A staggering 96% of businesses believe generative AI will improve customer interactions. But there's a catch: Many customer experience teams are rushing to implement it without knowing how to measure the ROI of AI for CX.
Despite the enthusiasm, only 29% of CX leaders actually track ROI—leading to expensive AI investments with unclear returns. This disconnect means companies are spending thousands—sometimes hundreds of thousands—on AI solutions without really understanding what they're getting back.
The good news? Measuring AI's ROI doesn't have to be complicated.
Here's your guide to calculating the real value of AI investments in customer experience, complete with practical examples and formulas you can start using today.
There are two critical mistakes many companies make when they start implementing AI.
The 'spray and pray' approach to AI—rolling it out everywhere without clear, role-based use cases—tends to water down its impact and kills adoption.
Buying ChatGPT licenses for your entire team might seem progressive, but without specific applications in mind, you're likely throwing money away.
In many companies, employees start using these tools initially, then usage drops dramatically after a few weeks. Without clear direction on how to apply these tools to everyday tasks, that enthusiasm quickly fades. In other cases, every employee invents their own ways of using these tools — which might be better than nothing, but makes the ROI of AI really hard to measure.
It’s usually hard for frontline employees to understand the possibilities of using generative AI unless they’re first inspired by other people.
Instead of going for a blanket approach, a better strategy is to identify 2-3 high-impact use cases first, implement AI specifically for those scenarios, measure the results, and then expand based on proven success.
You can't track improvements without knowing your starting point.
This oversight makes it impossible to quantify AI for CX's impact objectively—leaving you with anecdotal evidence at best.
Anecdotal evidence is actually not a bad thing to rely on. If people perceive their work becoming easier or faster, they’re usually right. But it’ll still only give you part of the picture, and it’s really hard to quantify.
Some agent copilot tools charge a significant amount per person (and many companies are investing in AI add-ons for a bunch of different tools at once). If your team is only 5% more efficient but you’ve doubled your overall tooling cost, that’s probably not the best investment.
These are some baseline CX metrics you can capture before any AI implementation to understand how things are going pre-implementation:
Without these anchors, you'll struggle to demonstrate positive—regardless of how effective your AI solution might be.
Quantifying the ROI of an AI solution is only complex in that it can help your team in many different ways. The biggest challenge is in targeting the one that’s most meaningful to you.
Here are seven data-driven approaches to quantifying AI's impact on your customer service operation.
One of the most immediate and measurable impacts of implementing AI comes from improving how your team accesses and utilizes internal knowledge.
Support teams often struggle with fragmented information across wikis, knowledge bases, documentation, and tribal knowledge. In many ways, this has gotten a lot worse with Slack. While Slack helps a ton with real-time communication, it’s extremely challenging to surface knowledge stored in Slack (and to know if it’s still accurate).
How can your team know if they can trust a message an engineer wrote six months ago? How many people in your team can even find that message?
Enterprise AI can dramatically help by creating a unified search interface that understands natural language questions and delivers precise answers from across your knowledge ecosystem and tech stack:
You could look at:
For instance, one contact center software company implemented Ask-AI specifically to make it easier for their agents to find information.
They tracked how many questions their agents asked in Slack over 60 days and found that it typically took a minimum of 33 minutes to get the first response in a thread. Once they connected Ask-AI to their Slack, 86% of those were answered by Ask-AI within moments.
If a typical support agent asks just five internal questions per day, and each one takes 33 minutes to get a response, that's 2.75 hours daily spent waiting for information. Multiply that across a team of 20 agents, and you're looking at up to 55 hours of lost productivity every single day.
One of the clearest ways to measure AI's impact is by comparing the performance of agents using AI tools against those who aren't.
Some metrics you could look at would be:
You would calculate ROI by looking at the productivity gain.
For instance, imagine you have a team of 16 support agents struggling with an 8% ticket backlog (1,200 tickets) and only achieving 80% SLA compliance.
Here’s what your ROI calculation might look like:
The most impactful part is that the AI implementation protected an estimated 10% of ARR that was at risk due to ticket delays and knowledge gaps. That’s the type of metric that’s a little harder to include in your ROI calculation—it’s difficult to predict how many of those customers would actually churn—but it’s a huge win for the business.
How easy is it for your team to keep your internal knowledge base up-to-date?
The vast majority of companies struggle with this, especially if they have effective and fast-paced product and engineering teams.
AI can help here too.
A less obvious but equally valuable ROI metric comes from measuring how AI helps build your knowledge base—creating a positive feedback loop for both human and AI performance. Those metrics would be:
Calculating ROI here can be a little convoluted. You can measure the average time to create an article with AI assistance and the time without. Then figure out how much time was saved by subtracting those and multiplying by the number of articles created. The formula would be:
Time saved = (average time to create KB article without AI - time with AI) × number of articles created.
For example: An enterprise SaaS company can implement AI to help agents draft knowledge base articles from successful customer interactions.
They dropped the time to create an article from 3.5 hours to 1.2 hours each—a 65% reduction. This efficiency can help them create 120 articles in their first year (up from zero), saving 276 agent hours.
This type of increase and access to new resources should also reduce the number of repeat questions, which saves additional time.
Traditional QA programs typically review only 2-5% of all customer interactions.
That’s because manually reviewing a lot of interactions is pretty time-consuming, especially if you’re doing it well. Most companies review a limited sample, and then are forced to treat that as a representative sample.
AI-powered QA can now review 100% of customer interactions.
It typically involves still creating a scorecard with the aspects you want it to check, like tone, compliance, resolution accuracy, or empathy. While the AI might not be 100% accurate (just like a human), getting 100% coverage can make a QA program significantly more effective.
It can also help you identify cases that would benefit from manual review a lot faster than random selection.
This would involve metrics like:
Measuring ROI here will also need some creativity but it’s possible.
Say a company typically rates 4% of interactions. By implementing AI-powered QA, they increase that to 100% coverage. There are two ways to measure ROI:
In CX, it’s often easy to focus on high-level metrics while overlooking the cumulative impact of small, repetitive tasks that consume disproportionate agent time and mental bandwidth.
These "micro-inefficiencies" are often insignificant individually but collectively represent a real productivity drain.
How much time do you lose doing the same three clicks to reassign a conversation every time you come across it? How much bandwidth is used across your team when multiple agents are reviewing duplicate tickets from the same customer?
It’s impossible to really quantify until you batch these improvements together.
AI excels at identifying and automating these repetitive tasks, from categorizing and routing tickets to generating standardized responses for common scenarios. Some metrics you could look at to calculate ROI would be:
Your first attempt to measure ROI here would be in assessing how much you’re able to save from automating these small tasks. The next layer to look at is how you’re using that extra available time.
For example, if you can reduce the time it takes to qualify and document customer-reported bugs, you can save time per agent. But the greater value is two-fold:
These things aren’t quite as straightforward to measure—and sometimes a correlation is the best you can do—but they’re the things that make a huge difference over time.
Most of the areas we’ve covered so far have focused on enhancing agent performance.
Self-service solutions are customer-facing, which means their value is in preventing tickets from reaching your CX team in the first place.
A modern AI for CX solution can handle complex, multi-step processes including troubleshooting, account management, product guidance, and even personalized recommendations. When it works, it works really well.
Here you’d want to look at metrics like:
You can calculate ROI by calculating your cost per ticket, and then multiplying that by the number of tickets solved by the AI. Since deflected and AI-resolved tickets don’t require human intervention, the more tickets you deflect, the more you can scale support without hiring.
Say you’re able to resolve 47% of customer inquiries from end-to-end, and you managed to reduce your response time from 8 hours to under 10 minutes.
The "hidden tax" on support operations often comes from lengthy onboarding periods and struggles to adapt to new products, features, or unexpected volume spikes.
This often results in the CX team lagging behind other teams in the business. Everyone might be celebrating because a new initiative was extremely successful, and the CX team instead feels burdened and stressed because adding and training new staff is a pretty time-consuming process.
The trade-off has always been especially painful: you can either invest extensive time and money in training, or you can accept lower quality during the learning curve.
AI can help provide real-time guidance, contextual knowledge, and decision support that dramatically accelerates time-to-proficiency while maintaining quality standards from day one. You can measure this by looking at:
Again, the simplest ROI of AI calculation here is to look at how much time you’re saving with a faster ramp up time. If your support agents reach full productivity two months sooner because AI supports them with in-the-moment answers, that’s 25% more tickets handled per year by each agent.
In addition, tools like Ask-AI also functionally upgrade the members of your team, especially if you’re also using AI to deflect and resolve simple questions from customers. This means that your Tier 1 agents now have more time to devote to more challenging Tier 2 support questions, and AI enables this because they now have all of your company’s knowledge at their fingertips (and they won’t have to ask endless questions to your subject matter experts).
But the real value for your business is in the better experience those agents provide to your customers.
If your newest agents can achieve great CSAT scores and help you maintain a better customer experience a lot faster, that will always have a knock-on effect on your retention.
It’s easy to get distracted by the technology, the marketing jargon, and the promises of the future. But the most successful AI implementations start with clear objectives and measurement frameworks.
Before choosing any AI solution, ask yourself:
AI is just a tool. It’s really powerful, but you don’t win bonus points simply for implementing AI. The real goal is to improve your customer experience in ways that drive measurable business results.
Ask-AI specializes in unifying fragmented knowledge and delivering precise answers exactly where your teams need them. We can help you identify knowledge gaps, track emerging trends, and build a more comprehensive knowledge base over time.
Reach out today to find out how Ask-AI can help transform your CX.
