Quick summary: CS teams are using AI for administrative tasks, but most lack the ability to track performance or tie it to revenue outcomes. Start measuring AI impact to learn how it affects your team and identify the best areas for AI investment.
Almost all CS and revenue leaders are using AI to automate administrative and manual work for their teams – such as logging tasks, summarizing calls, and conducting research for accounts. But the majority of CS leaders cannot measure the impact of those automations: they can’t show that AI actually works, and they still require manual approvals for every AI recommendation that hits their desk.
That’s according to ChurnZero’s recent 2026 Customer Success Leadership Study, which surveyed nearly 600 CS and revenue leaders to see how AI is being used for their teams.
Several challenges arise when teams deploy AI but don’t report on its effectiveness. For one thing, you can’t strategically use AI to improve your processes if you don’t know the business impact and you’re just going off a feeling. For another, teams that can’t measure impact don’t trust AI to make decisions and still rely heavily on human intervention. And finally, it’s difficult to justify AI spend if you can’t show what you’re doing with it.
CS AI measurement by the numbers
The study’s responses from CS and revenue leaders about AI measurement revealed how AI is currently measured (or not) by CS teams:
- Nearly two-thirds (63%) of CS leaders have no consistent method to track AI performance.
- Less than one-quarter (21%) of CS leaders can tie AI to revenue outcomes, compared to more than half (57%) of sales leaders.
- While NRR has remained flat overall for SaaS companies over the past three years, nearly two-thirds (65%) of AI leaders (organizations with mature AI deployments) reported NRR of 100% or greater, compared with 39% of AI laggards (organizations behind the curve on AI deployments).
The responses tell us that even though almost everyone is using AI, CS teams are falling short when it comes to measuring the results.
How to start measuring your AI impact.
The good news is that you don’t have to start measuring everything, everywhere, all at once. Use these strategies to start chipping away at your AI blind spots so you’re better positioned to show what AI can do for your team:
Start asking the right questions.
Before you start gathering data or building reports, ask yourself a few key questions that can guide your AI measurement:
- Sales leaders are more likely than CS leaders to show how AI impacted revenue. What metrics does sales track that you currently don’t?
- What AI automations do you currently have in place, and how would you define their success?
- What is AI currently not able to automate for your team because you have no idea if it will work?
- Where does AI currently reside in your budget, and who has to review your AI spend and approve or deny it?
Automate one workflow and see what happens.
You can build a process for measuring AI impact by looking at a current or new AI automation used by your team:
- Take inventory of the current workflows that AI manages for your team right now, or find one that you’re getting ready to introduce.
- Choose one of those workflows and define the metric you would use to measure its success, then start tracking it.
- Check the numbers on a quarterly basis to see if anything changed, and continue to monitor.
- Replicate this measurement process as you begin to automate more tasks.
Establish baseline performance metrics before introducing AI.
Before you create a new workflow or automation, you need to know how the process is currently performing so you can measure if AI made it better or worse. Define what you’re trying to accomplish – such as shortened time-to-value, higher renewal rates, or a greater frequency of expansion calls – and identify the metric that will measure this goal. Now you have a method for assessing your AI impact over time.
Feeling overwhelmed with the idea of complex reporting? Efficiency metrics are one of the easiest places to start. See how many hours it takes your team to perform a manual task, then compare that to how fast AI can accomplish the exact same work.
Introduce AI into your budget.
Many teams build their own AI workflows using the tools provided by the company, but don’t maintain an AI budget. Including your AI spend as a line item in your budget creates a need for greater accountability. Now that you’re officially spending your organization’s money on a project, you have a responsibility to demonstrate how the money was spent and determine if the results justified the expense.
In addition, tracking your AI budget and breaking it out by project can help you strategically deploy AI where it’s most effective. You can determine which projects were successful and worth further investment, and which ones didn’t work out and should be scaled back.
Teams that measure AI can work more strategically.
The biggest risk for many CS teams is that they are using AI blindly, without any way to measure its impact. When CS leaders can’t track performance or tie AI to revenue, they’re relying on guesswork instead of reality. This means teams are four times more likely to require manual sign-offs for every AI recommendation, keeping your staff bogged down with less time to focus on more strategic work.
By defining your baseline metrics, rolling out reporting one workflow at a time, and introducing AI into your budget, you can create a framework for using AI strategically in a way that drives value for your team and the business at large.
