The State of GTM AI 2025 report by Scale Venture Partners highlights a critical evolution in B2B go-to-market strategies, marking the transition from Phase 1 (Efficiency/Quantity) to Phase 2 (Effectiveness/Quality). While AI adoption has reached a staggering 67% of teams, the report notes that productivity gains often fail to move high-level metrics like pipeline and win rates because teams are stuck in "low-hanging fruit" use cases. To unlock true , organizations must empower to lead the strategy, hire specialized , and shift their focus toward high-value, quality-driven applications like and . This shift is essential for organizations to move beyond doing more with less and start doing better with what they have.
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Ubiquitous Adoption: Two-thirds (67%) of GTM teams now use AI regularly, representing the fastest technological adoption cycle in history.
The Productivity Paradox: While ~85% of adopters see a productivity boost, many are hitting a wall where individual time savings do not translate into department-wide revenue growth.
Phase 2 Shift: Teams focusing on Phase 2 use cases—such as AI coaching and data-driven decision-making—are 3-5x more likely to see a significant impact on pipeline and conversion rates.
RevOps as the Strategic Hub: Initiatives led by RevOps are 20% more likely to deliver meaningful business impact compared to those driven by fragmented executive mandates.
The GTM Engineer Emergence: Hiring a dedicated GTM Engineer to manage APIs, prompt design, and workflow orchestration increases the likelihood of high impact by 30%.
New ROI Metrics: Organizations must move away from blunt spend metrics toward ratio-based KPIs, specifically Net New ARR per GTM FTE, to measure the true value of AI.
Quality Over Quantity: Moving from Phase 1 (drafting emails/research) to Phase 2 (strategic analysis) is the primary differentiator between market leaders and laggards.
Detailed Summary by Topic
The Two Phases of AI Adoption
The report categorizes AI maturity into two distinct stages. Phase 1 focuses on individual productivity and volume. This involves using tools like ChatGPT for drafting outreach, summarizing meetings, and basic account research. While 92% of marketers see productivity gains here, these activities are often isolated and don't influence the broader revenue funnel.
Phase 2 shifts the focus to organizational effectiveness and quality. Here, AI is used for lead scoring, content quality assessment, and prescriptive sales coaching. For example, SDR teams in Phase 2 are 3x more likely to increase their Sales Qualified Opportunities (SQOs) because they use AI to identify the "best" leads rather than just "more" leads.
The Centrality of RevOps
RevOps is identified as the natural home for AI strategy. Because RevOps manages the intersection of data, process, and technology, they can ensure AI tools are integrated into existing workflows rather than existing as "shelfware." The report finds a 20% increase in success when RevOps leads the charge, as they are better equipped to align AI outputs with the CRM and sales pipeline.
The GTM Engineer: A New Specialized Role
As AI workflows become more technical, a talent gap has emerged. The GTM Engineer bridges the gap between Sales/Marketing and IT. This role focuses on the technical architecture of the GTM stack, including prompt engineering, vector databases, and automated workflow orchestration. Teams with this specialized resource are 30% more likely to achieve "high impact" results.
Measuring ROI through Efficiency Ratios
Traditional ROI measurements (like tool cost vs. time saved) are becoming obsolete. The report recommends using efficiency ratios, specifically:
Net New ARR / Total GTM Headcount (FTEs)
By tracking this ratio, leaders can see if AI is actually making the human workforce more effective at generating revenue, rather than just keeping them busy with automated tasks.
Data & Figures
Data Point
Value
Context
GTM AI Adoption Rate
67%
Teams using AI on a regular basis
Productivity Boost
~85%
Adopters reporting general productivity gains
Marketing Adoption
81%
Highest adoption rate among all GTM functions
RevOps Leadership Impact
+20%
Increased likelihood of business impact when led by RevOps
GTM Engineer Impact
+30%
Increased likelihood of "High Impact" results
SDR SQO Increase
3x
Likelihood of increasing SQOs in Phase 2
Marketing Pipeline Lift
Stories & Anecdotes
The Vanta Time Study CaseMark Lail, VP of GTM Ops at Vanta, conducted a "shadowing" study where he observed team members for 1 week. He discovered that top performers were using AI for call prep and meeting recaps while others were struggling with manual data entry. By standardizing these AI workflows across the team, Vanta achieved a 15% productivity lift across the entire department.
Duplo Cloud’s Persona PivotMatt Amundson (CMO) noticed that their marketing content was failing to engage. He utilized AI-generated buyer personas to critique their existing blog posts. The AI identified that the content was too generic. After refining the topics based on AI insights, page viewership increased by 50% in less than 1 month.
Fivetran's Opportunity SurgeKatherine Andruha shared how Fivetran used AI to automate the most tedious parts of SDR research. This allowed the team to produce 50% more opportunities than their previous record, illustrating how AI can break through historical volume ceilings.
References & Recommendations
Articles & Research Papers:
State of GTM AI 2025, Scale Venture Partners - The primary report analyzed.
Marketing & Sales AI Adoption Gameboards - Practical frameworks included in the report for starting AI pilots.
People Referenced:
Scott Brinker, Chiefmartec - Context: Commented on the unprecedented speed of AI ubiquity.
Jordan Crawford, Founder of Blueprint - Context: Expert on GTM Engineering and "talking to the machines."
Sydney Sloan, CMO of G2 - Context: Highlighted the need for AI to handle the modern volume of content demand.
Mark Lail, VP of GTM Ops at Vanta - Context: Provided the methodology for GTM time studies.
Tools/Platforms/Products:
ChatGPT - Context: The entry-point tool for most Phase 1 activities.
CRM (General) - Context: Highlighted as the central source of truth that AI must integrate with.
AI Buyer Personas - Context: Recommended for content optimization and resonance testing.
Vector Databases - Context: Mentioned as a technical requirement for GTM Engineers building custom solutions.
Quotes
"In 3 years, GTM AI went from zero to ubiquitous. We've never seen a technology transform GTM work this quickly." - Scott Brinker (On the pace of adoption)
"The best way to learn is to just start talking to the machines." - Jordan Crawford (On overcoming the technical barrier)
"The next phase is about doing better, not doing more." - Scale Venture Partners (Defining the core thesis of Phase 2)
"Instead of just talking about what's happening, we can now be prescriptive about what to do next." - Neil Harrington (On the power of AI call analysis)
Speakers & Credentials
Scott Brinker: Chiefmartec; widely considered the father of the MarTech landscape.
Jordan Crawford: Founder of Blueprint; a leading voice in GTM Engineering and automation.
Sydney Sloan: CMO of G2; veteran marketing leader focused on buyer behavior and tech stacks.
Mark Lail: VP of GTM Ops at Vanta; specialist in operationalizing AI for scale.
Katherine Andruha: Sales Leader (ex-Fivetran); expert in SDR productivity and outbound strategy.
Actionable Next Steps
Launch a GTM Time Study: Have RevOps shadow Sales and Marketing reps for 1 week to identify "time sinks" that can be automated via Phase 1AI tools.
Formalize the GTM Engineer Role: Either hire or designate a technical resource to manage APIs, data flows, and prompt libraries to ensure AI initiatives are scalable.
Audit for Phase 2 Opportunities: Move beyond simple "drafting" use cases; identify 3 areas where AI can provide prescriptive quality (e.g., scoring leads or coaching reps on call transcripts).
Update Executive Reporting: Transition board-level reporting to include Net New ARR per GTM FTE to demonstrate the ROI of AI investments in terms of human leverage.
Centralize AI Ownership: Move AI budget and strategy under RevOps to prevent the proliferation of redundant, disconnected tools.
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