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While scientific workflows have been established and used in a number of disciplines for specifying and executing experiments and data analysis, early and recent studies have demonstrated that an important proportion of workflows suffer from decay. This phenomena is exacerbated by legacy scientific workflow systems, notably Taverna, which was popular in e-science for orchestrating complex analyses. A step towards addressing this issue, we report on in this paper a feasibility study on using generative AI to revive decayed workflows, combining large language models with modern workflow technologies. Our approach automates critical revival tasks including parsing of legacy Taverna workflows, failure point identification, repair suggestion, and conversion to contemporary formats, viz. SnakeMake. The methodology integrates AI-driven workflow summarization, pseudocode abstraction, graph-based visualization, automated service substitution, and code generation. We demonstrate and evaluate this approach through a real-world decayed workflow case study. We conclude the paper with a discussion on key lessons that we learned and will guide development of a systematic workflow revival framework as part of our future work.