By Yelna Yuristiary
Quality management has changed significantly over the past several decades.
In the past, quality was strongly associated with inspection, testing and finding defects. Organizations then moved toward quality assurance, process control, continuous improvement and risk-based thinking.
Today, another transformation is taking place.
It is often called Quality 4.0.
Quality 4.0 refers to the development of quality management in the era of Industry 4.0, where technologies such as artificial intelligence, big data, Internet of Things, cloud computing, automation and machine learning are increasingly used to support quality. The American Society for Quality (ASQ) describes Quality 4.0 as the future of quality and organizational excellence in the context of Industry 4.0.
But Quality 4.0 is not simply about buying new technology.
The bigger question is how technology can change the way organizations manage, measure and improve quality.
From traditional quality to Quality 4.0
Traditional quality management has often followed a familiar pattern.
A product or activity is performed, it is inspected, defects are identified, corrective action is taken and the process is improved.
This approach remains important.
But digital technologies create new possibilities.
Instead of waiting for an inspection to identify a problem, organizations can increasingly collect information continuously. Sensors can monitor processes, digital systems can collect large amounts of data, and analytical tools can identify patterns.
This means quality management can move from simply asking:
“Did something go wrong?”
to asking:
“What is happening in the process, and what might happen next?”
That is one of the important ideas behind Quality 4.0.
Quality 4.0 is more than technology
One of the most important points about Quality 4.0 is that it is not simply an IT project.
ASQ emphasizes that Quality 4.0 involves people, processes and technology. Quality professionals can play an important role in helping organizations use digital technologies while maintaining sound quality principles.
This is important because technology does not automatically create quality.
A company can have artificial intelligence, sensors and sophisticated dashboards but still experience poor quality if its processes are weak or employees do not understand their responsibilities.
Quality 4.0 therefore requires both technological capability and organizational capability.
What technologies are involved?
Quality 4.0 can involve many different technologies.
Artificial intelligence can help analyze information and identify patterns. Machine learning can support prediction and classification. Internet of Things devices can collect information directly from equipment and processes. Cloud systems can make information available across locations. Big-data technologies can help organizations analyze large and complex datasets.
In manufacturing, for example, sensors may continuously monitor equipment and production conditions.
In construction, digital systems can connect inspection records, photographs, material information, testing results and project performance.
The technology may be different from one industry to another, but the objective is similar:
Use better information to make better quality decisions.
Quality 4.0 in construction
The construction industry has a particularly interesting opportunity to apply Quality 4.0.
A construction project produces enormous amounts of information.
Consider concrete works.
A project may collect information about concrete supplier, mix design, temperature, slump, delivery time, casting location, cube tests and curing conditions.
Traditionally, these records may be stored separately.
With Quality 4.0, these different sources can potentially be connected.
A digital system could show the relationship between concrete quality and factors such as supplier, location, temperature or testing results.
Over time, the organization may be able to identify patterns that are difficult to see from individual reports.
The same principle can apply to waterproofing, welding, finishing, earthworks and infrastructure construction.
Quality becomes increasingly data-driven.
From inspection to intelligent inspection
Traditional inspection asks:
“Is this work acceptable?”
Quality 4.0 can add another question:
“What does the information tell us about the condition of the process?”
For example, imagine a project where waterproofing defects are gradually increasing.
A traditional inspection process identifies the defects.
A Quality 4.0 approach could combine inspection data with information about location, subcontractor, material, weather, installation conditions and previous defects.
The organization may then be able to identify recurring patterns.
This does not eliminate the inspector.
Instead, technology gives the inspector better information.
The role of the quality professional therefore changes from simply collecting observations to interpreting information and supporting decisions.
Quality 4.0 and artificial intelligence
Artificial intelligence is one of the technologies attracting significant attention in Quality 4.0.
AI can potentially analyze large datasets, identify patterns and support activities such as automated visual inspection, anomaly detection and prediction. ASQ identifies AI, machine learning, data science, sensors, cloud computing and IoT among technologies that can support Quality 4.0.
However, AI should not be treated as a replacement for quality professionals.
Quality decisions often involve context, engineering judgment, customer expectations, contractual requirements and ethical considerations.
AI can provide information.
People still need to determine what that information means and what action should be taken.
The future may therefore be less about AI replacing quality professionals and more about quality professionals working with AI.
The importance of data
Quality 4.0 depends heavily on data.
But having more data does not automatically mean having better quality.
If the data is incomplete, inaccurate or inconsistent, the results can also be unreliable.
For example, if one project records a defect as “waterproofing failure” while another records the same problem as “leakage” and another uses “membrane defect”, it may be difficult to analyze the information consistently.
Quality 4.0 therefore requires good data practices.
Organizations need to think about how data is collected, classified, stored, protected and analyzed.
In simple terms:
Good technology + poor data = poor information.
But:
Good technology + reliable data + good processes = better quality intelligence.
Quality professionals need new skills
Quality 4.0 also changes the skills required from quality professionals.
In addition to understanding quality standards and traditional quality tools, professionals increasingly need to understand data, digital systems and technology.
They may need to interpret dashboards, understand trends, evaluate data quality and work with digital teams.
This does not mean every Quality Manager needs to become a software engineer.
Instead, quality professionals need enough digital understanding to ask the right questions.
For example:
Where does this data come from?
Is the data reliable?
What does this trend mean?
What action should we take?
These questions may become increasingly important in the future of quality management.
Quality 4.0 and ISO 9001:2026
The development of Quality 4.0 is also relevant to the latest ISO 9001 revision.
ISO 9001:2026 was published on 16 September 2026 and replaces ISO 9001:2015. ISO says the revision reflects changes in the business environment, including digitalization and increasingly interconnected supply chains. It also strengthens areas such as leadership, quality culture, strategic alignment and the proactive treatment of risks and opportunities.
However, ISO 9001:2026 does not require organizations to implement a specific artificial intelligence system or Quality 4.0 technology.
The standard provides a management framework.
Organizations can then decide which technologies are appropriate for their own context.
This distinction is important.
Quality 4.0 is not an ISO requirement.
It is a broader development in how quality management can evolve in a digital environment.
The human side of Quality 4.0
There is another important issue.
As organizations become more digital, there is a risk of focusing too much on technology and not enough on people.
Quality is ultimately created by people working within processes.
Employees need to understand why data matters. They need to trust the system and be willing to report problems. Managers need to respond to information. Quality professionals need to understand both technical and human factors.
Recent quality literature has also begun discussing the transition from Quality 4.0 toward ideas associated with Quality 5.0, including greater attention to empathy, ethics and human engagement alongside advanced technology.
This suggests that technological progress does not make human factors less important.
In some ways, it makes them more important.
The future of Quality 4.0
Quality 4.0 can be understood as a gradual evolution.
Traditional quality focuses heavily on inspection and control.
Digital QMS focuses on connected quality information.
Quality 4.0 adds data, connectivity, automation and intelligent technologies.
Predictive quality then uses this information to identify potential problems before they become major failures.
The progression can therefore be expressed simply:
Quality Control
→ Find the defect
Quality Assurance
→ Prevent the defect
Digital QMS
→ Connect the information
Quality 4.0
→ Use digital technology to improve quality
Predictive Quality
→ Anticipate potential problems
The boundaries between these stages are not rigid. Organizations can use several approaches at the same time.
Quality 4.0 is ultimately about better decisions
The biggest mistake would be to define Quality 4.0 simply as “quality with AI”.
That definition is too narrow.
Quality 4.0 is about using modern technology and quality principles together to improve organizational performance.
ASQ identifies potential benefits including improving human intelligence, increasing the speed and quality of decision-making, improving transparency and traceability, and helping organizations anticipate change.
This gives Quality 4.0 a much broader meaning.
The ultimate question is not:
“How much technology do we have?”
It is:
“Does technology help us make better quality decisions?”
If the answer is yes, technology is creating value.
If the answer is no, digitalization may simply be creating more data without creating better quality.
The future of quality management will therefore not be determined by technology alone.
It will be determined by how effectively organizations combine people, processes, data and technology.
That is the real meaning of Quality 4.0.
