Programmatic Advertising Optimization Using AI Bidding Strategies

Abstract

This article examines how artificial intelligence is used to optimize bidding strategies in programmatic advertising and real time bidding environments, focusing on the tension between short term performance gains and broader marketing and consumer outcomes. The study conducts a systematic literature review of peer reviewed journal articles published between 2016 and 2021, asking which AI and optimization methods are applied to bidding, which objectives and constraints they address, and what impacts they report. Across the reviewed studies, profit oriented models, reinforcement learning policies, control based pacing, and advanced click or conversion prediction generally outperform rule-based bidding on efficiency metrics such as clicks, conversions, and return on ad spend, but often under narrow objectives and proprietary data settings. The article discusses these results by grouping studies according to algorithmic approach and optimization focus, and by contrasting technical findings with evidence on media quality, privacy concerns, and brand outcomes. The main conclusion is that future work should integrate multi objective optimization and consumer centric evaluation into AI bidding research and practice.