The news: Media companies are leaning on AI to make TV and streaming ads work more like digital ads on platforms such as Google, Meta, and Amazon—where advertisers can track performance, target audiences precisely, and optimize campaigns in real time.

• During Upfronts, Amazon announced Dynamic TV Creative, an AI-powered tool that fixes repetitive connected TV (CTV) ads by auto-personalizing Prime Video ads based on viewer shopping behavior and a viewer’s prior exposure to a brand.
• Netflix featured AI tools that can build and optimize media plans by brand goals and AI agents that manage, optimize, and buy ads across formats. It also uses AI to adapt creative for vertical video and pause ads, match ads to shows, and personalize ad load and frequency based on viewer behavior.
• NBCU detailed plans for its AI agents to automate transactions, deliver near-real-time insights, and support campaign decisions within its One Platform stack. 

Why it’s worth watching: The push toward AI and agentic systems addresses specific pain points for advertisers, including ad repetition, frequency capping, and cross-platform measurement. But some buyers are skeptical.

The media industry's eagerness to label everything “AI-powered” risks an interoperability bottleneck—a concern Mary Gabrielyan, chief strategy officer at AI Digital, brought up to Ad Age. “Media buying and media planning isn’t becoming simpler. All of these announcements are making a very fragmented, siloed industry even worse. Right now, the interconnectivity of data is becoming close to impossible.”

Given Upfronts are sales presentations where media companies herald upcoming products and tools, the trending topic for the foreseeable future is harnessing AI that can reduce waste, automate optimization, and bring TV buying closer to digital-style performance, but these solutions remain unproven.

Implications for advertisers: Rather than asking whether these nascent, siloed solutions all work together, advertisers should evaluate how well each one integrates with existing ad measurement and determine if their value justifies the added complexity.

• Run parallel audits to compare AI-driven campaigns against traditionally managed buys. 
• Insist on transparent methodology for how ad performance is measured and reported to see if they’re worth adopting.

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