Airport airside taxi delays significantly impact airlines, passengers, and the environment. Departure Metering (DM) is an effective approach to contain taxi delays by controlling departure pushback timings. In this work, we demonstrate the potential of Deep Reinforcement Learning (DRL) based DM method to reduce taxi delays by effectively transferring delays from taxiways to gates. This work casts the DM problem in a markov decision process framework to train a DM policy over simulations generated using historical airport surface movement data. We further develop an Intelligent Departure Metering Assistant Tool (I-MATE) that employs the trained DM policy to recommend pushback advisories to Air Traffic Controller (ATCO). We conducted validation experiments to assess the efficacy and acceptability of I-MATE in assisting ATCOs to manage airside traffic. The results reveal a significant reduction in taxi delays (25.6\%) with increased compliance with I-MATE recommendations, which may translate to improved efficiency, cost savings for airlines, and enhanced passenger experience. While increased compliance reduced taxi delays, a slight decrease in runway throughput (3.2\%) was also observed. This suggests a potential trade-off between optimizing runway usage and minimizing delays. The study also reveals a spectrum of compliance among ATCOs, influenced by factors like experience and age. Qualitative feedback indicates high user satisfaction with I-MATE, suggesting its usefulness, reliability, and trustworthiness. This research underscores the value of AI-based decision support systems for air traffic control, thereby paving the way for further advancements in airside traffic management.