Observed Signal · Aug 31, 2026 · Technical Release · Source: https://martechseries.com/feed/ · Impact: 2/5 · Sentiment: Neutral
LeanData Survey Reveals Bad Data Hinders GTM AI Success
LeanData, an AI-ready go-to-market (GTM) infrastructure provider, has released results from its AI GTM Customer Survey conducted in May 2026. Polling 157 revenue operations, marketing operations, and sales practitioners, the survey highlights a growing gap between AI deployment and performance. While 79% of respondents are currently scaling or deploying AI agents, 70% state that poor data quality is undermining their execution. Key hurdles to successful AI integration include inconsistent or incomplete CRM data (45%), undocumented business processes (37%), and siloed teams (32%). GTM professionals also expressed apprehension, with 60% fearing that AI agents would operate on inaccurate data or wrong records, indicating a need for greater control and auditability in GTM systems.
Identifies the core data hygiene and CRM challenges currently limiting the enterprise deployment of AI agents in go-to-market workflows.
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Key Takeaways & Evidence Grounding
- LeanData polled 157 GTM, RevOps, and marketing operations practitioners in May 2026.
- 79% of surveyed GTM professionals are scaling or deploying AI agents.
- 70% of respondents say poor data quality is undermining their AI GTM execution.
- 45% of respondents cited inconsistent or incomplete CRM data as a blocker for AI initiatives.
- 60% of GTM professionals are concerned that AI agents will act on inaccurate data or violate ownership rules.
Ontology Mapping & Concepts
Related Market Signals & Shifts
Recent verified developments and strategic activity across this market segment.
GTM Teams Losing Track of AI Agents
A new report from LeanData reveals that while 93% of go-to-market (GTM) teams have deployed at least one AI agent, many lack visibility into their actions. Nearly one-third of the 157 B2B practitioners surveyed couldn't state how many agents were operating on their records, and 30% found unauthorized actions without an audit trail. Data quality and AI readiness are top challenges, with 70% reporting degraded GTM execution due to poor data hygiene. This has led to issues like multiple tools contacting the same prospect and marketing sequences firing during sales interactions. The chaos stems from a proliferation of agents across embedded tools, custom LLM-based apps, and agent platforms like Agentforce, Copilot, and Gemini Enterprise. Many organizations lack clear governance, with 19% having no owner for AI strategy. The top desired solution is a complete audit trail for all record actions. This highlights the urgent need for better agent management and data governance in marketing operations.
Marketers Say AI Uses Poor CRM Data
A Validity survey of 500 B2B and B2C marketing professionals (July 2026) finds that marketers increasingly delegate decisions to autonomous AI agents despite poor CRM data readiness. Nearly 91% say data readiness is critical for AI adoption, but only 21% consider their CRM data very well prepared. Forty-five percent already use agentic AI and two-thirds increased delegation to autonomous agents in the past year. Poor CRM data is already hitting revenue (62% said it probably or definitely cost revenue), and many marketers have acted on AI recommendations later suspected to be wrong (19% frequently, 43% occasionally). Organizational gaps — limited data governance, uneven collaboration between marketing and IT/RevOps, and unclear ownership — are cited as barriers. Respondents prioritized continuous automated monitoring, unified platforms, and third-party validation to improve CRM readiness.
Survey Reveals Major Gap in Agentic AI Readiness
Reltio published results of a Harvard Business Review Analytic Services pulse survey, “Unlocking the Data Advantage in the Age of Intelligence,” which surveyed 325 global business and technology leaders. The survey shows strong AI interest—94% of organizations are exploring or implementing AI—but major gaps in data readiness for agentic AI. While 94% rank trust in data reliability as critical, only 39% say they are highly proficient; just 15% consider their data foundation "very ready" for agentic AI. Respondents cite data silos (46%), insufficient data talent (42%) and unclear data strategies (39%) as top barriers. The report stresses need for unified, real-time, governed data, semantic layers, and evolving governance from compliance to strategic differentiator.
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