Call Center Quality Monitoring Guide: AI, KPIs, 10 Tools (2026)

Delivering consistently great customer experiences isn’t easy. Every interaction matters, yet ensuring customers leave satisfied after every conversation with your support team is a challenge. As your contact center grows, quality assurance becomes even more critical and complex. When thousands of customers reach out each day, outdated, manual QA methods simply can’t keep up.

That’s where AI-powered call center quality monitoring changes the game. With the market projected to grow from USD 47.3 billion in 2026 to USD 72.31 billion by 2035, it’s clear this technology is becoming essential.

In this guide, you’ll discover:

• What call center quality monitoring is
• How call center quality monitoring software improves CX
• How AI and automation have changed quality monitoring
• 10 tools for call center quality monitoring in 2026

What is call center quality monitoring?

Call center quality monitoring is the structured process of reviewing and evaluating customer interactions—such as phone calls, chats, or emails—to ensure agents meet defined standards for service quality, accuracy, and professionalism.

It’s essentially how a call center checks whether agents are doing the job the right way and how they can improve. You’ve probably heard an automatic message before talking to a customer support agent stating that a call is recorded for quality purposes. Most interactions with customers are recorded to ensure compliance and quality.

Here are some ways call centers evaluate customer interactions:

• Manual reviews – supervisors or QA analysts listen to recorded calls or read chats
• Live monitoring – listening in real time
• Speech and text analytics – AI tools scan 100% of interactions for trends, risks, or keywords
• Customer feedback – surveys (CSAT, NPS) tied back to agent performance

Setting KPIs helps evaluate communication effectively. When you know what you want to measure and focus on, you know what to look for during the evaluation.

How does call center quality monitoring improve KPIs?

Call center quality monitoring improves KPIs by turning real customer interactions into actionable behavior change. As the saying goes, “You can’t improve what you can’t measure,,” which is best applied to call center quality assurance. 

When you monitor how your team handles customer inquiries and the experience customers have with your business, you can identify opportunities and gaps. But the list of benefits goes on—let’s take a look.

• More efficient calls: Call center quality assurance software identifies behaviors and process gaps that slow calls down, such as unnecessary scripting, poor probing, or incomplete resolutions. Coaching based on monitoring results focuses on eliminating these issues, leading to lower handle time and fewer repeat contacts without sacrificing quality. And with the right tools, you can expect to cut your resolution times even further. Lyft, a ridesharing company, offers a good example. After integrating an AI-powered quality monitoring system, the company successfully reduced the average customer service resolution time by 87% and now resolves thousands of customer requests daily.
• Enhanced agent performance: Quality monitoring sets clear, consistent expectations for what good performance looks like. Agents improve faster because feedback is specific, objective, and directly tied to customer outcomes. Let’s take a look at another example, this time focused on productivity increases. Games24x7, a popular gaming company, implemented an AI-powered quality sampling platform to automate quality assurance processes. Soon after, results followed: the company increased employee productivity by 20%, as teams were able to handle more inquiries in less time.
• Personalized coaching: Quality data highlights individual strengths and weaknesses, allowing team leads and managers to tailor feedback to each agent rather than relying on generic training. This targeted approach accelerates skill development and improves long-term retention of best practices.
• Reduced agent turnover: Retaining call center employees can be a challenge, with the industry’s turnover rate sometimes reaching as high as 60%. When monitoring is transparent and developmental, agents perceive feedback as fair and supportive rather than punitive. Clear expectations and meaningful coaching reduce stress, increase engagement, and lower attrition.

How does call center quality monitoring improve CX?

Quality monitoring is your first step to improving call center customer service. When you know what works and what could be better, you can adjust your communication to improve how your customers experience your business. And with the right tools, you can achieve it faster, while cutting costs.

• Enhanced support quality: Studies show that AI assistance boosts productivity by 15% on average, especially for less experienced workers. Quality monitoring ensures agents follow best-practice behaviors like active listening, empathy, and accurate resolution. Consistent evaluation and feedback raise the overall standard of service customers receive across every interaction. 
• Higher convenience: By identifying friction points such as repeated questions, long holds, or unresolved issues, quality monitoring helps streamline interactions. Customers get faster, smoother resolutions with less effort on their part.
• More personalization: It’s not enough to add a name to your email subject line. Personalization is getting, well… more personalized. But it’s challenging without the right tools. Research on AI in retail found that 25% of shoppers want AI to personalize their experience. Monitoring highlights how well agents tailor conversations based on customer context, history, and intent. Coaching reinforces personalization behaviors, leading to interactions that feel relevant rather than scripted.
• Greater consistency across channels: Call center quality monitoring software applies shared standards across calls, chat, and email, reducing inconsistencies in the customer experience. Customers receive the same level of service regardless of channel or agent.
• Increased trust and confidence: We surveyed 1,000 people, and it wasn’t surprising that only 42% feel their issues are truly resolved after contacting a business. Accurate information, compliant handling, and a professional tone—reinforced through quality monitoring—build support that doesn’t disappoint. Consistently positive experiences increase confidence in the brand and willingness to return.

How have AI and automation changed quality monitoring?

Back in the day, companies could evaluate only so many customer interactions. You’d select a few calls or text interactions and have someone manually go through the transcript, checking it against the criteria. AI and automation have shifted quality monitoring from manual spot-checking to continuous, insight-driven improvement. 

The biggest change is:

• Scale
• Speed
• Objectivity

First, AI-powered call center quality monitoring software enables evaluation of 100% of interactions, not just a small sample. Instead of reviewing 1–3% of calls, speech and text analytics automatically scan every conversation to detect:

• Compliance issues
• Sentiment
• Silence
• Interruptions
• Resolution signals

Second, automation turns monitoring from reactive to real-time and predictive. AI can flag risk during or immediately after an interaction—such as escalation likelihood or compliance breaches—allowing faster intervention. Teams no longer wait weeks for QA results to take action.

Third, quality monitoring is now more consistent and objective. Automated scoring applies the same criteria to every interaction, reducing reviewer bias and variability between evaluators. Human QA shifts from scoring mechanics to coaching, judgment, and edge cases.

6 best practices for using AI in call center quality monitoring

AI is a game-changer in reducing manual work and helping you scale your business. Let’s discuss practical steps you can take today to begin using AI in call center quality monitoring.

1. Identify key opportunities

Start by defining what “quality” actually means for your operation. Every business is different, and so should be your goals. You might look into increasing first-resolution rates or reducing wait times, and AI can help you identify the areas that need more work.

You can use AI-powered call center quality assurance software to surface high-impact behaviors that most strongly influence CX and performance KPIs, such as:

• Missed resolutions
• Compliance risk
• Low-empathy moments
• Inaccurate or outdated information
• The need for a specialized agent

Let’s take a common situation in call centers: A support team notices CSAT is declining even though agents are meeting handle-time targets. AI analysis shows that low CSAT correlates strongly with calls where agents fail to summarize the resolution before ending the call. By redefining “quality” to include clear call closure and configuring AI to flag missing summaries, the team targets coaching on that behavior. CSAT improves without increasing call length.

2. Provide continuous training

Many call centers have a misconception that AI is a one-time thing. You upload information once and expect it to magically transform your customer experience. But AI insights should feed ongoing coaching, not one-time corrections. Use recurring quality trends to update training content, reinforce best practices, and shorten time to proficiency for new agents. Continuous learning keeps performance improving as customer expectations evolve.

A much easier route is choosing AI tools that never stop learning. For example, Capacity, an AI-powered customer and employee support platform, connects and unifies your corporate information across touchpoints, such as:

• Files
• Internal communication
• Third-party integrations
• Slide decks
• And more

Not only does AI learn from accurate and comprehensive data, but it also stays connected to it, learning as your business moves forward. Every new interaction or update makes the brain behind the platform smarter.

3. Deploy across all your channels

The best way to avoid data silos or gaps is to apply AI-powered call center quality management software consistently across:

• Voice
• Chat
• Email
• Messaging

Cross-channel monitoring ensures quality standards remain uniform and allows teams to understand how customer behavior and agent performance differ by channel.

Let’s think about this situation: A contact center monitors call quality with AI but relies on manual reviews for chat and email. AI analysis across all channels later reveals that chat interactions have the lowest resolution rates due to delayed responses and incomplete troubleshooting. By deploying AI consistently across voice, chat, and email, the team standardizes quality expectations and improves resolution rates across digital channels.

4. Reward high-performing agents

It’s common to face resistance or difficulties when introducing AI-powered systems. Often, it’s not because people dislike the technology, but because they’re overwhelmed by the extra work of learning new tools and integrating them into existing processes.

Instead, use AI-backed quality data to recognize agents who consistently deliver strong outcomes. Southwest Airlines offers a great example: they incorporated an employee appreciation program to motivate their staff. The impact was clear—86% of employees felt genuinely valued and respected, and nearly 8 in 10 reported higher engagement and motivation thanks to the program. 

Transparent, objective recognition builds trust in the call center QA process and motivates agents to repeat the behaviors that drive great customer experiences.

5. Collect customer feedback

Combine AI quality insights with direct customer feedback like CSAT or CES to validate what customers actually value. This connection helps distinguish between internally “compliant” interactions and experiences customers truly perceive as high quality.

6. Choose the right tools

Select AI tools that are explainable, configurable, and easy for agents and managers to understand. The best call center quality management software supports human oversight, integrates with existing systems, and translates insights into clear coaching actions—not just scores.

Top 10 tools for call center quality monitoring in 2026: Features, pros & cons

Call center quality monitoring tools at a glance

Platform	Pros	Cons
Capacity	Improves quality by preventing mistakes before they happenReduces cognitive load on agentsStrong fit for internal support and IT help desksUnifies customer and employee supportOmnichannel QA	Might be too complex and expensive for smaller businesses
Qualtrics	Strong voice-of-the-customer alignmentExcellent for CX strategy and leadership reportingHelps validate whether QA standards reflect customer expectations	Relies on sampled feedback rather than 100% interaction analysisRequires integration with other tools for agent-level QA detail
Observe.AI	Massive reduction in manual QA effortStrong compliance and risk use casesClear linkage between quality behaviors and KPIs	Requires thoughtful configuration to avoid over-scoringMore powerful than necessary for very small teams
AmplifAI	Strong at operationalizing QA insightsSupports personalized coaching at scaleImproves athe doption of QA programs by frontline managers	Depends on upstream data quality from QA and WFM systemsLess focused on raw interaction analytics
Dialpad	Fast time to valueNative, real-time insights for agents and managersStrong fit for modern, cloud-first teams	QA depth is lighter than dedicated platformsLess customization for complex enterprise QA frameworks
Level AI	Strong at spotting patterns that manual QA would missBridges quality monitoring with customer experience insights	May require upfront setup to tailor to specific QA scorecardsCan feel complex for teams that are new to AI-based QA tools
Playvox	Very coach-friendly QA designGreat for aligning quality with broader performance metrics	Less strong on real-time AI analytics compared to newer platformsBest suited to larger teams with defined QA programs
MaestroQA	Highly configurable and transparent scoringEasy to integrate with existing contact center systems	Doesn’t include deep conversational AI analytics by defaultSlower automation
QEval	Highly scalable for enterprise operationsStrong compliance and risk detection	Heavier platform that may require more setup and governanceNot as light or plug-and-play as some SaaS solutions
Hear	Good balance between automation and usabilityFocused on practical insights, not just raw data	Less broad in scope than full CCaaS platformsMay need pairing with other tools for workforce or case management

Choosing the right platform is a crucial next step to put your new AI strategy in motion. But what are those tools? We gathered 10 top software solutions that automate quality assurance monitoring and offer many amazing features to improve employee and customer experiences.

1. Capacity

Capacity is an AI-powered support and knowledge automation platform with advanced auto quality assurance features. It offers a unified solution that connects all customer and employee support tasks into one ecosystem. Its strength in quality monitoring comes from reducing agent error and inconsistency by guiding agents in real time, providing QA agent tools, and automating routine interactions.

QA & monitoring features:

• An AI knowledge base surfaces relevant, approved answers to agents during live calls or chats, reducing guesswork and ensuring customers receive accurate, consistent information without long holds or escalations.
• Automation of 90% of repetitive support tasks, such as password resets, ticket categorization, or follow-up steps, the system minimizes human error and ensures these tasks are handled the same way every time.
• Each interaction is automatically tagged by topic, intent, or outcome, allowing teams to analyze trends, identify recurring issues, and understand where processes break down.
• Insights into resolution paths and agent dependency on knowledge tools reveal how agents arrive at resolutions and how often they rely on knowledge resources, helping leaders identify best practices, call center training opportunities, and ways to improve self-service content.
• Agent assist and training provide all the necessary tools to support your team. They can always access an automated knowledge base, get real-time suggestions and coaching while interacting with customers, get information about a customer from connected systems in an instant, and automate post-call tasks like transcripts, notes, and information transfer.

Pros:

• Improves quality by preventing mistakes before they happen
• Reduces cognitive load on agents
• Strong fit for internal support and IT help desks
• Unifies customer and employee support
• Omnichannel QA

Cons:

• Might be too complex and expensive for smaller businesses

2. Qualtrics

Qualtrics approaches quality monitoring from a customer-experience-first lens. Rather than analyzing agent behavior in isolation, it excels at connecting quality signals to how customers actually feel about the interaction.

QA & monitoring features:

• Post-interaction surveys tied to agents and touchpoints enable teams to understand how agent behavior impacts perceived quality.
• Sentiment and experience analytics across customer journeys help identify experience gaps that traditional QA reviews may miss.
• Integration of CX metrics with operational data helps leaders correlate service behaviors with customer loyalty.
• Text analytics on open-ended customer feedback allows AI to analyze free-text survey responses to uncover themes, complaints, and improvement opportunities at scale.

Pros:

• Strong voice-of-the-customer alignment
• Excellent for CX strategy and leadership reporting
• Helps validate whether QA standards reflect customer expectations

Cons:

• Relies on sampled feedback rather than 100% interaction analysis
• Requires integration with other tools for agent-level QA detail

3. Observe.AI

Observe.AI is purpose-built for AI-driven quality monitoring in contact centers. It replaces manual sampling with full-coverage analysis and turns QA into a continuous, data-driven function.

QA & monitoring features:

• Speech and text analytics across 100% of interactions eliminates sampling bias and uncovers risks or trends that manual QA would miss.
• Automated QA scoring with customizable scorecards allows you to automatically score against defined criteria, ensuring consistent evaluation while reducing manual effort.
• Real-time and post-call compliance and risk detection flags compliance issues, escalations, or risky language as they occur or immediately after the interaction.
• Coaching insights tied directly to call moments allow managers to review specific call segments linked to coaching recommendations, making feedback precise and actionable.

Pros:

• Massive reduction in manual QA effort
• Strong compliance and risk use cases
• Clear linkage between quality behaviors and KPIs

Cons:

• Requires thoughtful configuration to avoid over-scoring
• More powerful than necessary for very small teams

4. AmplifAI

AmplifAI focuses on turning quality data into behavior change. It sits between QA, performance management, and coaching, making quality insights easier for agents and managers to act on daily.

QA & monitoring features:

• AI-driven identification of performance and quality gaps pinpoints where agents struggle and which behaviors most impact results.
• Personalized coaching recommendations are based on each agent’s specific performance trends and quality gaps.
• AI links quality behaviors to KPIs like CSAT, FCR, and AHT, clarifying which actions actually drive success.
• Performance tracking at agent and team levels allows leaders to monitor improvement over time and compare performance across teams using consistent, data-backed metrics.

Pros:

• Strong at operationalizing QA insights
• Supports personalized coaching at scale
• Improves athe doption of QA programs by frontline managers

Cons:

• Depends on upstream data quality from QA and WFM systems
• Less focused on raw interaction analytics

5. Dialpad

Dialpad embeds AI directly into its cloud contact center platform, making quality monitoring more accessible and real-time. It’s especially attractive for teams that want QA without a complex tool stack.

QA & monitoring features:

• Dialpad transcribes calls live and analyzes sentiment, giving agents and managers immediate visibility into conversation quality.
• AI-generated summaries capture key moments and flagged terms, reducing review time and simplifying QA workflows.
• Interaction insights highlight talk time, interruptions, and phrasing patterns to support targeted coaching.
• Built-in analytics without third-party QA tools enable faster adoption without complex integrations.

Pros:

• Fast time to value
• Native, real-time insights for agents and managers
• Strong fit for modern, cloud-first teams

Cons:

• QA depth is lighter than dedicated platforms
• Less customization for complex enterprise QA frameworks

6. Level AI

Level AI is a customer interaction automation and call center quality management platform. It brings conversational intelligence to quality monitoring by automatically evaluating interactions and surfacing performance gaps without manual sampling. It focuses on customer insights and conversational patterns that matter most to coaching and CX teams.

QA & monitoring features:

• 100% interaction evaluation with AI-driven insights surface quality trends, missed opportunities, and risks that traditional sampling would overlook.
• The platform automatically highlights specific behaviors—such as weak probing or missed empathy cues—that managers can address through targeted coaching.
• Deep conversational analytics detects customer intent and emotional signals throughout the conversation, helping teams understand not just what was said, but how it was experienced.
• CX optimization tied to quality outcomes enables teams to see how conversation behaviors influence satisfaction and loyalty.

Pros:

• Strong at spotting patterns that manual QA would miss
• Bridges quality monitoring with customer experience insights

Cons:

• May require upfront setup to tailor to specific QA scorecards
• Can feel complex for teams that are new to AI-based QA tools

7. Playvox Quality Management

Playvox QM specializes in structured QA workflows and performance tracking, giving teams a clear framework to score, coach, and improve agent behaviors. It’s designed for mid-market to enterprise contact centers that want structured, repeatable quality processes.

QA & monitoring features:

• Customizable QA scorecards and evaluation rubrics standardize quality criteria that align with internal policies and customer experience goals.
• Automated evaluations and trend tracking reduce manual effort by automating scoring workflows and highlighting performance trends over time.
• Real-time and historical performance dashboards give visibility into agent quality performance at both individual and team levels for faster decision-making.
• QA results feed directly into structured coaching sessions, ensuring feedback is timely and actionable.

Pros:

• Very coach-friendly QA design
• Great for aligning quality with broader performance metrics

Cons:

• Less strong on real-time AI analytics compared to newer platforms
• Best suited to larger teams with defined QA programs

8. MaestroQA

MaestroQA focuses on giving QA teams control and clarity with flexible scorecards, calibration tools, and detailed analytics. It offers advanced call center quality management 

features and is a good choice if you want to customize how your organization defines quality and align review practices across teams.

QA & monitoring features:

• MaestroQA supports customized evaluations for voice, chat, and email, ensuring consistent standards regardless of channel.
• QA calibration and alignment tools help teams maintain scoring consistency across evaluators and reduce subjectivity.
• Cross-channel quality analytics and reporting show trends across interaction types to identify systemic issues and improvement opportunities.
• Actionable insights for continuous improvement highlight root causes behind quality gaps, enabling teams to focus on meaningful operational changes.

Pros:

• Highly configurable and transparent scoring
• Easy to integrate with existing contact center systems

Cons:

• Doesn’t include deep conversational AI analytics by default
• Slower automation 

9. QEval

QEval AI Platform, powered by ETSLabs, is tailored for large-scale contact centers looking to expand quality monitoring with enterprise-grade capabilities like multi-language speech analytics and extensive integrations. It’s especially strong where compliance and risk require robust monitoring coverage.

QA & monitoring features:

• The platform analyzes voice interactions at scale to detect quality issues, compliance risks, and performance trends.
• QEval applies predefined quality and compliance criteria consistently across large volumes of calls.
• Enterprise-grade integrations with major contact center systems, supporting complex, large-scale environments.
• Real-time monitoring and alerting for high-risk interactions, enabling faster intervention and escalation control.

Pros:

• Highly scalable for enterprise operations
• Strong compliance and risk detection

Cons:

• Heavier platform that may require more setup and governance
• Not as light or plug-and-play as some SaaS solutions

10. Hear

Hear is an AI conversation intelligence tool built to automate quality assurance, compliance monitoring, and coaching insights by analyzing every interaction. It’s a simpler, focused option if your main goal is conversation analytics and actionable insights.

QA & monitoring features:

• Automated conversation analysis for QA evaluates interactions using AI to identify quality gaps, coaching needs, and recurring customer issues.
• The platform flags deviations from scripts, disclosures, or regulatory requirements to reduce operational risk.
• Quality findings are translated into clear recommendations that managers can apply directly in coaching sessions.
• Ongoing monitoring highlights emerging quality issues before they impact customer experience at scale.

Pros:

• Good balance between automation and usability
• Focused on practical insights, not just raw data

Cons:

• Less broad in scope than full CCaaS platforms
• May need pairing with other tools for workforce or case management

Your call center quality monitoring done right

When it comes to customer experience, you need to ensure your tool stack can meet growing customer demand and evolving business needs. Quality assurance, though it happens in the background, is one of the most important drivers of the service your customers expect and deserve.

If you want to provide quality monitoring while also integrating a full customer and employee support toolkit into your business, you can’t miss Capacity.

Capacity is a unified CX Automation Platform built to help businesses like your reduce costs, improve CSAT, and support both virtual and human agents with AI-powered efficiency.

We connect your knowledge, data, and systems into one AI Knowledge Layer—powering your virtual agents, human agent assistance, auto-QA, and conversational intelligence across every channel.

Sounds good? Book a demo!

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