Most AI agents fail without data and governance maturity | MarTech

Every vendor is currently 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 quickly, but there’s a problem.

According to Gartner’s 2025 survey of martech leaders, 81% are testing or actively using AI agents. Yet 45% say so the agent provided by their supplier does not deliver on the promised business performance.

This gap between expectation and reality is a signal flame. Marketing gets ahead of its own readiness, leaving agents in piles littered with inconsistent data, weak integrations, loose governance, and underdeveloped talent. As usual, it’s left to MOps to clean up the mess – and 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 releasing agents into environments that simply aren’t ready for it.

Are AI agents the real deal?

There’s a reason why adoption of AI agents is exploding:

• 81% of martech leaders are using or testing AI agents offered by vendors.
• 89% expect “significant benefits to business performance.”
• Agents are already powering key use cases: content production, campaign management, asset creation, and journey building.
• And Gartner predicts that by 2026 40% of business 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 building the operational maturity to support them.

Dig deeper: 7 tips to get started with AI agents and automations

Gartner also found the reasons behind this underperformance of agents are remarkably consistent across organizations:

• The stack is not ready: 50% of leaders report infrastructure deficiencies. If your systems aren’t syncing in real time, if field-level hygiene is a mess, if your CDP isn’t fully deployed, and if identity resolution is inconsistent, your AI agent will never deliver the stunning business results highlighted in the vendor’s demo.
• Teams don’t have the talent or skills: Marketers know the results they want, but not the operational implications of allowing autonomous agents to trade within the stack. Gartner sees a growing skills gap, specifically in integrating, orchestrating and monitoring agents.
• The board is missing or looks back to the future: Agents need policy, supervision and monitoring, not as an afterthought, but as a prerequisite for deployment. According to Gartner, most organizations are still writing their governance policies after problems arise. It’s no wonder that expectations and results don’t align.

What’s broken and how MOps can fix it

When AI agents perform poorly or fail, MOps is the first to feel the impact:

• Wrong ROI alignment: Suppliers promise saved hours and performance improvements. What teams experience is friction, cleanup, and manual transfer.
• New security surfaces: Agents increase the number of automations, triggers, and API touchpoints, increasing visibility.
• The stacking complexity increases: Agents placed on top of an already vast martech environment can create complicated workflows that no one can figure out.
• Supplier lock-in: Once an agent is deeply embedded in your daily workflows, switching becomes painful, even if the agent is underperforming.

We can’t wait for suppliers to magically fix the performance gap. We must lead the operationalization. Here is the step-by-step process I use to advise clients.

Step 1: Assess the readiness of your stack (before deploying anything)

Check data cleanliness, field standardization, identity resolution, API stability, and sync frequency. Most failed agent implementations can be traced back to fundamental gaps that should have been obvious. AI agents inherit your flaws. If your data is fragmented, your agent will be fragmented too.

Step 2: Vets with real use cases, not vendor demos

Drive the agent within your actual workflows using real data, segments, campaigns, dependencies, and management constraints. If it fails in a pilot, scaling up won’t solve the problem.

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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, boundaries for agent behavior, approval workflows, and monitoring protocols.

Research by Gartner shows that organizations do that embedding governance in business units experience 40% fewer AI-related incidents.

Step 4: Improve your team – don’t wait for the supplier

Teams need training in agent orchestration, rapid 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 perform.

Step 5: Continually measure and monitor agent performance

Define success metrics early. Think of statistics like:

• Contribution of income.
• Hours reclaimed.
• Error rate.
• Workflow breaks.
• Model drift.
• Impact on data quality.
• Impact on customer experience.
• Compliance flags.

If an agent is not delivering meaningful value within 60 to 90 days, retire it. Agents should earn their place in your deck.

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 leaders of CMOs and MOps, our job is to deploy the right agents in the right environment, with the right governance and expectations.

Rather than racing to deploy the most AI agents, we should strive to deploy them responsibly, in line with our goals, so that they have the greatest impact.