Advertising has never truly operated in silos, even if the technology supporting it often has.
Campaign performance depends on coordination across advertisers, DSPs, SSPs, publishers, measurement systems, identity solutions, and increasingly, AI-powered tools. But despite how interconnected the ecosystem already is, much of the operational reality still feels fragmented. Teams bounce between platforms to troubleshoot issues, piece together delivery signals manually, and rely on disconnected workflows to understand what’s happening across campaigns.
As AI becomes more embedded into advertising workflows, the next phase of innovation will come from how systems collaborate with one another across the ecosystem.
That’s what made our recent collaboration between AdRoll and PubMatic so exciting.
The collaboration centered around a concept integration using Model Context Protocol (MCP), an open standard designed to help AI systems securely interact with external tools, data, and workflows.
Together, AdRoll and PubMatic explored how agents could work across both demand-side and supply-side environments to diagnose campaign delivery issues in a shared workflow.
Instead of troubleshooting in separate systems, AdRoll’s agents were able to query PubMatic’s diagnostics directly to evaluate pacing constraints, creative blocks, and publisher-side delivery factors alongside campaign configuration signals from the demand side.
The technical implementation itself is interesting, but the broader implication matters even more.
The collaboration validated a more interoperable approach, where independent platforms can coordinate dynamically through shared standards rather than relying on tightly coupled integrations or manual operational workflows.
That flexibility becomes increasingly important as advertising workflows grow more complex and AI becomes more embedded into day-to-day operations.
One of the biggest operational challenges in programmatic advertising is that no single platform has complete visibility into what is happening across the full delivery path.
A DSP may see campaign setup and bidding behavior. An SSP may see auction dynamics, publisher constraints, or deal-level pacing issues. Publishers have their own operational signals. Advertisers often sit in the middle trying to reconcile fragmented information across systems.
That fragmentation slows everything down.
When intelligence can move more fluidly across platforms, teams gain a more complete understanding of performance and can act faster with more confidence.
In the AdRoll and PubMatic collaboration, this meant surfacing root causes and recommended actions within a coordinated workflow rather than requiring manual investigation across multiple interfaces.
The outcome is faster troubleshooting, reduced operational friction, clearer visibility, and more efficient optimization across the supply chain.
Importantly, interoperability does not require every platform to become the same system. It creates a framework where specialized platforms can contribute intelligence collaboratively while maintaining flexibility across the ecosystem.
The broader advertising ecosystem is already moving in this direction.
Alongside emerging initiatives like AdCP, the IAB Tech Lab has also introduced AAMP, a framework focused on enabling agentic advertising workflows through interoperable standards and protocols.
While approaches across the industry may differ, the underlying direction is becoming increasingly clear: AI-driven advertising workflows will depend on shared standards, interoperable systems, and better coordination across platforms.
That evolution mirrors earlier industry shifts around standards like OpenRTB, header bidding, and shared identity frameworks, which helped create more scalable coordination across the advertising ecosystem.
MCP, AdCP, and initiatives like AAMP represent the next phase of that evolution, focused on enabling agents and AI systems to work across platforms, workflows, and marketplaces more dynamically.
The rise of AI agents is changing expectations around how advertising systems operate.
Today, many AI experiences in advertising remain platform-centric, helping users generate insights or complete workflows within a single environment. Increasingly, however, the opportunity is shifting toward agents coordinating across systems, workflows, and partners.
That evolution becomes especially important as more advertising activity moves toward marketplace-driven and deal-based environments. As more inventory becomes interoperable and standards continue to mature, collaboration between the demand side and supply side will matter even more.
Troubleshooting, optimization, forecasting, deal coordination, and transaction workflows will increasingly depend on intelligence that spans multiple systems rather than living in isolated platforms.
The AdRoll and PubMatic collaboration represents an early operational example of how ecosystem-level coordination between platforms can work in practice today.
One of the most exciting parts of this work is that it reflects a broader shift happening across the industry.
AI is increasingly becoming connective infrastructure across the ecosystem, enabling systems to coordinate more intelligently across platforms, workflows, and partners.
As interoperability improves, platforms across the ecosystem will have more opportunities to coordinate workflows, reduce operational friction, and create more transparent ways to manage advertising outcomes together.
Diagnostics are one example, but the same principles can extend into optimization, forecasting, marketplace coordination, and transacting across supply and demand.
AdRoll has been engaging with supply-side partners to explore future use cases for MCP expansion. As we assess the workflows that require the most cross-system coordination today, we see a clear opportunity to offload these tasks to our MCP server to drive meaningful efficiency gains.
We envision the next phase of our SSP-focused MCP expansion extending beyond deal ID troubleshooting into deal discovery and creation. While most platforms offer self-serve deal discovery UIs, these interfaces can be overwhelming and often fail to surface the full picture of available inventory for a given request.
Today, large advertisers with specific deal requirements frequently need one to two weeks of lead time just to assess feasibility and available inventory. We see a future where agent-to-agent collaboration compresses that timeline to minutes. We also aim to enable deal parameter adjustments directly via MCP, allowing campaigns to make straightforward changes — such as broadening targeting criteria or adding geos and formats — without manual intervention.
Beyond deal management, we aim to accelerate DSP-to-SSP collaboration for integration health checks and new integrations through MCP. This process historically relies on manually reviewing code to confirm support for various formats and fields, with follow-up handled through spreadsheets. When expansion opportunities arise, SSPs need visibility into potential spend upside while DSPs need clarity on scale and level of effort.
By surfacing these insights through MCP — pre-validated and backed by real scale data — both sides gain the transparency needed to deepen their connection in a fraction of the time and with far fewer resources than current processes demand.
The future of advertising will not only be shaped by advances in AI models, but also by how effectively platforms collaborate across the ecosystem to help marketers move faster, operate more efficiently, and make better decisions together.
While the PubMatic collaboration explored cross-platform ecosystem coordination, AdRoll’s own MCP server is already being actively tested in beta for day-to-day marketer workflows.
The AdRoll MCP Server connects AI clients like ChatGPT, Claude, Cursor, and automation platforms directly to AdRoll through the open Model Context Protocol, making campaign workflows accessible within the AI tools marketers already use every day. Early use cases include reporting and performance analysis, campaign creation and management, and account exploration workflows.
If you’re interested in learning more or participating in the beta, you can find setup details and additional information in our Help Center article.
Last updated on May 29th, 2026.