EMARKETER recently published its “Field Guide to AI-Powered Programmatic Platforms,” created in partnership with MiQ. It examines how AI is enhancing programmatic advertising platforms and offers marketers a guide to choosing between these adtech tools. This FAQ explores key questions from the report.
Q: How are marketers using AI for programmatic advertising?
A: Sixty-one percent of brand and agency marketers worldwide use AI for programmatic advertising, according to October 2024 Digiday data cited in the EMARKETER report. Among these users, 77% employ AI for campaign management automation, 61% for customer journey personalization, and 61% for programmatic advertising. Yet only 30% of ad industry professionals have fully scaled AI across their media campaign cycles, according to January 2025 Interactive Advertising Bureau data. This gap creates advantages for organizations that achieve comprehensive implementation before their competitors.
Q: Why do marketers need AI for programmatic advertising?
A: Programmatic advertising has become incredibly complex. Marketers juggle multiple buying platforms, data sources, and measurement tools. AI handles this complexity by processing huge amounts of data points per second to adjust bids, find audiences, and place ads.
Q: Why are marketers adopting AI-powered programmatic platforms?
A: Privacy regulations and signal loss are straining traditional targeting methods, forcing marketers toward AI-enabled approaches like contextual AI, first-party data modeling, and predictive audience creation without personal identifiers. Performance demands are rising. Marketers must lower costs and increase returns. AI platforms have become easier to use. Solutions no longer require months-long integrations. Ready-made DSP integrations and natural language interfaces make adoption accessible to non-technical marketers. And conversational AI lets marketers type commands like "find my best audiences" instead of clicking through complex menus.
Q: What can AI-powered programmatic ad platforms do?
A: Features and capabilities include intelligent campaign orchestration, enabling AI to serve as a command center, dynamically directing spend, and adjusting tactics across multiple DSPs in real-time. Predictive audience creation uses machine learning to build high-performance audiences by analyzing behavioral, contextual, and first-party data signals. Real-time, goal-based optimization sees platforms optimize toward specific business objectives. Finally, they explain their decisions. You see why tools choose certain bids or audiences through clear dashboards.
Q: How do AI-powered programmatic ad platforms handle creative optimization?
A: AI platforms a range of possible ad variations at once. They identify which creative works for which audience, then scale winners instantly. Some platforms generate new creative elements. Others personalize existing ads for each viewer. The result: fresher ads that perform better without constant manual testing.
Q: What are examples of AI-powered programmatic advertising platforms?
A: Broadly, there are two categories. Standalone AI optimization tools like MiQ, Scibids, Albert AI, and Cognativ specialize in campaign optimization using advanced machine learning to improve performance across multiple DSPs. Meanwhile, traditional buying platforms with built-in AI include Amazon DSP Performance+, Google Display & Video 360, The Trade Desk's Kokai, RTB House, StackAdapt, Viant, and Basis Technologies. These leverage proprietary data sets to automate bidding, targeting, and creative optimization within single ecosystems.
Q: How should marketers choose an AI-powered programmatic ad platform?
A: First, define clear performance goals aligned with business KPIs. Jason Carmel, VML's global creative data lead, warns in the report against vendors distracting marketers "into optimizing proprietary or proxy KPIs that don't impact your actual bottom line." Second, consider ease of use through natural language interfaces, intuitive dashboards, and comprehensive support. Third, prioritize transparency. Some 60% of US ad industry professionals cite AI accuracy and transparency concerns as significant adoption barriers, according to January 2025 IAB data. Fourth, evaluate data scale and quality. Examine the platform's ability to unify contextual signals, behavioral data, and privacy-compliant first-party inputs.
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