AI in Quality Management – What Can You Really Do With It Today?

Quality systems generate large amounts of data: non-conformances, customer complaints, test results, process data, audit reports, corrective actions, supplier data, and engineering documentation. The challenge is not always a lack of data. Sometimes the challenge is actually extracting insights from it in time. This is where artificial intelligence can become a significant tool for quality professionals and engineers.

From Data to Insights

In many organizations, quality-related information is scattered across systems, files, reports, and free-text documents. AI tools can assist in analyzing large amounts of information, classifying it, and identifying recurring patterns. For example, suitable tools can be used to assist in analyzing:

• Customer complaints
• Failure reports
• Non-conformances
• Audit findings
• Supplier data
• Test results
• Corrective actions

The goal is not just to receive a faster summary. The real value begins when the information helps identify trends, relationships, and patterns that require attention.

AI and Non-Conformance Analysis

Suppose an organization accumulates hundreds of non-conformance reports. Some describe the same phenomenon in different words. An AI-based system can assist in classifying descriptions into groups, identifying recurring topics, and highlighting trends for professional review. This allows moving from a long list of events to more focused questions: Which failure recurs with high frequency? In which products does it appear? Is there a trend that was not previously identified?

AI Is Not Root Cause Analysis

Here it is important to make a distinction. An AI system can help identify connections, suggest lines of investigation, or organize information. But a statistical correlation or a pattern in text is not necessarily a root cause. Root Cause Analysis requires an understanding of the product, the process, working conditions, failure mechanisms, and engineering evidence. Therefore, AI can be a tool that assists the investigation – but the professional conclusion must be examined and verified by professionals.

AI and FMEA

Interesting uses can also be found in FMEA processes. For example, AI tools can help the team review historical data on failures, identify previously occurring Failure Modes, organize Lessons Learned, or suggest questions the team should examine. But FMEA is not an exercise in creating a list of failures. Its value stems from the discussion of a multidisciplinary team that is familiar with the system, the design, the usage conditions, and the engineering significance of each failure. Therefore, the correct use is: AI as process support – and not as a replacement for the FMEA team.

AI and Corrective Actions

In quality systems, a great deal of knowledge about problems that have already occurred accumulates over the years. When a new non-conformance is opened, AI can assist in locating similar events from the past: Was there already a similar failure? What was the root cause found? What corrective action was taken? And was it effective? Such an approach can turn the historical event log from an "archive" into an active organizational knowledge base.

AI in Supplier Quality Management

Advanced data analysis can also be integrated into the world of suppliers. When data on non-conformances, delivery quality, complaints, response times, and corrective actions exist, it is possible to search for patterns and trends over time. The goal is not necessarily to replace supplier evaluation. The goal is to provide quality personnel with a broader picture that will allow them to know where it is worth investigating in depth.

And What About Writing Quality Documents?

AI can also assist in daily work: summarizing documents, organizing information, drafting texts, comparing texts, helping to prepare questions for an audit, or turning unstructured information into an initial document. But here there is a significant trap. A document that looks professional is not necessarily a correct document. AI systems may generate incorrect information, omit context, or present an answer with confidence even when it is unfounded. Therefore, quality documents, standard requirements, and engineering decisions always require professional review and approval.

And What About Sensitive Information?

Before entering organizational information into AI tools, one must understand what happens to the data. Engineering documents, customer data, information on failures, specifications, drawings, and test results are not "just another file." They are the organization's knowledge assets, and sometimes information protected by agreements with customers or suppliers. Therefore, before inputting such information into AI tools, it is worth checking:

• Does the tool allow using the data for training or exposing it to a third party?
• Where is the information stored, for how long, and who can access it?
• Is there an organizational policy that defines what is allowed and what is forbidden to upload?
• Is there information that, according to contract or regulation, is forbidden to be taken out of the organization?

In many cases, the same value can be achieved with limited or anonymized information – without inputting full engineering documentation.

What Can Be Done Today?

AI can generate real value in quality management – when used correctly:

• Summarize and classify large volumes of quality information
• Identify trends and patterns in non-conformances, complaints, and supplier data
• Locate similar events from the past before opening an investigation
• Prepare drafts, summarize documents, and assist in preparing for audits

And what remains in the hands of the professionals:

• Determining the root cause
• FMEA decisions and evaluating the severity of the failure
• Supplier evaluation and business decisions
• Final approval of documents, standard requirements, and engineering decisions

The bottom line: AI is a tool that strengthens quality professionals – not a tool that replaces their judgment.

How to start in practice – in small and measurable steps – is described in the article How to start with AI in the quality department? 5 first steps.

Regarding the broader change – how Quality 4.0 is changing the role of the quality professional and what skills are required of them – read the article Quality 4.0 – How is the role of the quality professional changing, and what skills will be required?.

Want to implement AI in your organization's quality processes?

ALD Advanced Solutions accompanies organizations in process mapping, selecting solutions, and implementing AI in a controlled manner – including defining usage boundaries, controls, and data management.

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