What is Automated Quality Management (AQM)?
Automated quality management (also known as automated QM or AQM) is a method for streamlining and enhancing quality monitoring, analysis and improvement using artificial intelligence (AI) and machine learning (ML) technologies. Traditionally, quality management involved manual reviews, assessments, and adjustments, which were time-consuming and often prone to human error. With AQM, AI-powered systems take over these tasks, providing faster, more accurate and consistent results.
These AI-driven systems automatically evaluate processes, interactions, and outcomes, ensuring that they meet predefined quality standards. Advanced, generative AI-powered solutions, like Uniphore’s Conversation Insights Agent, go even further, providing the reasoning behind each evaluation and validating it by linking to key moments in the interaction. This added layer of contextual intelligence allows quality managers to take a wider look at quality assurance (QA) processes, enabling them to make more informed decisions that improve overall performance.
As businesses look for new ways to drive efficiencies and reduce costs, automated quality management has become increasingly popular. By assigning tedious evaluation tasks to AI, AQM is transforming the way companies manage and ensure quality control across a variety of industries, particularly in sectors that handle large volumes of customer interactions, data, or repetitive tasks. Consequently, businesses that deploy AQM typically see enhanced efficiency, reduced human error, and consistently higher standards of quality across the board.
Key components of AQM
The implementation of AQM typically involves several key components, each of which plays a vital role in ensuring that quality standards are met. Below are some of the primary elements:
LLM-powered monitoring
AQM uses large language models (LLMs) to automatically monitor and analyze interactions for quality-related issues. This monitoring helps identify issues such as performance gaps, inconsistencies, or non-compliance with standards, allowing companies to act swiftly.
Data analytics and reporting
The ability to collect and analyze vast amounts of data is essential for AQM. By leveraging big data analytics, AQM systems can provide insights into quality trends, root causes of issues and areas that need improvement.
Automated feedback and improvements
Once potential quality issues are identified, AQM systems can trigger automated responses or recommend improvements. This might include alerts for human supervisors or even fully automated adjustments to processes, depending on the complexity of the system.
Customizable scorecards and dashboards
Before generative AI, it could take quality managers days to create QA scorecards. Now, with advanced AQM systems, supervisors can custom-build evaluation criteria in just a few hours. And with custom dashboards, they can drill down into the data in more ways than ever before.
Actionable insights
AI technologies provide real, usable insights, allowing businesses to respond to issues proactively. By providing QA managers and business users with relevant and timely AQM insights, organizations can reduce downtime and ensure consistent product or service quality.
Benefits of AQM for business
When integrated within a unified business AI platform, AQM offers a wide range of benefits—from optimizing operations to maintaining high-quality outputs. Key advantages of AQM for businesses include:
Increased efficiency
By automating quality management tasks, AQM frees up human resources for higher-level strategic work. Automated QM systems handle repetitive, time-consuming tasks like reviewing customer interactions, monitoring product defects, or analyzing data, allowing staff to focus on decision-making and problem-solving. This efficiency boost can result in significant cost savings and faster turnaround times.
Improved accuracy and consistency
Human error is an unavoidable part of manual quality management, but AQM systems minimize this risk. AI algorithms are capable of detecting even the slightest inconsistencies or errors, ensuring that all quality checks are accurate and standardized. This leads to more reliable outcomes and a consistently high level of product or service quality.
Scalability
AQM allows businesses to scale their quality control efforts effortlessly. As an organization grows, so does the volume of interactions, data and products that need to be reviewed. Automated QM systems can handle increasing workloads without sacrificing performance or accuracy, making it easier for companies to maintain their standards even as they expand.
Proactive issue resolution
One of the greatest advantages of AQM is its ability to detect and address issues—such as customer churn or pricing changes—before they become widespread. AI-powered systems can identify potential problems before they impact customers or cause significant damage. This proactive approach helps reduce downtime, prevent costly recalls or repairs, and maintain a strong brand reputation.
Enhanced customer experience
AQM’s ability to monitor and analyze customer interactions in real time ensures that service quality remains high. In call centers, for example, AQM can automatically assess calls to ensure that agents are adhering to scripts, maintaining a professional tone and resolving customer issues effectively. This results in improved customer satisfaction and loyalty, as well as reduced churn rates.
Optimized agent performance
On the agent side, AQM can instantly review agent performance and automatically fill out scorecards. These tools can help supervisors coach agents on individual-level areas for improvement. By adding emotion AI and sentiment analysis to the formula, advanced solutions, like Uniphore’s Conversation Insights Agent, can help agents and coaches identify and understand the impact of emotion on call quality.
Common use cases for AQM
Now that we’ve discussed some of the benefits of automated quality management, let’s explore where enterprises are deploying AQM within their operations. Below are some of today’s most popular AQM use cases:
Call centers and customer support
In call centers, automated QM systems can automatically analyze voice or text interactions to assess the performance of customer service agents. These systems can detect issues like compliance violations, tone mismatches, or inefficient resolutions, helping companies improve their overall customer experience.
Manufacturing and production
In manufacturing, AQM systems monitor the production process to detect defects or inconsistencies in real time. Automated systems can instantly flag any deviations from the standard, allowing operators to correct the issue before it affects the entire production line.
Telecommunications
AQM can be integrated into telecommunications systems to improve business-wide QA processes. This is exceptionally beneficial for telecom providers who offer multiple services—and thus operate on multiple systems—such as mobile, internet, and cable. Automated QM can find and diagnose bottlenecks before they impact broader service quality.
Healthcare
In the healthcare industry, AQM can be applied to monitor medical procedures, patient interactions, and data management systems, ensuring that all actions adhere to strict regulatory and quality standards.
The role of generative AI in automated quality management
Generative AI has been a catalyst—and a disruptor—for many enterprise AI programs, including automated quality management. That’s because it’s unlike any other AI that’s come before it. With the ability to analyze enormous volumes of data and generate actionable insights, GenAI is rewriting the rules of AQM. Today, advanced AQM systems cannot only track quality indicators; they can comprehend the reasoning and intent behind their findings—and generate actionable feedback through plain language interaction.
That’s changing how business leaders and QA managers think about—and plan around—automated quality management. Next, we’ll explore how AQM is evolving and how the next major AI shift—the rise of agentic AI—is reshaping the future of quality management in the modern enterprise.
How automated quality management is evolving
Automated quality management, like many other enterprise AI systems, is evolving at a rapid pace. With advances in generative AI mentioned above, AQM systems that were state-of-the-art just a few years ago are suddenly showing their age.
As technology evolves, AQM will become more sophisticated, enabling even more granular control over quality management and providing companies with unprecedented levels of precision and insight. In fact, we’re already seeing the next generation of automated quality management solutions is capable of with the rise of the AQM agent.
AQM in the age of agentic AI
With agentic AI, enterprises are now able to create domain-specific AI agents for countless use cases. That includes automated quality management. Today, businesses can leverage the AQM capabilities of a generative AI interaction analytics agent, like Uniphore’s Conversation Insights Agent, to address their unique quality management needs.
Powered by our business AI platform, our Conversation Insights Agent not only automates quality management across all enterprise interactions; it connects those findings with data from across the enterprise to generate rich, actionable insights. Business leaders can use these insights to maximize operational efficiencies, drive quality enhancement and consistency, and uncover hidden opportunities for improvement.
From AQM insights to action
As businesses continue to digitalize and expand, maintaining consistent quality across processes, products, and services is becoming increasingly complex. Agentic AI automated quality management offers a solution to this challenge, providing companies with the tools they need to automate and optimize quality assurance. By generating enhancement suggestions and recommended next steps gleaned from AI insights and reasoning, contact centers—and other business units—can improve critical KPIs and position themselves for long-term success.
By choosing an agentic AI platform with built-in AQM capabilities powered by generative AI, businesses can embrace the road ahead, future-proofing their AI investment with an AQM solution with endless flexibility and scalability.