Advertising has automated transactions, but not workflows. Bidding, targeting, and optimization are already machine-led across much of the stack, yet campaigns still move through disconnected tools, platforms, and approval chains. The result is an industry that appears automated on the surface but still depends on manual stitching underneath.
That is why the next phase of Artificial Intelligence (AI) in advertising is not about adding more copilots. It is about enabling agents to operate across systems with context, permissions, and task awareness. The emerging standards in advertising already reflect this shift: Open Real-time Bidding (OpenRTB) remains the auction layer, Ad Context Protocol (AdCP) frames campaign-level workflow coordination, and Model Context Protocol (MCP) enables agents to interact with platforms through structured tool-calling.
MCP does not make advertising more intelligent, it makes it executable across systems.
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Exhibit 1 shows that MCP does not replace the ad stack. It introduces a common orchestration layer that enables AI agents to operate across it.
The real shift: from tools to workflows
The biggest change MCP introduces is not just technological. It is organizational. Today, most work in advertising is still structured around tools. In an MCP-enabled model, work starts to be structured around workflows. Agents can assemble task-specific context, interact with the right systems, and move processes forward within defined limits.
This shifts the operating question from:
Which dashboard do I need to open?
to:
What workflow should run, what guardrails should define it, and where should human approval sit?
That is more significant than deploying a copilot. It changes how campaign operations are designed, how accountability is assigned, and where value sits in the operating model.
Implications for agencies
Agencies are uniquely positioned in this shift because they already sit at the center of advertising fragmentation. While they may not own every platform, they manage the handoffs between them. In effect, agencies already function as orchestration layers, just with too much manual effort.
MCP allows agencies to formalize and scale this role. The competitive edge will come from designing better workflow logic: embedding client context into execution, defining approval paths, setting agent permissions, and ensuring human talent focuses on strategy, judgment, and exception handling rather than coordination-heavy work.
Exhibit 2 illustrates how MCP is reshaping agency value, shifting the role from manual execution and coordination to orchestrating intelligent, context-driven workflows across platforms.
This is a strategic opportunity for agencies. If MCP adoption grows, the competitive edge will not come from simply using more AI tools. It will come from designing better operating logic around them. The winners are likely to be agencies that can connect context, governance, and execution more intelligently than their peers.
Implications for AdTech platforms
For Advertising Technology (AdTech) platforms, MCP changes the strategic question from How do we add AI features? to How do we become usable within agentic workflows?
Historically, platforms competed on data, algorithms, inventory access, workflow depth, and user experience. Those dimensions still matter. But as agents begin interacting across systems, another factor becomes more important: how easily and safely a platform can expose its capabilities to external orchestration layers.
This has implications across the AdTech ecosystem:
- Demand-side Platforms (DSPs) and media activation platforms: enable campaign setup, pacing, budget shifts, troubleshooting, and optimization
- Supply-side Platforms (SSPs) and supply-side infrastructure providers: expose inventory, yield, and deal signals for cross-platform coordination
- Measurement and analytics vendors: move from reporting endpoints to active participants in decision loops
- Retail media and commerce media platforms: simplify fragmented activation through shared agent interfaces rather than repeated manual operations
Platforms will differentiate on data, optimization models, identity, measurement, and supply access. But MCP raises the strategic value of interoperability. In an agentic ecosystem, the platform that is easiest to coordinate with may become more valuable than the one that is easiest to use manually.
Implications for enterprises
Large advertising environments create two recurring issues for enterprises: fragmented visibility and fragmented control. MCP introduces the possibility of a more connected operating model. Enterprises should not view MCP as another experimental AI concept, the opportunity lies in more consistent execution with stronger governance.
This enables enterprises to:
- Connect internal context with external partner tools
- Standardize workflows without hard-coding every integration
- Define clearer approval paths for agent-led actions
- Centralize permissions and auditability across teams and vendors
- Reduce dependence on manual coordination for routine work
For enterprise organizations, this is especially relevant as the boundaries between Marketing Technology (Martech) and AdTech continue to blur. MCP can help enterprises manage that convergence more coherently.
The market is early, but no longer theoretical
Market direction is becoming clearer as agencies, platforms, and enterprise-facing vendors begin to expose agent-friendly infrastructure and workflow-specific access models. The common thread is that AI is moving beyond insight generation toward governed execution with real operating environments.
Three signals stand out.
- Agency operating systems are becoming agentic. Examples such as PMG and FreeWheel point to use cases that go beyond reporting into negotiation, optimization, and execution. WPP’s Agent Hub reflects a similar direction: agencies are moving toward agent-driven operating models, whether or not they explicitly use MCP terminology
- Ad platforms are exposing infrastructure for agent connectivity. Amazon Ads’ MCP Server is an example of how platforms are starting to treat agent connectivity as infrastructure rather than experimentation
- Enterprise providers are productizing governed AI access. Guideline’s Media Plan Management MCP Server shows how vendors are creating workflow-specific access layers for planning, budget, vendor, and plan-versus-actual workflows instead of waiting for generic assistants to solve operational problems
These signals point to a clear shift: AI is moving from insight generation to governed execution.
Exhibit 3 highlights how MCP transforms day-to-day marketing operations, shifting from fragmented, manual processes to streamlined, agent-driven execution with greater automation, coordination, and control.
With MCP, work does not disappear; it moves up the value chain. The value shifts from performing fragmented tasks to designing and governing how those tasks are performed.
The bottom line: the next battleground is workflow control
MCP will not eliminate AdTech fragmentation overnight. But it changes where that fragmentation is managed. Instead of coordinating work across disconnected interfaces, organizations can manage execution through intelligent agents operating across a shared protocol layer.
That is meaningful for the entire ecosystem:
- Agencies can move up the value chain from platform operation to workflow design
- AdTech platforms can compete not only with features, but with how well their capabilities participate in broader agentic workflows
- Enterprises can scale execution with more consistency, stronger governance, and better cross-functional coordination
The next phase of advertising is unlikely to be defined by who has the most tools. It will be defined by who builds the best orchestration layer above them. That is where MCP could have its biggest impact.
If you found this blog interesting, check out our report, The AdTech Revolution: Decoding the AI-driven Advertising Landscape.
If you have any further questions and would like to discuss unified revenue orchestration platforms, please reach out to David Rickard ([email protected]), Lochan Surana ([email protected]), or Aakash Verma ([email protected]).