This research paper evaluates the operational integration, performance efficiencies, and primary structural implementation barriers of Artificial Intelligence (AI) within the contemporary digital marketing ecosystem. As corporate enterprises face unprecedented market saturation and fluctuating data compliance laws, legacy digital marketing methodologies fail to deliver consistent scalability. Adopting a mixed-methods research design utilizing purposive sampling (N = 60), this study gathers primary empirical data from digital marketing professionals alongside qualitative global corporate frameworks. The quantitative evaluation employs a Weighted Mean Score matrix on a 5-point Likert Scale to analyze key performance indicators such as Return on Ad Spend (ROAS) and Customer Acquisition Costs (CAC). The empirical findings indicate that while AI acts as a significant operational efficiency multiplier in automated workflow execution (Mean Score: 4.37) and algorithmic programmatic media buying (Mean Score: 4.20), it faces substantial friction points regarding independent creative autonomy (Mean Score: 3.07). The paper identifies technical talent deficits, data fragmentation silos, and high enterprise software licensing costs as primary organizational barriers. Ultimately, the study concludes that the future scope of AI over the next decade relies on the transition toward autonomous multi-modal marketing agents and privacy-first data handling systems, requiring comprehensive structural updates across marketing agencies and academic management curriculums.