AI agents have moved into go-to-market stacks faster than many teams can count them, let alone govern them. LeanData’s 2026 State of AI Go-to-Market Readiness Report finds that 93% of GTM teams have deployed at least one AI agent. Yet nearly a third of surveyed practitioners could not say how many agents were taking actions on their customer and prospect records, and 30% had seen actions taken without an audit trail.
The survey of 157 B2B practitioners across revenue operations, marketing operations, sales, marketing and IT was fielded in May 2026. The sample leaned heavily toward operations roles, with 36% in RevOps and 13% in MOps.
The adoption-oversight gap
Most teams have moved past experimentation. Seventy-nine percent reported they are deploying their first agent use cases or already scaling agents across go-to-market. Only 8% described their AI operations as fully optimized.
That gap between adoption and operational maturity is the central tension. Teams are adopting automation faster than they can track what it is doing.
Bad data gets amplified
Agents do not solve the data problems already sitting in the stack; they act on them. Data quality and AI readiness were the top transformation challenge, cited by 55% of respondents. Seventy percent said data hygiene has degraded GTM execution.
The consequences are already visible. Twenty-seven percent saw multiple tools or agents contact the same prospect, and 17% had marketing sequences fire while a sales rep was working a deal. Bad data is also the top reason cited for stalling AI initiatives at 45%, followed by undocumented processes at 37% and siloed teams at 32%.
Agents are coming from everywhere
Part of the problem is ubiquity. 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, and 46% use agent platforms such as Agentforce, Copilot or Gemini Enterprise.
The most commonly reported number of agents in use was three or four, but nearly one-third of respondents could not provide a number at all. Without a clear inventory, teams cannot see where one agent enriches a record, another scores it and a third enrolls the same prospect in a sequence.
Ownership is still unsettled
Governance remains fragmented. Forty-two percent of respondents said a cross-functional committee owns GTM AI strategy, but 19% said nobody owns it and AI remains ad hoc. RevOps was the designated owner in only 18% of organizations.
The people best placed to maintain the underlying rules 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.
What to do before adding another agent
The research points to a practical sequence for marketing and revenue operations teams:
- Map every agent that can read or change a customer or prospect record.
- Require an audit trail for all agent actions, especially customer-facing ones.
- Assign a named owner for routing rules, data hygiene and AI agent boundaries.
- Fix data quality and document processes before scaling any new agent use case.
Teams appear to want the same thing. When asked what they wanted from technology coordinating GTM activity, the top choice, at 31%, was a complete audit trail of every action on every record across systems. Another 21% wanted agents to follow the same rules as human teams.
Before adding more automation, the more urgent move is to build the ledger, the rules and the ownership structure that make existing agents safe to scale.
Source: MarTech
