GTM teams already have AI agents acting on customer and prospect records, but many teams don’t know what the agents are doing.
According to LeanData’s “2026 State of AI Go-to-Market Readiness Report,” 93% of GTM teams have deployed at least one AI agent. However, nearly a third of respondents couldn’t say how many agents are taking actions on their records, and 30% found actions were taken without an audit trail.
Simply put, marketing and revenue teams are automating decisions faster than they can track them.
LeanData surveyed 157 B2B professionals across revenue operations, MOps, sales, marketing, IT and related functions in May 2026. The sample skewed towards operational roles, with 36% of respondents in RevOps and 13% in MOps.
Most have gone well beyond AI experimentation. 79% said they are implementing their first use cases of the agent or are already scaling agents in the go-to-market. Only 8% described their AI operations as fully optimized.
AI is exposing problems already in the stack
One problem is that agents are using the same customer data, workflows, routing rules and systems that MOps teams have struggled with for years.
Data quality and AI readiness are the top AI transformation challenges, cited by 55% of respondents. 70% said data hygiene made GTM execution worse. As a result, 27% saw multiple tools or agents contact the same prospect, and 17% saw marketing sequences trigger while a sales rep was closing a deal.
AI raises the stakes of bad data because agents can automatically act on it
Bad data is also the top reason (45%) cited for AI initiatives to stall, with 37% citing undocumented processes and 32% citing siled teams. These findings place much of the work required for AI squarely in the familiar territory of MOps: customer data, business rules, integrations, and process documentation.
Agents arrive from everywhere
One of the main issues is the ubiquity of agents and the number of systems that implement them. 69% of respondents use AI features built into GTM tools like Gong, Outreach, or HubSpot. 62% use custom applications based on LLM APIs, while 46% use agent platforms such as Agentforce, Copilot or Gemini Enterprise.
The most commonly reported number of agents in use was three or four. However, nearly a third of respondents were unable to provide any numbers.
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This creates a lot of room for collisions.
A prospect might be enriched by one system, rated by another, enrolled in an automated sequence by a third, and assigned to a representative by a fourth. Every system can work as intended and still produce a poor customer experience if it works with different data, timing, or rules.
Before adding another agent, teams need to know which agents can already edit a customer or prospect’s record, what data they use, and what actions they can take.
This explains why, when asked what they wanted from the technology that coordinates GTM activity, the top choice (31%) was a complete audit trail of every action taken on every record in the systems. Another 21% prioritized having agents follow the same rules as human teams.
Someone still has to take charge of the rules
Who owns and governs those AI agents remains uncertain.
42% of respondents said the GTM AI strategy is in the hands of a cross-functional committee, while 19% said no one owns it and that AI remains ad hoc. RevOps was the designated owner in 18% of organizations.
While cross-functional governance can bring the right functions into AI decisions, someone still needs to maintain the operating rules. The people most likely to take on that job are already under stress. 66% of GTM operations teams said they had more work than they could handle or could keep up with daily operations but lacked capacity for strategic projects. Only 8% said they had enough staff for both.
The full report can be found here. (Registration required)
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