A more accurate measurement system can be intimidating because it reveals uncomfortable truths. It may show that seemingly successful activities, like generating high MQL volume, had a negligible impact on actual pipeline. Leaders must prepare to face this exposure to truly improve performance.

The CRO of Personio frames the ultimate question for AI's impact on GTM not as incremental efficiency, but as transformational growth. The true north star is whether the company can double its business with existing headcount, shifting the default from hiring more people to solving problems with AI first.

Instead of marketing and sales running separate races with siloed KPIs, a modern GTM model measures the entire journey like a relay. Both teams are measured on how efficiently accounts move through the funnel, focusing on the quality of handoffs and collaborative impact on velocity.

To build a business case for better analytics, split your pipeline into two buckets: high-intent sources (e.g., demo requests) and everything else. Analyzing the performance gap in win rates, velocity, and conversion reveals the dollar value of closing that gap through improved visibility.

A motion (e.g., PLG) contributing 20% of revenue might seem successful. However, elite teams analyze its efficiency—the conversion rate and cost to acquire that revenue. A high-cost, low-conversion motion is a significant drain, even if its top-line contribution appears acceptable on paper.

The primary ROI of sales AI isn't just saved time, but the reallocation of that time. Evaluate and justify AI tools based on their ability to maximize Customer Facing Time (CFT), as this directly increases both the quantity and quality of customer interactions, leading to better performance.

Traditional product metrics like DAU are meaningless for autonomous AI agents that operate without user interaction. Product teams must redefine success by focusing on tangible business outcomes. Instead of tracking agent usage, measure "support tickets automatically closed" or "workflows completed."

While AI tools dramatically increase content production speed, true ROI is not measured in output. Leaders should track incremental engagement, conversion lift, and revenue per message. An often overlooked KPI is brand consistency—how often content passes governance checks on the first try.

While pipeline is important, the real signal of a successful AI-driven business is the depth of customer engagement. Are customers expanding beyond their initial use case? Are developers integrating your tool into core workflows? Are communities actively discussing you? These leading indicators show a stronger foundation than top-of-funnel metrics alone.