The GTM Efficiency Gap

The AI, Applied data indicates that AI is making the large majority of GTM teams more productive, but positive impact on customer ROI metrics remains a minority outcome. The lack of customer ROI appears to be related to shallow GTM data integration.

In our view, until AI expands beyond individual internal use cases and becomes more deeply embedded in GTM data and workflows touching the customer, positive impacts on acquisition costs, customer net retention, and customer lifetime value will likely struggle to tip over to the majority.

For GTM teams surveyed, AI appears to be working better on one side of the GTM ledger than the other. GTM teams report improvement to team efficiency as AI use becomes widespread at the individual and team level. Overall GTM cost and revenue ROI metrics, while still lagging behind productivity improvement, are also gaining ground.

The gap is between GTM teams internally and their customers. Customer metrics are proving harder to impact. Fewer than 50% of GTM decision makers report a positive impact on net revenue retention, LTV-to-CAC, or revenue per rep — the metrics that sit closest to the customer relationship and the P&L.

GTM decision makers appear to be behind their Tech counterparts when it comes to AI in production (see Chapter 4: The Agent Engineer). AI coverage of historically manual GTM workflows is a good way to measure the depth of AI adoption for this function — and the numbers indicate a relatively shallow level of deployment thus far. This is likely the structural explanation for the gap between GTM efficiency gains and customer ROI.

The good news: 99% of GTM decision makers report at least some level of AI dependence — they appear to be moving toward more coverage, a thesis we will watch in upcoming waves.

GTM use cases tied to data and near real-time decisioning are lagging in broad adoption. We believe this is the next phase of AI for GTM teams — leading to the rise of the “GTM engineer” role and the focus on upskilling, findings we discuss in Chapter 8.

Typically we see Runners much further ahead of Walkers in all dimensions of this research. This is less the case with customer-centric and deeper data-driven GTM use cases. Runners are a modest +12 pts ahead of Walkers on broad adoption of AI for customer retention and +11 pts for lead scoring; conversely, the sales and marketing messaging gaps are +26 pts.

Even Runners may be suffering from broad AI adoption at shallow integration depth for customer-centric use cases.

PLG dropped 19 points and is no longer the primary GTM motion cited by GTM decision makers. PLG’s share appears to have been absorbed across multiple GTM motions — account-based, outbound, inbound, and channel — all of which benefit from the GTM productivity gains in sales messaging and content creation increasingly visible in these Wave 3 benchmarks. We believe the higher-touch impact of non-PLG motions is being unlocked at scale by AI agents — potentially an agent-led GTM motion that this research does not capture yet.

As B2B software increasingly delivers value through agentic features that customers discover and activate themselves, the acquisition and expansion dynamics look less like classic PLG and more like a new GTM motion. Wave 4 will explore a potential emerging AI-agent-led GTM motion explicitly.

Forward-looking confidence appears to be strong. Two-thirds of GTM decision makers expect agentic AI to materially change how their function operates within the next year. This confidence crosses all GTM decision-maker segments we analyzed, including company-size and AI-maturity segments. The gap between what GTM decision makers expect and what the metrics currently show will be a closely watched benchmark — will we see expectations meet production-level outcomes in Wave 4?