AI in quality management: Benefits and challenges

AI has seeped into pretty much every modern workflow we can think of, especially software development. According to JetBrain’s research, as of 2025, a whopping “85% of developers regularly use AI tools for coding and development, and 62% rely on at least one AI coding assistant, agent, or code editor.”

This paradigm shift is enabling code to be generated at blistering speed. Such speed that humans alone can’t match. The question then arises: With all this code, who is going to maintain its quality and ensure that our increasingly software-driven world doesn’t break and come crashing down?

The obvious answer lies right in front of us: Careful and supervised use of AI for quality management.

In a world where DevOps pipelines are being automated and made increasingly “touchless,” bottlenecking everything at the quality validation stage honestly defeats the whole point. Manual inspections and static checklists simply fall short in such scenarios. This is why software teams need to adapt quickly and adopt AI-enabled code quality management to improve velocity.

In this post, we’ll explore how AI is applied in quality management systems. We’ll also discuss the benefits, challenges, and risks. Let’s begin!

Integrating AI in quality management systems translates into the embedding of AI techniques to facilitate the quality management process

Defining AI in quality management

Integrating AI in quality management systems translates into the embedding of AI techniques to facilitate the quality management process. These techniques can span supervised learning, anomaly detection, computer vision, predictive analytics, and more!

Unlike traditional rule-based systems, which just consist of a bunch of if-else statements, AI learns from historical and real-time data to identify patterns, recommend optimizations, and even decide on the executions. This self-learning and decision-making capability sets the foundation for truly automated development pipelines.

Let’s understand this via an example. Imagine you have a codebase rigged with a plain-text password. In a rule-based quality management system, you would have to entertain a ton of edge cases, detecting garbled text patterns and common password layouts. In the process, you’d have to program for every possible scenario.

However, with AI, you’d be able to train the model on a few plain-text password examples, and the AI would then be able to recognize similar vulnerabilities on its own, refining itself as it goes.

What AI empowers you to do better?

If you think about it, this kind of capability means you’d be able to:

1. Predict potential failures before they come tottering down
2. Automate repetitive and boring QA tasks like defect triaging
3. Audit and flag outages in real time as the code is created

For instance, Landing AI, a platform led by Andrew Ng, is helping manufacturers detect leakages in their compressors. The same platform is also helping John Deere, a heavy machinery manufacturer, to spot defective joints as part of the quality management process.

Similarly, in the software development space, Tricentis is enabling AI-augmented testing with Tricentis Copilot: a suite of tools that helps streamline testing and provide deep testing life cycle insights.

In all these cases, AI is able to spot cases that humans would’ve missed. However, the aim here isn’t to eliminate humans altogether from the quality management process. Rather, it’s to boost human performance and build robustness.

Benefits of AI in quality work processes

Better accuracy and precision

Imagine that you work as a QA manager at a soap bar factory. Thousands of bars of soap are being produced per minute. Each needs to be checked for weight, moisture level, texture, and scent.

However, with the current capacity, you can only sample a few pieces per hour at best. That’s where AI systems come in. With the right sensors and AI model setup, you can boost performance by processing thousands of pieces per minute and checking for micro-defects.

This is not just a theory. The BMW group has been using AI in quality management since 2018. The company uses AI to control paint thickness and monitor pseudo-defects among other quality work processes.

Predictive maintenance and real-time monitoring

Usually, maintenance is done as a reactive measure. However, this is a costly means of bringing a system back to base. Downtime can occur randomly and at critical points, such as peak-volume seasons during an operations process.

Imagine it’s a hot summer day with peak electricity needs. Yet your electricity generation turbine breaks down midday. No fun, right? Here, machine learning models come to the rescue. Sufficiently trained models can help gather multiple data points, such as vibrational analysis, temperature, and sound patterns.

The system will then predict when a machine is likely to fail, allowing maintenance teams to intervene before a breakdown occurs. General Electric integrates AI with Predix, its Industrial Internet of Things platform. The platform enables real-time monitoring for manufacturing facilities and power generation units.

AI manages to do a good job at documentation and auditing tasks that otherwise would take a back seat

Improved decision-making and governance

Humans tend to get bored with repetitive tasks. AI does not. AI manages to do a good job at documentation and auditing tasks that otherwise would take a back seat. Also, AI streamlines quality processes by automating data tracking, task delegation, and anomaly detection.

Instead of losing time for each defect to sit idle, AI systems can help escalate the task till it gets resolved. This helps adhere to SLAs and strengthen governance.

Challenges and limitations

AI is not a set it and forget it system. Human oversight and creative problem-solving are critical nonetheless. Below are a few challenges that quality management systems present and that must be navigated carefully.

Data quality and its availability

AI models devour tremendous amounts of high-quality and unbiased data, day in and day out, to be effective. After all, AI models are only as good as the data behind them. Poor data hygiene, such as missing values, inconsistent formats, and stagnant data, can lead to inaccurate predictions and biased conclusions.

Startup costs and complexity

Integrating AI into a quality work process is not every organization’s cup of tea. It requires great patience, funding, leadership, and directed effort to be successful. The right technical expertise is required for each stage, and appropriate training is needed.

Smaller businesses in particular may find it challenging, as their quality management systems are not fully digitized and not ready to start implementing AI in the first place. And the cherry on top? If the company has a ton of manual legacy quality systems, there might be an additional bill to foot in order to modernize this legacy tech stack from the ground up.

Applications and case studies

Manufacturing and industrial production

• Siemens has been using AI-enabled visual quality inspection to detect scratches, dents, and poor welds.
• Toyota harnesses AI models to predict optimal welding conditions for different materials. In the process, they minimize weak joints and ensure consistent build quality.

Healthcare and pharmaceuticals

• Roche employs AI to diagnose and monitor tumors. The advanced algorithms are even able to detect anomalies and conduct accurate risk profiling.
• Teesside Hospital has been using AI-powered diagnostic tools to enhance quality assurance during anomaly detection and for reading chest X-rays.

Risks and ethical concerns

As with any technology, AI also comes with its own set of inheritance risks and concerns. Let’s discuss a few.

Automation bias

Overreliance on AI can lead to a phenomenon known as automation bias. In some cases, humans might become a bit too comfortable with AI outputs and develop an inclination toward trusting AI blindly. This is no good, since we need to realize that AI systems are too prone to errors, and built-in logic can break down in certain use cases.

Algorithmic bias

This can come into play if the AI model is trained on inherently biased data. As a result, the system might start unfairly flagging certain individuals. Imagine a model is trained to pick an ideal candidate for a job. However, the data used is from a candidate pool that is primarily male.

In this case, if a female is then vetted through the system, the system might unfairly reject her. To avoid such scenarios, fully representative data sets should be used. In fact, this is exactly what happened at Amazon, when its AI recruiting tool started to show a bias against women.

Data risks

Inadvertently, we’ll sometimes allow sensitive data and PII to be processed as part of AI-enabled quality systems. AI systems should be equipped with sufficient cybersecurity measures to ensure that the data is encrypted and not divulged to bad actors.

In order to fully leverage AI’s potential, organizations need to aggressively digitalize and rapidly upskill

Future outlook

The use of AI in quality management is here to stay and will inevitably grow. In order to fully leverage AI’s potential, organizations need to aggressively digitalize and rapidly upskill. Instead of viewing AI as an expense, leadership needs to think about it and treat it as a strategic asset—one that helps stay competitive, prevents complaints, and boosts customer trust.

If you’re excited about the future like I am and are ready to move beyond traditional testing, explore the Tricentis quality intelligence platform powered by SeaLights. The platform leverages advanced analytics and machine-learning algorithms to provide real-time visibility and identify risks as they appear.

Integrating such intelligent solutions with your business can help you bring your product to life faster, all while maintaining durability and confidence.

This post was written by Ali Mannan Tirmizi. Ali is a senior DevOps manager and specializes in SaaS copywriting. He holds a degree in electrical engineering and physics and has held several leadership positions in the manufacturing IT, DevOps, and social impact domains.