The evolving role of AI in call quality monitoring
Manual quality assurance has long been a cornerstone of contact centre operations, but it comes with real challenges. Reviewing calls is time-intensive, results can vary depending on who’s scoring, and only a small percentage of conversations are typically evaluated. This limits visibility, slows down feedback and risks overlooking both compliance issues and coaching opportunities.
Enter AI call quality monitoring. With the rise of speech analytics, natural language processing and machine learning, AI is reshaping how QA is done. Rather than replacing human evaluators, it complements them, scaling the review process, surfacing trends faster and improving consistency across the board.
What does AI bring to call quality monitoring?
AI-powered tools have introduced several capabilities that are now essential in modern QA environments. Here’s how they support and extend traditional quality call monitoring practices:
Speech analytics and automated transcription
Calls can be transcribed more accurately and quickly with the latest technology making it easier to search conversations, detect key phrases and analyse communication trends.
No more notetaking
AI removes the need for manual notetaking and provides a full summary record of agent-customer interactions. No more post-call admin for agents, faster and more accurate summarisations of interactions for team leads and analysts and standardised records of all interactions across a business for clearer visibility and understanding.
Compliance flagging
AI call quality monitoring tools can detect when required statements aren’t delivered or when sensitive data is discussed without proper handling. These alerts allow supervisors to intervene quickly and correct risky behaviour to avoid noncompliance and misconduct or generally uncover knowledge gaps and subsequent training needs for agents.
Sentiment and tone scoring
Advanced models can detect frustration, empathy, and satisfaction based on voice patterns and word choices. While imperfect, these scores add an extra layer of insight into how a customer felt about the experience.
Smart tagging and keyword detection
AI can scan every call or interaction for mentions of key topics, products, or issues. When calls are categorised in a more structured way, it is easier to review trends and pull specific examples for coaching, compliance or analysis when using AI-powered analytics tools.
As a result, AI-driven call quality monitoring provides a faster, more scalable way to surface insights across thousands of interactions, without losing sight of what matters most.
Benefits of AI in contact centre QA
One of the most significant shifts AI brings to QA is scale. Rather than evaluating 2–5% of calls manually, AI enables call centre call quality monitoring at 100% coverage.
This unlocks several key benefits:
- Fairer, more objective evaluations: AI scores every agent against the same criteria, reducing the variability that often comes with human interpretation.
- Faster insight loops: Trends and issues are surfaced faster, allowing QA teams to take immediate action rather than waiting for end-of-month reports.
- Stronger coaching cycles: With better visibility into specific agent behaviours, supervisors can personalise development plans and track progress over time.
- Pattern recognition at scale: AI excels at spotting recurring issues, be it a script deviation, repeated product confusion, emerging customer pain points, or general shifts in market behaviour that impact the business’ direction.
When paired with strong governance and oversight, quality call centre monitoring becomes more proactive and less reactive.
Where AI still needs human support
Despite its power, AI has real limitations. It doesn’t truly “understand” context in the way humans do, so it still needs human judgement and intervention. Here’s where people are essential:
Understanding nuance
AI struggles with empathy and sarcasm, and some less advanced models in the market can’t comprehend culturally-specific language. A joke might be flagged as inappropriate or missed entirely. Human reviewers bring emotional intelligence that models cannot replicate.
Managing false positives
A system might flag a compliance issue incorrectly, causing unnecessary escalation or confusion. Manual review helps determine whether a flag was valid and what corrective action (if any) is needed.
Coaching and development
AI can show where performance lags, but it can’t explain why. That’s where supervisors step in, interpreting results, identifying root causes and having real conversations that lead to improvement.
Maintaining quality and fairness
Even the best AI systems need calibration. Teams must regularly test accuracy, review flagged calls and ensure scoring algorithms align with actual business goals. Without oversight, call centre call quality monitoring can drift away from what’s most important.
Ultimately, quality call monitoring is about understanding human interactions. AI helps scale and speed up that understanding, but it can’t replace the human element entirely. At Liquid Voice, we believe in humans using AI – not AI replacing humans.
Blending AI with your QA team
The future of QA isn’t AI versus people, it’s people and AI, working together. Here’s how to build a hybrid model that works:
Use AI as a first pass
Let AI flag high-risk calls, surface common coaching themes or score calls against specific checklists. This lets your QA team focus on deeper reviews and strategic support, rather than routine tasks.
Focus analysts on coaching
With more time freed up, team leads and analysts can invest in higher-quality coaching, one-to-ones, and skill development. That’s where real behaviour change happens.
Integrate your tools
Modern QA platforms combine transcription, analytics and coaching into one system. Choose tools that enable end-to-end quality call centre monitoring, so your insights, feedback and compliance checks live in one place.
Use AI to improve your QA forms
AI doesn’t just support evaluations, it can also help improve the tools you use to measure them. By analysing large volumes of interactions, AI can highlight which agent behaviours most influence outcomes like CSAT, compliance or first call resolution. These insights can guide the creation or refinement of your quality monitoring forms, ensuring that scorecards reflect what actually drives performance. This data-driven approach results in more relevant, focused criteria—reducing guesswork and improving the effectiveness of your QA programme.
Conclusion
AI has transformed the speed, scope and precision of call quality monitoring. By automating menial analysis admin and surfacing actionable insights, AI call quality monitoring empowers contact centres to evaluate more calls, respond faster and coach more effectively.
But AI is a force multiplier. Its real value comes when paired with human judgement, empathy and insight. For QA leaders, the goal should be to find the right balance: using AI where it adds efficiency, while preserving the people-centric elements that drive true service improvement.
Review your call quality monitoring goals and processes to see where automation can offer support, without sacrificing quality, fairness or team engagement.
