Media Buying and Selling

Traditional automation typically follows predefined rules and workflows. AI agents introduce the possibility of systems that can interpret objectives, determine which actions are required and execute multi-step tasks across different tools and platforms.

Media buyers often work across multiple DSPs, social platforms, retail media networks and other buying systems. AI agents could provide an orchestration layer across these environments — helping configure campaigns, allocate budgets, monitor performance and coordinate actions from a common workflow.

AI can combine campaign performance, audience, contextual, inventory, pricing and other data to support decisions about where and how media should be bought. Predictive and optimisation models could continuously evaluate opportunities and adjust buying strategies as conditions change.

As advertising, measurement and business data become more connected, buying strategies can increasingly be informed by outcomes such as sales, revenue and customer acquisition rather than optimising individual platforms around media metrics alone.

Emerging agentic models create the possibility of buyer and seller agents interacting directly. A buying agent could communicate campaign requirements and constraints, while seller agents expose relevant inventory and opportunities — creating new ways to discover, negotiate and transact media.

Greater autonomy doesn’t necessarily mean removing people from media buying. Buyers can define objectives, budgets, constraints and governance rules while agents handle more of the repetitive analysis, platform interaction and execution required to achieve them.