Artificial intelligence is reshaping how advertisers plan, execute, and optimize media campaigns. From automated bidding tools offered by Google, Meta, and Amazon to emerging agentic AI systems designed for end-to-end campaign automation, AI is becoming embedded at every stage of the media buying process. Nearly 60% of US ad buyers have used or plan to use AI-powered buying products, according to an August 2024 EMARKETER survey.

But adoption comes with trade-offs: Transparency concerns, data security risks, and questions about how much control to cede to algorithms.

What is AI-powered media buying?

AI-powered media buying uses machine learning and automation to plan, purchase, and optimize advertising placements across digital channels. Instead of manual audience targeting, bid management, and creative selection, AI systems evaluate hundreds of campaign inputs in real time to make data-driven decisions.

These tools range from platform-native products like Meta's Advantage+ Shopping Campaigns and Google's Performance Max to open web solutions like The Trade Desk's Koa and Criteo's Commerce Max. The technology acts as a "force multiplier" for agencies, enabling them to surface insights without manual data analysis, per AdLib CEO Mike Hauptman. AI also lowers the barrier for smaller advertisers who previously struggled to access high-impact channels.

How are major ad platforms using AI to automate buying?

Many of the largest advertising platforms have launched AI-powered buying products that automate targeting, creative, and optimization in a single workflow, per EMARKETER. The timeline reflects rapid proliferation:

• Google Performance Max launched in November 2021, the first mover in comprehensive AI buying
• Meta Advantage+ Shopping Campaigns followed in 2022
• Amazon Performance+ and Pinterest Performance+ launched in 2024
• TikTok Smart+ and LinkedIn Accelerate formally launched in October 2024

Open web platforms have followed suit. Taboola's Maximize Conversions, Criteo's Commerce Max, and The Trade Desk's Koa automate bidding and creative optimization while giving buyers the final say on transactions. These products helped platforms offset lost revenues from privacy-related signal loss. Advertisers appreciate improved performance but complain about limited transparency.

What are the key barriers to AI adoption in media buying?

Despite growing usage, adoption barriers remain substantial. Among US ad industry professionals, 62% cite complexity of setup and maintenance as a key challenge when adopting AI in media campaigns, according to EMARKETER. Additional barriers from the same survey:

• 62% express concerns over data security risks
• 61% report insufficient understanding of AI technology
• 60% worry about lack of transparency in decision-making

These challenges create a paradox: AI promises efficiency gains, but the tools require technical sophistication and organizational readiness that many teams lack. "Handing everything over [to AI] and just saying 'go' puts the burden on the brand and the advertiser to make it work," said AdLib CEO Mike Hauptman in an EMARKETER interview. "There's a lot of value to be extracted, but that future is much further down the line."

How is AI changing the role of media buyers and agencies?

AI is shifting media buyers from manual executors to strategic overseers.

"The things that used to take a week now take five minutes, if that," said Hauptman. Tasks like data analysis, anomaly detection, and performance reporting are increasingly automated, freeing buyers to focus on strategy and creative direction.

Agencies are repositioning accordingly. 81.3% of senior agency professionals worldwide anticipate AI will shape the next decade of digital advertising, per a March 2025 survey cited by EMARKETER. This suggests the competitive advantage will shift toward teams that interpret, direct, and quality-check AI outputs rather than those with the deepest manual buying expertise.

What is agentic AI and how will it affect programmatic advertising?

Agentic AI refers to systems capable of independent decision-making, goal-oriented behavior, and complex task execution without continuous human input. In programmatic advertising, agentic AI represents the next evolution beyond current genAI tools, eventually automating end-to-end campaign workflows, per EMARKETER.

New infrastructure protocols are accelerating this shift. The Unified Context Protocol (UCP), Advertising Context Protocol (AdCP), and Agentic RTB Framework (ARTF) give AI agents a shared framework to communicate and collaborate. Early pilots are underway: Butler/Till is testing a media activation agent with Scope3, targeting a 40% cost reduction in media plan execution, per Digiday. Full automation remains unlikely in 2026, but performance reporting and customer journey operations will be among the first processes to become fully automated.

How are agency holding companies investing in AI for media?

The three largest holding companies are racing to build AI capabilities through acquisitions and partnerships:

• Publicis Groupe acquired content intelligence firm AdgeAI in March 2026 for predictive measurement and data-management company Lotame in 2025 to expand its CoreAI platform.
• Omnicom completed its acquisition of IPG in late 2025, becoming the largest holding company by revenue. Enterprise genAI sits at the core of its value proposition, with all teams accessing the Omni intelligence platform.
• WPP acquired clean room provider InfoSum and signed a five-year deal with Google AI to power its Open Intelligence platform.

This consolidation reflects a strategic bet that AI-driven predictive capabilities will differentiate agencies as clients increasingly consider in-housing and self-serve platforms.

What transparency and brand safety concerns does AI media buying raise?

AI media buying creates a tension between performance gains and advertiser control. Walled garden tools like Advantage+ and Performance Max deliver improved results but limit visibility into targeting decisions and inventory selection. Brands have complained about this lack of transparency and the few learnings they take away from AI-managed campaigns, per EMARKETER.

The risks are concrete. Some 70% of marketers have encountered at least one AI-related incident in their advertising, including hallucinations, biased content, or off-brand material, per the Interactive Advertising Bureaur (IAB). Some 40% had to pause or pull ads due to AI-related problems. Yet governance lags: only 6% believe current safeguards are sufficient, and 14% report no one at their organization owns AI governance. The IAB launched its first AI Transparency and Disclosure Framework in January 2026 to address these gaps.

How should marketers evaluate AI-powered media buying tools in 2026?

Evaluation should balance performance potential against operational readiness across four areas:

• Transparency and control. Does the tool provide visibility into targeting decisions, inventory sources, and bid logic? Platforms offering measurement controls and negative keyword targeting deserve preference over opaque systems.
• Data readiness. AI tools are only as good as their inputs. Audit data quality, ensure cross-platform interoperability, and establish governance rules before deploying AI at scale, per EMARKETER.
• Human oversight model. Define where automation ends and human judgment begins. The goal is "a nice balance" between automation and oversight, as Hauptman described.
• Measurement evolution. Prioritize tools supporting multi-touch attribution or marketing mix modeling. Measurement must evolve into a live feedback system that informs decisions continuously.

Start with channels where you have existing performance benchmarks to measure AI-driven lift against known baselines.

EMARKETER forecast data was current at publication and may have changed. EMARKETER clients have access to up-to-date forecast data. To explore EMARKETER solutions, click here.

We prepared this article with the assistance of generative AI tools and stand behind its accuracy, quality, and originality.