The Future of B2B Attribution: A Perspective for Today's Modern, Complex GTM Motion
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Future of attribution

The 2025 State of Agentic AI in B2B GTM

Research conducted in partnership with Ascend2
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The problem isn’t AI.

It’s the GTM machinery behind it.
Agentic AI is accelerating faster than any other GTM innovation. But most organizations aren’t struggling with the AI itself; they’re struggling with the systems, data, and workflows expected to support it.
The truth is simple: You can’t run autonomous intelligence on infrastructure built for manual operations.
Teams feel the friction every day: fragmented signals, inconsistent follow-up, disconnected channels, and reporting that changes depending on who pulls it. Download the report to uncover the reality behind the adoption wave and understand what teams must rebuild before Agentic AI can actually deliver.
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The Challenge
The Underlying Gap
The Way Forward
The Challenge

Why GTM Teams Struggle Even as AI Adoption Accelerates

GTM leaders report confidence in their execution, but their underlying bottlenecks reveal a system that’s stretched thin:
Lead quality remains unpredictable
Data remains inconsistent & fragmented
Follow-up breaks down across teams
Content & asset operations lack alignment
Behind these symptoms is a GTM engine still dependent on manual interpretation, tool-hopping, and human coordination across disconnected channels. Agentic AI is advancing rapidly, but most GTM organizations are still operating on infrastructure designed for reactive workflows rather than for autonomous decision-making.
The result?
AI pilots and partial automation deliver value in pockets, but the system as a whole remains fragile, inconsistent, and difficult to scale.
The Underlying Gap

AI Isn’t the Problem. The Operating Model Is

It’s the GTM machinery behind it.
The disconnect isn’t in the enthusiasm for Agentic AI; adoption is happening at unprecedented speed:

41%

Already use Agentic AI

35%

Are rolling it out

96%

Believe AI with full-funnel context would meaningfully improve execution

90%

Expect Agentic AI to be essential within two years
Yet teams aren’t seeing the transformational lift they expect because:
Their data foundation is incomplete or siloed
Systems don’t communicate consistently
Context is fragmented across tools
Governance exists, but isn’t operationalized
Execution still relies on humans to connect the dots
GTM teams want autonomous intelligence, but the environment that supports it lacks the cohesion needed to sustain it. Agentic AI can understand, but it can only act as reliably as the context and structure it sits on.
This is the real readiness gap: AI is evolving faster than the GTM operating models meant to support it.
The Way Forward

Building the Foundations for Agentic Execution at Scale

Breaking the cycle of fragmented execution doesn’t require more tools; it requires a re-architecture of how GTM intelligence flows:
Unified Data Foundations
Create a single, governed layer of truth that connects signals across CRM, MAP, sales engagement, product usage, ads, and intent sources. Autonomous AI requires complete, accurate, timely data, not isolated snapshots.
Context-Rich Intelligence
Move beyond dashboards. Agentic AI needs to interpret patterns, understand buying groups, read funnel movement, and predict readiness across the full revenue cycle. This reasoning layer is what transforms AI from an analytical layer into a decision engine.
Coordinated Autonomous Action
The future isn’t single-task automation; it’s networks of agents working together on shared context to prioritize accounts, tune channels, improve pipeline quality, accelerate opportunities, strengthen forecasting, and protect revenue. Governance, permissions, auditability, and explainability form the guardrails that allow autonomy to scale without losing control.
The organizations that win the Agentic Era will not be the ones with the most AI tools; they will be the ones with the strongest operating model for autonomous intelligence.
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