Every vendor right now is selling a version of the same dream: AI agents that automate campaign execution, write content, optimize performance and orchestrate entire workflows while you sleep. Marketing leaders are buying in fast, but there’s a problem.
According to Gartner’s 2025 survey of martech leaders, 81% are piloting or actively using AI agents. Yet 45% say the agent provided by their vendor does not deliver on promised business performance.
This gap between expectation and reality is a signal flare. Marketing is racing ahead of its own readiness, dropping agents into stacks riddled with inconsistent data, weak integrations, loose governance and underdeveloped talent. As usual, MOps is left to clean up the mess — and gets blamed for slow adoption.
AI agents are only as strong as the data they use, the workflows they follow and the governance that shapes them. Right now, too many teams are unleashing agents in environments that simply aren’t ready.
Are AI agents the real deal?
There’s a reason AI agent adoption is exploding:
- 81% of martech leaders are using or piloting vendor-offered AI agents.
- 89% expect “significant business performance benefits.”
- Agents already power major use cases: content production, campaign management, asset creation and journey building.
- And Gartner predicts that by 2026, 40% of enterprise applications will embed task-specific AI agents — up from less than 5% in 2025.
The problem is simple — marketing is adopting AI agents faster than it’s building the operational maturity to support them.
Dig deeper: 7 tips for getting started with AI agents and automations
Gartner also found that the reasons behind agent underperformance are strikingly consistent across organizations:
- The stack isn’t ready: 50% of leaders report infrastructure gaps. If your systems don’t sync in real time, if field-level hygiene is a mess, if your CDP isn’t fully deployed and if identity resolution is inconsistent, then your AI agent will never deliver the dazzling business outcomes highlighted in the vendor demo.
- Teams don’t have the talent or skills: Marketers know the outcomes they want, but not the operational implications of allowing autonomous agents to act within the stack. Gartner notes a widening skills gap specifically around integrating, orchestrating and monitoring agents.
- Governance is either missing or backward-looking: Agents require policy, oversight and monitoring not as afterthoughts but as prerequisites for deployment. According to Gartner, most organizations are still writing their governance policies after issues arise. It’s no wonder that expectations and outcomes aren’t aligned.
What’s breaking and how MOps can fix it
When AI agents underperform or misfire, MOps feels the impact first:
- ROI misalignment: Vendors promise hours saved and performance uplift. What teams experience is friction, cleanup and manual override.
- New security surfaces: Agents increase the number of automations, triggers and API touchpoints, all of which expand exposure.
- Stack complexity increases: Agents layered on top of an already sprawling martech environment can create tangled workflows no one can unwind.
- Vendor lock-in: Once an agent is embedded deeply into your daily workflows, switching becomes painful, even if the agent underperforms.
We can’t wait for vendors to magically fix the performance gap. We have to lead the operationalization. Here’s the step-by-step process I use to advise clients.
Step 1: Assess your stack readiness (before you deploy anything)
Audit data cleanliness, field standardization, identity resolution, API stability and sync frequency. Most failed agent deployments can be traced back to foundational gaps that should have been apparent. AI agents inherit your flaws. If your data is fragmented, your agent will be fragmented too.
Step 2: Vet agents with real use cases, not vendor demos
Pilot the agent inside your actual workflows using real data, segments, campaigns, dependencies and governance constraints. If it fails in a pilot, scaling won’t fix it.
Dig deeper: Building AI agents that move from conversation to conversion
Step 3: Build governance before you build use cases
Create a governance structure that includes MOps, IT, security and legal. You need approved tool lists, data access rules, agent behavior boundaries, approval workflows and monitoring protocols.
Gartner’s research shows that organizations that embed governance into business units experience 40% fewer AI-related incidents.
Step 4: Upskill your team — don’t wait for the vendor
Teams need training in agent orchestration, prompt frameworks, risk detection, incident reporting, AI safety basics and workflow redesign. Agent adoption without skill-building is one of the top reasons agents fail to deliver.
Step 5: Continuously measure and audit agent performance
Define success metrics early. Consider metrics like:
- Revenue contribution.
- Hours reclaimed.
- Error rate.
- Workflow breaks.
- Model drift.
- Data quality impact.
- Customer experience impact.
- Compliance flags.
If an agent doesn’t deliver meaningful value in 60–90 days, decommission it. Agents should earn their place in your stack.
Dig deeper: What AI and agents mean for marketing teams — now and in the future
AI agents require intentional design and governance
AI agents have enormous potential, but only if MOps teams implement them strategically. As CMOs and MOps leaders, our job is to deploy the right agents, in the right environment, with the right governance and right expectations.
Instead of racing to deploy the most AI agents, we should strive to adopt them responsibly, in alignment with our goals, so they have the greatest impact.
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