CCaaS and BPO providers live and die by the quality of the customer experience they deliver. Clients rely on them not just to answer calls, but to do so with consistency, professionalism, empathy, and accuracy every time.
That’s where quality assurance (QA) comes in. QA is the foundation for customer satisfaction, brand reputation, compliance, and efficiency. It helps ensure that every agent, whether human or AI, is delivering the kind of service clients expect, and that providers are always one step ahead of issues.
But traditional QA has serious limitations. Manual reviews, random sampling, and subjective scoring leave gaps in oversight. And with thousands of conversations happening daily, even well-intentioned QA teams are forced to guess at the full picture.
That’s changing, fast.
Advances in speech-to-text (STT) and large language models (LLMs) now make it possible to monitor every call, analyze interactions in real time, and surface actionable insights automatically. The result: faster feedback loops, more consistent performance, and smarter operations at scale.
This article explores the impact of AI STT tools on quality assurance, and how you can integrate them for better call center services.
Key takeaways:
Call center QA is the process of monitoring, evaluating, and improving the performance of agents during customer interactions. It’s not just about grading calls; it’s about ensuring every customer touchpoint meets internal standards and client expectations.
QA programs typically include call recording, performance scoring, coaching, and feedback loops. In a traditional setup, QA teams listen to a sample of calls and rate them based on compliance, communication skills, and resolution effectiveness.
Modern QA, however, goes much further. Today’s top providers use real-time transcription, AI-driven scoring, and conversation intelligence tools to evaluate performance across all interactions. This approach turns QA from a reactive process into a proactive, data-driven engine for service excellence.
Quality assurance isn’t just a nice-to-have, it’s a core business function for any BPO or CCaaS provider.
Here are just some of the benefits of good QA:
The fundamentals of quality assurance haven’t changed: businesses still need to know how their agents are performing and how customer interactions are being handled. But the tools used to deliver that insight have evolved dramatically.
Where QA was once manual, time-consuming, and sample-based, it’s now becoming automated, scalable, and real-time.
Without automation and AI, quality assurance relies heavily on manual processes. Dedicated QA teams randomly sample calls, score them based on a rubric, and flag issues for coaching or compliance.
While this system lays the groundwork for consistent service, it comes with serious limitations:
Today, speech-to-text and AI-powered analysis have changed the QA equation entirely. Instead of relying on small samples and delayed feedback, companies can now analyze every interaction automatically, at scale, with greater context and precision.
AI is doing more than speeding up quality checks. It’s changing the value proposition of QA entirely. With the right technology in place, providers don’t just get more data. They get better outcomes: for agents, for customers, and for their business.
Here’s what that looks like in practice:
AI-powered QA delivers fast, objective feedback. Instead of waiting days or weeks for a scorecard, agents can get insights in near real-time. And because LLMs assess intent, tone, and context, their scoring is more consistent and less subjective.
That means coaching becomes more targeted, and performance improves faster. Top agents are recognized more clearly while struggling agents get timely, specific help.
When quality assurance covers every call, it’s easier to spot recurring customer pain points and then solve them. Agents are more likely to follow processes, stay on-script, and deliver a smoother experience.
AI can also surface insights that improve product design, self-service tools, or call routing logic. All of this reduces handle time, lowers repeat contact rates, and boosts CSAT and NPS scores across the board.
Manual QA teams are expensive and often overburdened. Automating QA—especially at high volumes—eliminates much of the repetitive review work and lets providers scale without growing headcount. The resources saved can be reinvested into agent training, performance ops, or innovation.
And because AI never tires or misses a call, coverage and consistency increase dramatically, often at a lower cost than traditional models.
AI QA isn't just faster, it's better. It helps providers move from reactive firefighting to proactive service improvement—driving gains in performance, satisfaction, and efficiency, all at once.
The technology is powerful, but it’s not plug-and-play. To get the most from AI-driven QA, CCaaS and BPO providers need to be thoughtful about how they implement it. That means making smart choices around tools, metrics, and human involvement.
Here are three key areas to focus on:
Everything starts with transcription. If your speech-to-text engine can’t accurately capture what was said—especially across diverse accents, dialects, or noisy call environments—your QA will be flawed from the start. Look for engines that are trained on real-world call center audio and tested across global regions.
Speed and cost matter too, but accuracy is the non-negotiable foundation. A slightly slower but more reliable transcript is far more valuable for scoring and compliance.
Learn more about STT benchmarking here.
Traditional QA scorecards often focus on binary outcomes: Did the agent say the right thing? Was the greeting correct? But AI allows for deeper, more dynamic evaluation.
LLMs can help define and track more meaningful metrics, like resolution sentiment, escalation risk, or adherence to tone guidelines. These are the kinds of metrics that reflect true business outcomes, not just compliance checkboxes.
AI can scale your QA coverage instantly, but human oversight remains critical. Use human analysts to fine-tune scoring rubrics, audit flagged calls, and catch edge cases. Likewise, for sensitive calls—like those involving medical advice, legal issues, or vulnerable customers—human-in-the-loop reviews may still be the gold standard.
The bottom line: to unlock the full power of AI QA, choose tools that match your operational needs, define success beyond scripts, and keep people involved in the loop. The right setup leads to faster insights, better service, and more trust in the system—internally and externally.
As we said, at the core of every great AI-powered QA system is high-quality transcription. And that’s exactly what Gladia delivers.
Our speech-to-text engine is built specifically for contact center use cases, with the perfect combination of accuracy, speed, and language flexibility. Unlike generic ASR solutions, Gladia includes denoising, speaker diarization, and multilingual support out of the box—making it easier to run QA across diverse teams and customer bases.
Whether you’re building QA tools into a CCaaS platform or optimizing internal QA for a BPO operation, Gladia offers full pipeline customization. You can create workflows that combine automated scoring with human validation, or power downstream LLMs to assess intent, tone, and compliance—all from a clean, structured transcript.
Leading customer acquisition specialist Selectra uses Gladia to automate its QA process. Every customer call is transcribed and analyzed for script adherence.
Human reviewers simply validate the system’s scoring, drastically reducing manual effort and lag time. And the results are impressive:
Want to try it for yourself? Get started for free now or talk to our team today.
Speech-To-Text
Customer service is evolving fast to meet new challenges. Today's clients expect immediate, accurate answers to increasingly specific queries and complaints. Meanwhile, contact centers need to reduce costs, improve efficiency, and maintain compliance…all while delivering exceptional experiences.
Product News
Even the most advanced speech-to-text (STT) systems can make mistakes, especially when they encounter unfamiliar words like brand names, technical acronyms, or non-standard pronunciations. For call centers and customer service platforms, these missteps aren’t just minor glitches. They can lead to broken workflows, misinterpreted customer needs, and frustrating experiences on both ends of the call.
Speech-To-Text
CCaaS and BPO providers live and die by the quality of the customer experience they deliver. Clients rely on them not just to answer calls, but to do so with consistency, professionalism, empathy, and accuracy every time.
