Beverage Marketer Sees Cost Savings with Agentic Media-Buying Test

Why It Matters

The results prove that agentic AI can dramatically lower media‑buy costs without sacrificing reach or brand safety, prompting advertisers to rethink traditional buying workflows. This efficiency boost could accelerate AI adoption across the ad tech ecosystem.

Key Takeaways

• AgenticOS cut buy‑side costs 5.5×
• 98% video completion rate achieved
• Impressions 40% above plan
• <1% inventory failed verification
• AI automation reduces manual optimization time

Pulse Analysis

The ad‑tech landscape is rapidly embracing autonomous solutions, and PubMatic’s AgenticOS represents a milestone in that evolution. By delegating campaign planning, buying, and real‑time optimization to AI agents, the platform eliminates the need for multiple vendor touchpoints. This end‑to‑end automation not only shortens the execution timeline but also creates a data‑rich environment where algorithms can continuously refine targeting decisions across CTV, display and mobile inventory.

Performance data from the Geloso test underscores the commercial upside of agentic buying. A 5.5‑fold reduction in supply‑chain costs translates into significant budget stretch, while a 98% video‑completion rate and a 40% uplift in impressions demonstrate that efficiency does not come at the expense of audience engagement. Moreover, the campaign’s sub‑1% verification failure rate and zero presence on made‑for‑advertising sites reassure brands that AI can uphold stringent quality standards, addressing a core concern that has slowed broader adoption.

For marketers and CMOs, the implications are twofold. First, the operational savings free up resources for strategic activities such as creative testing and audience insight generation. Second, the success of a fully agentic workflow signals that legacy, proprietary AI tools may become obsolete as open, interoperable platforms gain traction. As agencies double down on AI capabilities—evidenced by recent acquisitions and pilot programs—organizations that invest in skill development and transparent AI governance will be best positioned to leverage these efficiencies while maintaining brand safety and accountability.