February 25, 2026 • 12 min read
11 New Contact Center Metrics for 2026
Director of Content & Market Research
February 25, 2026
For decades, contact centers have leveraged the same metrics to track customer, employee, and business outcomes.
Customer metrics include customer satisfaction (CSAT), a Net Promoter Score (NPS), a customer effort score (CES), sentiment tracking, and AI escalation rates.
Meanwhile, employee metrics typically comprise absence and attrition rates, agent occupancy, and perhaps a staff engagement score, based on periodic surveys.
Lastly, service leaders also track business outcomes, such as service level, cost per contact, shrinkage, average handling time (AHT), and repeat contacts.
While there are many others, that core mix hadn’t changed all that much, until recently.
With advancements in AI analytics, data centralization, and measurement techniques, contact center reporting strategies are advancing, and new metrics are surfacing.
In light of this trend, here are 11 new contact center metrics to watch out for in 2026.
1. Mean Opinion Score (MOS)
A Mean Opinion Score (MOS) measures audio quality on a scale of five. Once it drops below 4.2 during service calls, customers notice issues like jitter, lag, or distortion.
“The team at Operata and I looked into this, and we found that agents with consistently low MOS scores were 50% more likely to leave within three months,” said Luke Jamieson, CX Evangelist.
That’s fascinating because it highlights how attrition isn’t necessarily a behavioral or coaching issue. Often, it comes down to:
- Old laptops
- Poor CPU performance
- Too many tabs open
- System lag
All of this leads to frustration: customers repeat themselves, communication breaks down, agents get stressed, and eventually they leave.
“Replacing a laptop might cost $2,000. Replacing an agent and taking them through recruitment, onboarding, and training can cost $15,000 or more.”
So, instead of automatically attributing attrition to “DNA issues,” more contact centers may start to track MOS to discover if it’s actually a desk issue and take action.
2. Case Complexity
As virtual agents handle simpler, transactional customer queries, some contact centers are tracking case complexity, since human agents are increasingly left to handle more difficult work.
The idea is to tag a customer case before it reaches an agent as either high-, medium-, or low- complexity. From there, the contact center actively manages that mix, so agents don’t receive too many tricky contacts in a row.
Service leaders can monitor case complexity by tracking various signals, such as:
- The customer’s stated intent
- The customer’s sentiment (as captured in the call queue)
- The complexity of the customer’s previous interactions
AI agents can combine these signals into a composite score, enabling the routing algorithm to assign each agent a balanced mix of customer inquiries across varying complexity levels, helping reduce overall stress.
Moreover, contact centers may deliberately feed new agents simpler queries to build confidence and lower the risk of early attrition.
3. AI License Adoption
Contact center agents often sidestep new solutions implemented to enhance their experience. Indeed, Gartner research shows that 45% ignore them altogether, sticking to the old, trusted ways of working.
Of course, this underscores the importance of involving agents earlier in the procurement process for tools designed to augment their roles. Nevertheless, tracking adoption is key, and there are two core metrics for doing so:
- Percentage of agents actively using AI features versus total licensed users
- Frequency of usage per agent (daily, weekly, per interaction)
These measures signal real engagement and, if they lag, that may indicate training gaps, workflow frictions, or a deeper mistrust of the AI.
Additionally, these metrics correlate with ROI, and underused licenses mean wasted cost. Meanwhile, high adoption paired with performance metrics shows true value.
Lastly, adoption interacts with other metrics, such as task time reduction and first contact resolution (FCR). If agents don’t use AI, contact centers won’t be able to move the needle on these outcomes.
4. AI-Assisted Task Time Reduction
More contact centers are implementing agent assist solutions. In doing so, they're offering agents next best actions and guidance cards for real-time support. They're also auto-drafting entire customer replies and summarizations for the CRM.
As these solutions become commonplace, more contact centers will track AI-Assisted Task Time Reduction to monitor their effectiveness. This metric considers:
- Average task handling time with and without AI assistance
- Time saved per interaction when AI provides suggestions or automates certain steps
Shorter task times contribute to operational efficiency and ROI, as fewer hours are needed for the same work, which translates into cost savings.
The metric also sheds light on agent engagement and, ultimately, customer experience as agents can spend more quality time on complex cases.
5. AI Coaching Success
Tracking how often agents engage with AI coaching suggestions shows whether AI is actually influencing agent behavior and improving outcomes.
Three distinct measures to look out for here are:
- Number of AI recommendations accepted versus ignored
- Frequency of agent interactions with AI coaching prompts
- Feedback submission from agents about AI suggestions
High engagement in the feedback loop signals agent trust in AI, effective knowledge transfer, and opportunities for model refinement.
Low engagement may indicate mistrust or alert fatigue, poorly tailored coaching suggestions, and workflow friction preventing effective adoption
Ultimately, monitoring this metric helps brands optimize AI performance and agent efficiency, not just measure usage.
6. AI-Generated Error Rates
Contact centers should track the frequency of AI mistakes and mis-recommendations across both agent-facing and customer-facing AI.
The latter has always proven tricky. However, recent advances in conversational intelligence solutions have allowed brands to track:
- Incorrect answers provided to customers
- Failed task completions due to AI errors
- Escalations triggered because AI gave the wrong guidance
Across these measures, high error rates erode customer trust, increase human workload due to corrections, and inflate operating costs from unnecessary escalations.
As such, it’s a key metric to track, as contact centers may iterate on prompts, knowledge bases, and model training.
“Error rate ties into broader success metrics like containment, customer trust index, and AI adoption,” added Josh Streets, Founder & CEO of QX Now. “Lower error rates correlate with higher trust and smoother AI-assisted workflows.”
7. AI Token Usage
Token usage has fast become a critical metric for the next generation of virtual agents.
Why? Because large language models (LLMs) charge per token (input + output). Longer prompts, verbose answers, or repeated retries increase token consumption and therefore cost.
As such, it should sit alongside the following metrics to better quantify the ROI of customer-facing AI:
- Cost per AI interaction
- Cost per successful resolution
- Cost per escalation
High token usage may indicate poor conversation design, overly long responses, and inefficient knowledge retrieval.
Finally, this is a particularly fascinating metric to consider alongside containment. Why? Because low token usage with high containment and trust indicates efficient AI. Meanwhile, high token usage with low containment equates to expensive experimentation.
8. AI Recommendation Success Rates
Brands typically measure conversion rates, lead qualification, and revenue per interaction to monitor how well AI makes recommendations to customers.
However, they should also track what happens after the sale. Specifically:
- Are customers returning products recommended by AI?
- Are refund rates higher when AI drives the recommendation versus a human?
- Are AI-suggested upsells resulting in regret or churn?
Why? Because short-term success metrics can mask long-term experience failures.
For instance, even if an AI agent drives a higher basket size, it could still recommend poorly matched products or over-promise, resulting in higher return rates, increased refunds, and lower lifetime value.
As such, contact centers should track these outcomes to monitor AI Recommendation Success Rates.
“Refund and return rates are becoming key quality-of-recommendation indicators. They are not just operational metrics anymore.”
9. Personalization Accuracy (& Emotional Mimicry)
Brands often measure whether their self-service systems deliver an appropriate amount of personalization via click-through, engagement, and conversion rates.
However, thanks to advances in conversational intelligence solutions, they can go deeper, monitoring whether AI misclassifies preferences or delivers inappropriate suggestions.
Contact centers may cluster these indicators within a broader Personalization Accuracy Index, which may also comprise emotional mimicry as a metric.
Emotional mimicry monitors how well virtual agent solutions track emotional cues in real time to adjust tone, pacing, or language dynamically.
Critically, that adds another layer of personalization, as the agent attempts to respond appropriately to the customer’s emotional state.
For instance, if a customer appears frustrated, the AI might slow down, use reassurance statements, or escalate sooner. Alternatively, if a customer appears confused, the AI might simplify explanations.
However, AI won’t necessarily do this well. Given this, emotional mimicry considers:
- Whether the AI recognized the customer’s emotional state
- Whether it responded appropriately
- Whether the interaction increased or eroded trust
As contact centers start leveraging avatars to offer video self-service, emotional mimicry measures may expand, monitoring how well the avatar adjusts to facial cues.
“Some CCaaS platforms are beginning to explore emotional mimicry, considering whether an AI avatar can correctly interpret facial expressions during a video interaction and respond appropriately. That may sound futuristic, but it’s already being tested.”
10. AI Trust Index
Organizations deploying AI, especially without a human in the loop, need a measure more robust than CSAT, NPS, or containment.
Given this, Streets and Jamieson recommended the creation of a weighted composite metric, built from multiple AI and experience signals, that brands can follow as their North Star.
One example is an AI Trust Index, which rolls up:
- Accuracy
- Bias mitigation
- Hallucination rate
- Escalation effectiveness
- Transparency
- Emotional alignment
- Quality of handoff
- Response appropriateness
With such a metric, contact centers can account for the new risks of AI - such as fabricated answers, subtle bias, inconsistent tone, and overconfident errors - while still monitoring more traditional measures of satisfaction, speed, and effort.
Additionally, this example makes trust measurable, not just a soft concept, sitting at the intersection of risk management, compliance, brand protection, AI governance, and long-term customer value. That’s board-level territory.
11. Predictive Net Promoter Score
The use of synthetic data to predict outcomes is an emerging field that’s starting to influence the contact center metric mix.
For instance, some solution providers have touted a Predictive Net Promoter Score (xNPS), which saves service teams from distributing post-contact surveys to track NPS.
Such solutions identify what drives NPS up and down, tagging each customer interaction with a predicted score, increasing the sample size while preventing survey fatigue.
Yet, the applications for xNPS extend beyond enhancing traditional reporting processes.
“What excites me is the possibility to use synthetic data to predict what a customer’s Net Promoter Score (NPS) might be if we guide them across different journeys, and having that influence the experience that then follows.”
Already, some brands are training AI customers, based on key personas, to simulate experiences as they test self-service applications. In doing so, they generate synthetic data that helps them isolate pain points before customers do.
However, the next frontier lies in leveraging individual customer data to predict the experience most likely to drive specific outcomes.
Trends Supporting the Adoption of These Metrics
The emergence of new metrics is the result of several shifts in measuring methodologies, data strategies, and AI advancements. Here are five key examples.
Lighthouse Metrics Emerge
The AI Trust Index is an example of a “lighthouse metric”, as coined by Jamieson. It takes a key outcome that stakeholders value, pools metrics that contribute to the outcome, and creates a composite score to track.
That single score is much more powerful than presenting a long list of disconnected stats. Why? Because it makes it easier for leadership to understand the direction of travel and justify investments that will move the needle.
“Right now, most contact center metrics are very binary, pass/fail. But those numbers don’t mean much to people outside of customer service. A lighthouse metric would roll multiple inputs into one meaningful indicator that everyone understands and inspires action.”
Contact Center Metrics Extend Beyond Speed & Accuracy
While some classic speed- and accuracy-based contact center metrics are here to stay, AI will drive far more real-time insight into emotional and experiential outcomes.
In doing so, it will help measure whether customers felt understood, whether their emotional state was recognized, and whether trust increased during the interaction.
Yet, beyond customer interactions, AI metrics will also add new insight into the success of broader customer service initiatives and strategies. Consider:
- Knowledge article effectiveness
- AI-driven ROI tied to finance
- Recommendation accuracy that affects marketing or returns
- Model performance benchmarks
There are likely dozens, even hundreds, of metrics that contact centers aren’t even considering today but may benefit from as they expand their AI transformation strategies.
Contact Centers Advance Their Data Infrastructure
Advanced contact centers are consolidating data into centralized lakes or warehouses for analysis. Meanwhile, AI, BI, and analytics teams are increasingly integrated.
“In some organizations, workforce management analysts are being absorbed into centralized AI or data science teams. Instead of reporting solely into contact center leadership, they now support broader AI initiatives.”
Ultimately, this signals a structural shift in how data and performance measurement are handled across the enterprise.
Benchmarking Platforms Come to the Fore
NPS gained traction because it created consistency and gave organizations a North Star to benchmark across industries. Yet, the value of benchmarking didn’t extend much further.
Now, in the age of AI, benchmarking platforms, like NiCE Industry Benchmarking, are emerging. These compare AI models, rank performance, and establish standards, according to Streets.
These standards span multiple use cases and industries, helping contact centers define clear, measurable outcomes before deploying AI.
New AI Deployments Breed New Metrics
According to a 2025 McKinsey study, one-third of high-performing organizations have committed over 20% of their digital budgets to AI. Much of that is funnelling through to the contact center.
As such, contact center AI innovation is accelerating, and service leaders aren’t only applying the same old metrics. Instead, they’re starting with the AI use cases and considering:
- What outcome are we trying to drive, efficiency or effectiveness?
- What is our brand promise?
- What experience are we committing to customers?
From there, they identify a mix of traditional measures (handle time, CSAT, NPS, effort) and AI-specific metrics that support those goals.
Contact Center Metrics in 2026: Some Final Words of Wisdom
“AI without the right metrics is just experimental curiosity,” Streets concluded.
To this point, as contact centers apply AI, they must define success criteria that tie back to their brand promise and the customer experiences they want to deliver.
Ultimately, those who fail to do so won’t know whether they’ve transformed their contact center operations or simply experimented.