Behavior-Driven Development (BDD) has existed since then as a collaborative way of matching the behavior of software to business requirements, but its historical use has suffered due to the slow manual nature of scenario creation, the lack of consistency in the quality of tests, and the significant maintenance costs incurred in large systems. Such constraints make the overall test more unstable as well as hinder engineering productivity. This work discusses the significant way in which Generative AI, specifically Large Language Models (LLM), could be used to supplement the BDD process, in terms of auto-generating Gherkin scenarios, authoring step definitions, optimizing redundant test flows, as well as engaging in predictive maintenance of flaky or stale test cases. Translating the business rules, user stories, and acceptance criteria to executable tests helps minimize the manual effort and enhance the consistency of scenarios and long-term maintainability of the scenario presented by AI. The study aims to estimate the efficiency improvement, decrease manual workloads, and test reliability enhancement in the presence of AI-driven automation integrated into the workflow of BDD. The experiments demonstrate that the creation time of the test is reduced by 40%, reducing ambiguity-related defects by 25%, and by 10%, the test coverage increases, which proves that the quality and performance have significantly improved. These results indicate that enterprises that implement AI-enhanced BDD have a high return on investment (ROI), faster release cycles, increased test stability, and scale to more automation requirements that are viable in current cloud-native and data-heavy environments.