I’ll admit: When I first started using AI at work, I was just going through the motions. Sure, ChatGPT could punch up my writing and make me sound wittier than I truly am, but what was that doing for me, really?
Then my editor said something that flipped my approach to using AI: “The goal isn’t to just use AI—it’s to use AI to speed things up or make things better.” So I put it to the test. I started tracking how long it took me to edit an article on my own versus with help from AI. The difference? Hours.
And I’m not the only one running those experiments. At Zapier, 89% of our team uses AI in their day-to-day work—writing code, tracking brand mentions across social platforms, analyzing customer sentiment—all in the name of working smarter.
But that brings up a bigger question: how do you know if AI is actually making a difference?
We talked to a range of experts—from marketing leaders to engineers—about how they track AI performance in their work. The answer wasn’t a single tell-all metric but a series of creative, practical KPIs that go far beyond model accuracy or response speed.
Here are the AI performance metrics real teams are using to measure what matters.
Table of contents:
Resolution gap index
Getting an answer to your customer support question isn’t the same as getting the right answer. That’s why Syed Balki, founder of WPBeginner, keeps a close eye on what he calls the resolution gap index (RGI)—it calculates the difference between what the AI initially recommends and what ultimately resolves his customers’ issues.
“Our RGI revealed that our models were technically accurate but contextually incomplete,” says Syed. “For example, our customer service AI would correctly identify product issues but miss important contextual factors like customer tier or previous interactions.”
Uncovering this gap was fundamental in helping Syed reshape his AI strategy: “Rather than chasing higher accuracy on isolated questions, we invested in contextual awareness and conversation memory. We also implemented a system that incorporates relationship history and customer-specific parameters into each interaction.”
And this pivot has paid off. “Our RGI has dramatically decreased,” shares Syed. “Which indicates that our AI is becoming more effective over time.”
Conversions from AI recommendations
While RGI focuses on how AI resolves individual support issues, there are other AI metrics that give you a big-picture view of how AI performance translates into broader business outcomes.
Here’s an example from Viraj Lele, industrial engineer and business unit advisor at DHL: “We map model accuracy directly to key business outcomes—such as increased revenue, customer retention, and operational efficiency.” He continues: “Take the AI-powered recommendation engine we use in our eCommerce platform, for example. We created a new AI metric to measure how accurately AI-recommended products translated into sales: conversions from AI recommendations.”
“This metric allowed us to quantify the real-world success of our AI strategy,” Viraj explains. “We noticed that even small gains in model accuracy correlated with a 12% lift in average order value and a 15% boost in repeat purchase rate.”
David Pickard, global CEO of Phonexa, takes a similar approach, focusing not just on whether AI drives conversions but on the quality of those conversions. “We developed a metric called the conversion quality score, which measures lead volume and high-intent conversions generated by AI-optimized campaigns.”
David continues: “Tracking this metric has helped us refine our overall AI strategy significantly. For example, we noticed that some high-volume campaigns looked strong on paper but underperformed massively in terms of actual client ROI. So we adjusted our AI training inputs and performance expectations to better align with what really matters: business outcomes.”
Insight adoption rate
If an AI model generates insights but no one uses them, did it really do its job? That’s what Tyler Butler, founder of Collaboration For Good, set out to measure.
“As a consulting firm dedicated to sustainability, corporate responsibility, and Environmental, Social, and Governance (ESG) strategies, we use AI to help clients identify emerging risks, predict trends, and uncover insights that might otherwise be overlooked,” Tyler explains.
But Tyler says using AI to identify insights is just the first step: “The true measure of success lies in how actionable and relevant these insights are to our clients.” That’s why one AI metric he monitors is the insight adoption rate: “It’s the percentage of AI-generated insights that are successfully integrated into our clients’ corporate responsibility and ESG strategies.”
Tyler says this AI metric goes beyond assessing AI performance. “It assesses the real-world value and impact that AI insights bring to our clients’ decision-making.”
From there, Tyler uses the adoption rate to help refine how he uses AI. “We use AI to not only provide relevant, actionable insights,” he says, “but also communicate them in a way that’s easy for our clients to understand.”
Prompt-to-result satisfaction score
Like many others, Kinga Edwards, CEO of Brainy Bees, uses AI to help develop content. And like those others, Kinga knows you need to constantly refine your AI prompts to get the output you’re looking for. Which can be a massive time suck.
“Since we rely on AI to support content work,” Kinga says, “we need to measure how useful the output actually is and if it helps move the task or project forward. So we implemented a system called the prompt-to-result satisfaction score (P2RSS).”
Kinga explains how it works: “After each AI session, team members rate the final output on a scale of one to five—one being highly dissatisfied and five being highly satisfied—based on how close it was to what they needed and how quickly they achieved the desired result. We also log how many prompt attempts it took to get there.”
When Kinga’s team initially started tracking their P2RSS, the average was 2.7. “It revealed that we were spending a lot of time having to tinker with the output.” But the team used those learnings to change how they prompt and the tools they use for different tasks. “Today, our average score is 4.3.”
Kinga shares one final takeaway about the value of tracking this AI metric: “It’s a simple way to keep everyone accountable for how they use AI and ensure it’s actually helping us to save time.”
AI-to-human completion ratio
AI agents are ridiculously impressive, and they’re getting better every day. Folks at Zapier have agents that enrich lead data, organize Gmail messages based on priority, and summarize Slack threads that run 100+ responses deep—all autonomously.
John Xie, co-founder and CEO of Taskade, is measuring that autonomy: “One key AI metric we track is the AI-to-human completion ratio: the percentage of tasks, messages, or project actions completed autonomously by AI agents versus human users. This helps us understand how effective our AI agents are at real execution, not just suggesting ideas or generating drafts.”
Tracking this metric gives John’s team valuable insights into where agents were falling short—and how to close the gap. “Tracking this metric showed us where agents were getting stuck or requiring too much human intervention. For example, if the ratio drops in a specific workflow, we dig into the agent’s reasoning steps, prompt quality, or missing context. It pushed us to improve agent memory, tool access, and system prompts, leading to more autonomous execution and better user outcomes.”
Ethics and bias score
In my professional and personal circles, you can’t talk about AI without also discussing the ethical concerns around using AI.
Colleen Barry, head of marketing at Ketch (a privacy management software company), is no stranger to the ethical discussion. “Ethics and bias are more than just technical concerns—they’re at the heart of how we build trust with our users and customers. That’s why we actively track our ethics and bias score as a key AI success metric.”
Here’s how it works: “This score helps us measure whether our AI models treat different customer groups fairly and without unintended bias, especially when it comes to personalized messaging and consent experiences. For example, if we notice that certain demographics are receiving fewer opt-in opportunities or seeing different messaging, that’s a red flag we investigate right away.” From there, the team uses its score to refine the AI to make it behave in a fair, compliant, and inclusive way.
Colleen shares how monitoring the AI’s ethics and bias score has also been valuable for the brand: “Our customers want to see that we walk the talk when it comes to ethical data use.”
“Ultimately,” Colleen says, “this metric keeps our marketing practices aligned with our core values and ensures our AI efforts are helping, not harming, our relationships with users.”
Don’t let your AI live in a silo
Tracking AI metrics to optimize your AI use is just one part of the equation. To really get value from your AI tools, it needs to be part of something bigger—something connected. With Zapier, you can orchestrate end-to-end AI workflows that don’t just generate insights or content but actually act on them.
For example, you can build a lead form using Zapier Interfaces and store that data in Zapier Tables. Then, when a new lead comes in, Zapier Agents can automatically follow up with the lead, enrich their data for human follow-up, and route all of this information to the right tools.
So yes, measure what matters. But then take the next step: build something that moves with it.
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