GTM teams already have AI agents acting on customer and prospect records, but many teams don’t know what the agents are doing.
Ninety-three percent of GTM teams have deployed at least one AI agent, according to LeanData’s “2026 State of AI Go-to-Market Readiness Report.” Yet nearly one-third of respondents couldn’t say how many agents were taking actions on their records, and 30% had found actions taken without an audit trail.
Simply put, marketing and revenue operations teams are automating decisions faster than they can track them.
LeanData surveyed 157 B2B practitioners across revenue operations, MOps, sales, marketing, IT, and related functions in May 2026. The sample leaned toward operations roles, with 36% of respondents in RevOps and 13% in MOps.
Most have moved well beyond experimenting with AI. Seventy-nine percent said they were deploying their first agent use cases or already scaling agents across go-to-market. Only 8% described their AI operations as fully optimized.
AI is exposing problems already in the stack
One problem is that the agents are using the same customer data, workflows, routing rules, and systems MOps teams have been wrestling with for years.
Data quality and AI readiness are the top AI transformation challenges, cited by 55% of respondents. Seventy percent said data hygiene has degraded GTM execution. As a result, 27% have seen multiple tools or agents contact the same prospect, and 17% have seen marketing sequences fire while a sales rep was working 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 stalling AI initiatives, 37% cited undocumented processes, and 32% pointed to siloed teams. Those findings put much of the work required for AI squarely in familiar MOps territory: customer data, business rules, integrations, and process documentation.
Agents are coming from everywhere
A major issue is the ubiquity of agents and the number of systems that deploy them. Sixty-nine percent of respondents use AI features embedded in GTM tools such as Gong, Outreach, or HubSpot. Sixty-two percent use custom applications built 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 one-third of respondents couldn’t provide a number at all.
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That creates plenty of room for collisions.
A prospect might be enriched by one system, scored by another, enrolled in an automated sequence by a third, and assigned to a rep by a fourth. Each system can work as designed and still produce a poor customer experience if it operates with different data, timing, or rules.
Before teams add another agent, they need to know which ones can already change a customer or prospect record, what data they use, and which actions they’re allowed to take.
That explains why, when asked what they wanted from technology coordinating GTM activity, the top choice (31%) was a complete audit trail of every action taken on every record across systems. Another 21% prioritized making agents follow the same rules as human teams.
Someone still has to own the rules
Who owns and governs those AI agents remains unsettled.
Forty-two percent of respondents said a cross-functional committee owns GTM AI strategy, while 19% said nobody owns it and AI remains ad hoc. RevOps was the designated owner at 18% of organizations.
While cross-functional governance can bring the right functions into AI decisions, someone still has to maintain the operational rules. The people most likely to handle that work are already stretched. Sixty-six percent of GTM operations teams said they either had more work than they could handle or could keep up with daily operations but had no capacity for strategic projects. Only 8% said they had enough staff for both.
The complete report can be found here. (Registration required)
