decision-making and brand perception. As businesses grapple with the volume of feedback, generative artificial intelligence (GAI) powered by large language models (LLMs) presents exciting opportunities for automating personalized responses and enhancing customer service experiences. However, integrating LLMs requires addressing potential limitations, such as factual errors, incoherence, and maintaining authenticity. This study investigates the effectiveness of GAI in responding to online customer complaints, examining its qualitative distinguishability from human-authored responses, and the moderating effects of review length and service provider ratings. Findings contribute to the discourse on GAI's ability to simulate empathy and understanding, offering practical insights for professionals considering GAI adoption in complaint management. Thoughtful navigation of GAI's potential and limitations is crucial for businesses seeking to leverage its power while safeguarding human-centered interactions.