An order management system (OMS) is a critical part of media technology infrastructure, allowing media companies to sell, manage, deliver and report on advertising. Nearly every media company has some form of OMS in place, sometimes more than one, as legacy software supporting different parts of the business evolved.
With the influx of new AI capabilities, media company leaders are determining the best strategy to infuse their ad sales and operations with efficiency, intelligence and higher performance. However, bolting AI capabilities onto a fragile, static OMS will not yield the results media companies are looking for.
OMS platforms must evolve from static systems to enable modular integrations, agent-to-agent communication, and orchestration across planning, pricing, and yield technologies.
Traditional OMS Structures Break Under AI
A typical enterprise technology stack has a complex and rigid integration model that is built on APIs, scheduled data transfers and workflow triggers. This infrastructure evolved when workflows were regular and fixed, and was built up in silos to serve people who are responsible for specific parts of the business.
AI changes this paradigm completely, with a more continuous and fluid design that can stretch across different parts of the business seamlessly and automate workflows. AI can work in real time, learn on the fly, simplify complex processes and connect quickly across disparate data sets and dashboards. The rigid infrastructure of the past simply can’t keep pace, lacking the ability to flex and scale to support.
Trying to put an AI agent on top of legacy technology that relies on daily data uploads or “swivel chair” manual data entry not only limits the effectiveness of the AI, but also threatens to break integrations, processes and data flows all together.
As more partners upgrade their own technology stack for AI, they will expect media companies to be able to handle a more fluid transfer of data, to reach immediately and scale easily. Companies that are stuck with technology that can’t interact effectively with the rest of the ecosystem will find themselves falling behind.
Design A Modular, Partner-Centric Stacks That Scales
The right architectural model supports direct collaboration between OMS AI and partner AI – and that means an open, modular stack that can communicate with partners with direct interaction.
This new model is “AI-first” which reduces redundancy and architectural sprawl across the business. Media companies rely on partner platforms for specialized execution while preserving control of their operating framework, without having to recreate complex capabilities internally. In this approach, the OMS becomes the environment where intelligence is coordinated and governed, pushing information out to other partners and ingesting insights fluidly.
In an AI-first architecture, media companies have the following:
- Open Agent Interfaces: Partner AI agents perform specific tasks automatically and autonomously and communicate outcomes to the OMS, rather than importing business logic. An agent can plan and generate deal structures, recommend rate adjustments, provide delivery instructions, forecast and report. With a modular integration design, intelligence is flexible.
- Shared Orchestration: The OMS is the center of orchestration logic including workflow sequencing, approvals, governance and escalation paths. While the OMS governs the flow, partner platforms are focused on task execution and optimization. This approach allows for clear ownership which preserves accountability and simplifies compliance, and ensures decision logic is centralized rather than fractured across systems.
- Clear Operational Boundaries: A good design allows the media company to govern decisions and data through the OMS while partners deliver as infrastructure. With an intelligent core, the enterprise maintains control and enables scale and business growth.
A Framework for AI-Ready Operational Infrastructure
With the OMS at the core, AI agents can be deployed to interact with partner agents, moving insight out to the correct systems to enable them to carry out the appropriate autonomous tasks.
OMS is the central intelligence. The OMS has the business rules, product data, pricing and other critical intelligence that governs the revenue business. With this information, it also provides context such as inventory availability, client history, business rules, and campaign objectives.
Partners deliver interactive execution. Partner agents interact directly with OMS agents to gather relevant information that then allows them to execute specific tasks effectively. Rather than being a “one way street” – partner and OMS agents interact fluidly, improving and changing as they gather new information or get new direction.
Media planning is a perfect example of how OMS and partner agents can interact effectively with this architecture. A planning workflow built on agentic AI has shorter, more efficient planning cycles and streamlines manual efforts without having to duplicate years of specialized development. Every media company can maintain its unique structure, while improving effectiveness, speed and performance.
The benefits of this AI-first model hold true across revenue operations. With a framework for AI architecture across their monetization workflows, media companies can future-proof their OMS by giving it the architectural freedom to evolve as technology and business demands accelerate in the future.
