Why Is AI Needed for Quality Assurance?

Quality Assurance (QA) professionals deal with vast sums of data stemming from various manufacturing operations and customer complaints. Even with a Quality Management System (QMS) software solution, it is very time-consuming for QA teams to capture and document data, ensure complete traceability, and log product errors. Much of the challenge stems from the fact that legacy quality data are siloed.

As manufacturers increasingly introduce smart devices to their operations, these data become even more difficult to analyze. Artificial Intelligence (AI) integrated into quality assurance workflows can streamline the process of identifying and logging product errors, while providing predictive analytics. The greatest appeal of AI adoption across industries is the promise of automation, and the QA space is no different. Not only can AI-based QMS software identify and report product failures more efficiently than humans, but its application also frees up labor resources. That way, human workers can spend more time on more high-priority tasks within the facility.

Applications of AI in Quality Assurance

This section explores some of AI's most impactful applications in the quality assurance process. Some of these AI applications are still in development by vendors, while others are used sparingly across the manufacturing sector.

Advanced Analytics

A key benefit of AI in QA is its ability to make sense of the huge amounts of data that QMS solutions already collect. Machine Learning (ML) and Deep Learning (DL) models draw meaningful insight into historical product defects so that QA teams can prevent future failures.

Risk Management

Drawing upon inspections, audit results, and past customer criticisms, AI identifies risk patterns and provides corrective steps for QA professionals.

Root Cause Analysis

AI automates the Root Cause Analysis (RCA) process by contextualizing real-time and historical data on product defects. This AI application significantly accelerates the time it takes for QA teams to accurately identify the root cause of a quality issue.

Employee Training

AI can accurately map training modules to specific job functions to ensure rapid onboarding and knowledge sharing. Moreover, Large Language Models (LLMs) like ChatGPT can quickly answer employee questions regarding quality assurance best practices.

Automated Logging/Data Entry

The manual logging/data entry and categorizing of customer complaints can be replaced with AI. This automated approach eliminates bias when categorizing complaints and enables QA teams to resolve quality issues promptly.

FMEA Authoring and Streamlining

Using past product data and Failure Mode and Effects Analysis (FMEA) documents, generative AI can automatically create new documents for similar products. The generative AI application learns and improves the more it is used.  

Challenges with AI in Quality Assurance

As beneficial as AI is in quality assurance, several challenges are still associated with its operation and implementation. Here are some of those challenges.

Determining What Works

A main challenge of deploying AI into QA processes is the lack of clarity on how to reach the end goal. While manufacturers know what outcomes they want with AI-based quality management, getting there is not straightforward. There are all too many business cases of AI deployment that cause more hassle than they’re worth.

Recent survey results show that only 33% of quality management professionals are willing to be early adopters of technologies. As technology solution providers continue testing what works, the uncertainty should dissipate, and QA teams will be more likely to adopt AI applications.

Data Quality and Availability

For AI to work for QA, the manufacturer needs a strong data collection and categorization process. If critical data cannot be collected or the data fed into the AI model are low-quality, then AI deployment will prove unfruitful.

Legal Ownership

There is still uncertainty regarding the legal ownership of an AI model once it’s fed into a QMS. Both the technology vendor and the manufacturer can make a case for entitlement to the model. Security-first-minded firms will be scared off by the idea that sensitive data could potentially be used to train AI models for other customers.

Growing AI Regulation

Regulatory activities such as the European Parliament’s Artificial Intelligence Act signal the dawn of heavy AI regulation. For manufacturers with a global presence, regional legislation will certainly affect AI deployments in QA. As technology vendors adjust to new rules and redesign their solutions, this can temporarily hinder manufacturers’ production processes.

Lack of Trust in AI

Successful deployment of AI in quality assurance requires company-wide buy-in, including your employees. However, recent survey results from Workday indicate that employees are more skeptical of AI than business leaders. In order to gain employee trust, technology vendors and manufacturers must build AI frameworks that promote transparency. These frameworks must also be able to explain the underlying rules of AI models to auditors.

Skills Gap

Introducing AI to a manufacturing plant won’t magically perfect QA overnight. To realize AI's true value in QA, companies will need to train their employees to leverage AI in quality management processes. QMS software suppliers are developing Software-as-a-Service (SaaS) offerings faster than manufacturers are acquiring the necessary skill sets to use them.

Conclusion

ABI Research assesses that AI integration into quality assurance processes is still in the preliminary stage. Today, most deployments are small-scale. However, our analysts note that QMS software vendors are very active in testing and designing these AI-based solutions.

In terms of market approach, there’s a clear distinction between the big-name software vendors and the smaller ones. Larger vendors such as Siemens, Rockwell Automation, and PTC are taking a gradual approach to AI integration into QA. These vendors are taking their time with testing AI-powered QMS software to ensure the smoothest launch possible. Moreover, larger software vendors want to let the market develop to assess which smaller vendors would make for a profitable acquisition.

Conversely, smaller QMS vendors like Intellect, ComplianceQuest, and Dot Compliance have quickly fused AI into their solutions. These vendors hope to differentiate their QA-focused offerings by placing AI at the center of their Unique Selling Point (USP).

Our analysts expect the manufacturing market to hear many announcements for AI-based QMS solutions in 2024, with releases at the tail end of the year. Full-scale deployments likely won’t be seen until early 2025.

AI functionality and automation in QMS solutions should sound like a no-brainer for QA professionals, but skepticism remains. Truthfully, their skepticism is warranted, given AI’s inconsistent results across industries and use cases. Recent surveys show growing suspicion about AI. For example, 52% of Americans are more concerned about AI than excited. Therefore, the next couple of years are crucial for software companies to identify the best opportunities for AI in quality assurance  and demonstrate measurable value.

If you’d like to examine AI's potential and drawbacks in quality management in more depth, download ABI Research’s QMS Software: AI Trends and Market Impact report today.

Alternatively, you can compare the top QMS software vendors your company could potentially partner with by reading our research highlight, Selecting the Right Quality Management System Software Provider for Your Company.