Beyond Continual Improvement: Why the Future of QMS Is Adaptive

 By Yelna Yuristiary

For decades, one of the central ideas in Quality Management has been continual improvement.

Organizations are expected to monitor performance, identify problems, take corrective action and improve their processes. This principle remains at the heart of ISO 9001.

But today's business environment raises a new question.

Is continual improvement enough when the environment itself is constantly changing?

Technology is developing rapidly. Customer expectations are changing. Supply chains are becoming more interconnected. Climate-related conditions can affect operations. Artificial intelligence is entering business processes, while new risks and opportunities can emerge much faster than traditional management systems can respond.

This is creating interest in a broader idea: the Adaptive Quality Management System, or Adaptive QMS.

An Adaptive QMS is not a new ISO standard. Rather, it is a way of thinking about how a QMS can remain effective when the organization's circumstances change.

ISO 9001:2026 reflects this changing environment. ISO says the new edition is intended to help organizations improve performance, respond to changing business conditions and strengthen resilience, while maintaining customer focus, the process approach, risk-based thinking and continual improvement.

From Continual Improvement to Continuous Adaptation

Continual improvement traditionally asks:

“How can we make this process better?”

An adaptive approach adds another question:

“Does this process still make sense under today's conditions?”

This is an important difference.

A process may have been effective five years ago but become less effective because technology, suppliers, regulations, customer requirements or operating conditions have changed.

Improving the old process may not be enough.

Sometimes the organization needs to redesign the process itself.

For example, a company may have improved a paper-based inspection process for years. But if the organization has moved to a digital QMS, continuing to optimize the paper process may no longer create much value.

The organization needs to adapt.

A QMS Should Not Become Static

One of the risks of a mature management system is that it can become highly structured but less responsive.

Procedures are written. Forms are created. responsibilities are assigned. Internal audits are scheduled.

Everything appears controlled.

But the external environment continues to change.

A QMS can therefore become “stable” while the business around it becomes unstable.

This is why an effective QMS needs both control and adaptability.

Control provides consistency.

Adaptability allows the organization to respond when circumstances change.

The challenge is finding the right balance between the two.

ISO 9001:2026 and the Changing Business Environment

This concept is particularly relevant to ISO 9001:2026.

The new edition strengthens the connection between quality management, leadership, organizational culture, strategic alignment, risks and opportunities. ISO also describes the revision as a way to help organizations remain relevant in a rapidly changing business environment.

Importantly, ISO 9001:2026 does not abandon continual improvement.

Instead, it builds on it.

The standard continues to require organizations to monitor, measure, analyse and evaluate QMS performance and to continually improve the system's effectiveness.

The emerging idea of an Adaptive QMS can therefore be understood as an evolution in how organizations apply continual improvement, rather than a replacement for the ISO 9001 approach.

What Makes a QMS Adaptive?

An adaptive QMS needs the ability to detect change.

This starts with organizational context.

Organizations need to understand what is happening inside and outside the business and identify issues that could affect the QMS.

The next step is interpretation.

Not every change requires a change to the QMS. The organization needs to determine which changes are relevant and what their potential consequences may be.

Then comes response.

The organization may need to change a process, update a risk assessment, modify a KPI, strengthen competence, change a supplier control or introduce new technology.

Finally, the organization needs to learn from the result.

This creates a continuous cycle:

Sense → Understand → Respond → Learn → Adapt.

This is broader than simply finding defects and correcting them.

Construction Provides a Clear Example

Consider a construction project operating under increasingly difficult weather conditions.

A traditional quality system may already have procedures for concrete placement, curing, waterproofing and material storage.

But if environmental conditions change significantly, the project may need to adapt its controls.

Concrete placement procedures may need additional temperature monitoring.

Waterproofing controls may need stronger moisture checks.

Material storage requirements may need to change.

Inspection planning may need to become more flexible.

The quality system is still controlling the work.

But it is adapting its controls to changing conditions.

That is the basic idea behind an Adaptive QMS.

Digitalization Makes Adaptation Faster

Digital QMS can accelerate this process.

In a paper-based system, information may take days or weeks to reach management.

In a digital environment, quality data can potentially become available much faster.

This means that organizations can identify changes earlier.

For example, a dashboard may show that NCRs are increasing in one project area. Another indicator may show that the same subcontractor is responsible for a growing percentage of defects.

Management can investigate and respond before the problem becomes significantly larger.

The technology does not create adaptability by itself.

The organization must still have the authority, competence and culture to act on the information.

AI Could Strengthen Adaptive QMS

Artificial intelligence adds another dimension.

AI can analyse large amounts of information and identify patterns that may be difficult to see through traditional reporting.

For example, AI-supported analysis could identify relationships between supplier performance, environmental conditions, inspection results and defect patterns.

This can help organizations detect changes earlier.

But AI should not automatically change the QMS.

The organization still needs human judgement.

A prediction is not the same as a fact.

An anomaly is not automatically a nonconformity.

AI can provide information, but people need to understand the context and determine the appropriate response.

This makes the human-AI relationship discussed in the previous article particularly important.

Adaptive KPIs

An Adaptive QMS may also require a different approach to KPIs.

Traditional KPIs are often established and reviewed periodically.

But some indicators may become more or less relevant as circumstances change.

For example, a construction project may initially focus heavily on concrete quality. Later, waterproofing defects may become the dominant quality concern.

The KPI structure may therefore need to evolve.

This does not mean changing KPIs simply because the numbers are inconvenient.

It means ensuring that the measurement system continues to reflect the organization's current objectives, risks, opportunities and quality priorities.

A KPI should remain useful for decision-making.

The Role of Quality Culture

Adaptation also depends heavily on people.

A company cannot have an adaptive QMS if employees are afraid to report problems.

If people hide defects because they are worried about blame, management receives incomplete information.

The system then adapts too slowly.

This is why quality culture is important.

ISO 9001:2026 places stronger emphasis on leadership, quality culture, accountability and people's awareness of their contribution to QMS effectiveness.

An adaptive organization needs people who are willing to say:

“This process is no longer working as expected.”

That statement should be treated as useful information rather than automatically as a failure.

Adaptive Does Not Mean Uncontrolled

There is sometimes a misunderstanding about adaptability.

Being adaptive does not mean changing procedures every week.

It does not mean allowing employees to ignore established processes whenever they want.

And it does not mean abandoning standardization.

Quite the opposite.

A strong adaptive QMS needs a stable foundation.

Processes, responsibilities, requirements and controls still need to be defined.

The difference is that the organization also has a mechanism for reviewing whether those controls remain appropriate.

The principle is:

Standardize what should be stable. Adapt what needs to change.

From Corrective Action to Organizational Learning

Another important change is the way organizations think about corrective action.

A traditional approach may focus on eliminating the immediate cause of a nonconformity.

An adaptive organization asks a broader question:

“What can this event teach us about the way our system operates?”

Suppose a project experiences repeated waterproofing defects.

The immediate corrective action might involve repairing the defective area.

A broader learning approach examines whether the problem is related to subcontractor competence, material selection, inspection timing, environmental conditions, design details or weaknesses in the quality planning process.

The objective is not simply to close the NCR.

It is to improve the organization's ability to recognize and respond to similar situations in the future.

Adaptive QMS and Resilience

This leads to another important concept: resilience.

ISO describes the 2026 revision as reflecting the need for organizations to respond to new challenges with greater speed, resilience and transparency.

Quality resilience means more than maintaining production during a crisis.

It means maintaining the ability to deliver intended quality results when circumstances change.

A resilient QMS can absorb disruption, learn from it and adjust its controls.

This can be particularly important for organizations with complex supply chains, major construction projects, manufacturing operations or critical services.

The Future Quality Manager

This evolution also changes the role of the Quality Manager.

The traditional Quality Manager is often associated with procedures, inspections, audits, NCRs and corrective actions.

The future Quality Manager may increasingly become a system intelligence and adaptation professional.

They will need to understand quality data, organizational context, risk, technology, people and business performance.

They will need to ask:

What has changed?

Why does it matter?

What evidence do we have?

Which quality controls are affected?

What should be changed?

And how will we know that the change worked?

These questions move quality management closer to strategic decision-making.

Toward an Adaptive Quality Management System

The evolution of Quality Management can therefore be viewed as a gradual progression.

First came Quality Control, focused on detecting defects.

Then Quality Assurance, focused on preventing problems through controlled processes.

Then Quality Management Systems, focused on managing quality across the organization.

Digital QMS brought greater connectivity and visibility.

Quality 4.0 introduced advanced technologies and data-driven quality.

Predictive Quality focuses on identifying potential problems earlier.

The next step is not necessarily another technology.

It is the ability to adapt the entire quality system as conditions change.

That is the idea behind the Adaptive QMS.

Quality in a Changing World

The future of quality will not be defined only by how well an organization follows its procedures.

It will also be defined by how effectively the organization recognizes when those procedures, controls, indicators or assumptions need to change.

A good QMS provides consistency.

A mature QMS provides continual improvement.

An adaptive QMS adds the ability to respond intelligently when the environment changes.

The question therefore evolves from:

“How can we continually improve our system?”

to:

“How can we ensure that our quality system remains effective as our organization and environment change?”

That may be one of the most important questions for the next generation of Quality Management.

Because the future of quality is not simply about improving yesterday's process.

It is about ensuring that the organization is ready for tomorrow's conditions.

Human + AI: How Artificial Intelligence Is Changing the Future of Quality Auditing

By Yelna Yuristiary

For decades, quality auditing has depended heavily on human auditors.

Auditors review documents, interview employees, observe processes, examine records and evaluate evidence. Their experience helps them identify weaknesses that may not be obvious from a checklist.

But the amount of information available to organizations is changing rapidly.

Digital QMS platforms can contain thousands of inspection records, test results, complaints, NCRs, supplier evaluations and performance indicators. Artificial intelligence can analyse large amounts of information much faster than a human auditor.

This raises an important question:

If AI can analyse the data, what will happen to the quality auditor?

The answer may not be that AI replaces auditors. Instead, the future may be a combination of human judgement and artificial intelligence.

Auditing Is Already Becoming More Digital

Quality auditing itself is changing.

The newly published ISO 19011:2026 provides guidance for auditing management systems and reflects changes in organizational operations, including technology, digitalization and virtual environments. The new edition also places greater emphasis on risk analysis and mitigation.

This is important because auditing is no longer limited to reviewing paper documents in a physical office.

Auditors increasingly encounter electronic records, cloud-based systems, digital workflows, remote operations, automated inspections and AI-supported processes.

The audit therefore needs to understand not only what the organization does, but increasingly how technology influences what the organization does.

AI Can Help Auditors Find Patterns

Imagine an auditor reviewing five years of quality data from a construction company.

There may be thousands of inspection records, hundreds of NCRs and numerous supplier performance reports.

A human auditor can review samples and investigate specific areas. AI, however, can potentially analyse a much larger dataset and identify unusual patterns.

For example, an AI system might identify that certain types of defects occur more frequently with a particular supplier, subcontractor, location, material or process.

The auditor can then investigate whether the pattern represents a genuine systemic issue.

This changes the role of the auditor.

Instead of spending most of the audit trying to find where the information is, the auditor can spend more time understanding why the pattern exists and what it means for the QMS.

From Sampling to Intelligent Sampling

Sampling will remain important in auditing.

Auditors cannot normally examine every single record, particularly in large organizations.

However, AI can potentially help make sampling more data-driven.

Suppose a project has 10,000 inspection records.

Instead of selecting samples only through traditional methods, an analytical system could identify records with unusual characteristics, repeated failures or other patterns that deserve attention.

The auditor can then investigate those areas.

The purpose is not to allow AI to decide that something is nonconforming.

The purpose is to help the auditor identify where further audit attention may be useful.

Human judgement remains essential.

AI Can Support Evidence Analysis

Audit evidence is another area where AI can be useful.

A modern organization may have information in many formats: inspection reports, emails, meeting minutes, photographs, test results, dashboards, complaints and corrective-action records.

AI can help organize and analyse this information.

For example, it may identify repeated references to the same problem across different records.

A quality auditor can then follow the evidence back to the relevant process and determine whether the issue represents an isolated event or a systemic weakness.

This can make audits more efficient.

But efficiency does not automatically mean effectiveness.

The auditor still needs to evaluate whether the evidence is reliable, relevant and sufficient.

AI Can Also Audit AI

An especially interesting development is that organizations are increasingly using AI within their own management systems.

This creates a new auditing question:

How do you audit a QMS that uses AI?

The ISO 9001 Auditing Practices Group published a paper in June 2026 specifically titled “Auditing a QMS that uses Artificial Intelligence.” The group explains that the paper is intended to provide insights for auditors dealing with QMS environments that use AI.

This is significant because AI is no longer simply an external tool used by auditors.

It can become part of the organization's actual operational processes.

For example, an organization may use AI to predict equipment failures, classify customer complaints, inspect products, evaluate suppliers or identify quality anomalies.

The auditor then needs to understand how that AI-supported process is controlled.

What Should an Auditor Ask About AI?

The auditor may need to understand where the AI system gets its data, how the system is used, who is responsible for decisions and what happens when the AI produces an unexpected result.

For example, if an AI system identifies a potential quality problem, does a qualified person review the result?

If the AI produces a false warning, is there a process for correcting it?

If the AI fails to identify a real defect, how is that failure detected?

And if the AI model changes over time, does the organization understand the consequences?

These questions move auditing beyond traditional document checking.

They require auditors to understand the relationship between data, technology, people and processes.

The Problem of “Black Box” Decisions

One challenge is explainability.

Some AI systems can produce highly sophisticated predictions, but the reasoning behind a particular result may not always be easy for a user to understand.

This can create difficulties in quality management.

Quality decisions may affect product acceptance, customer satisfaction, safety, cost and contractual obligations.

An organization therefore needs to understand the appropriate level of human oversight for the AI applications it uses.

This is also consistent with the broader development of AI management standards. ISO/IEC 42006:2025 establishes requirements for bodies that audit and certify AI management systems against ISO/IEC 42001, reflecting the need for appropriate competence and rigor when evaluating AI-related risks and controls.

The Auditor Still Needs Professional Judgement

AI can analyse data.

But auditing is not simply data analysis.

An auditor needs to understand the organizational context, process interactions, requirements, evidence and consequences.

Imagine that an AI system identifies an unusually high number of concrete quality issues in one project area.

The number alone does not explain the situation.

Perhaps the area contains the largest volume of concrete work.

Perhaps the testing frequency is higher there.

Perhaps the project has introduced a new supplier.

Perhaps the data collection method changed.

Or perhaps there really is a systemic quality problem.

The auditor needs to investigate.

This is where professional judgement remains important.

AI Should Support the Auditor, Not Become the Auditor

A useful way to think about the future is:

AI finds patterns.

The auditor investigates.

AI processes information.

The auditor evaluates evidence.

AI can identify anomalies.

The auditor determines their significance.

This creates a human-AI partnership rather than a competition.

The objective is not to make the auditor unnecessary.

It is to allow the auditor to spend less time on repetitive information processing and more time on analysis, questioning and understanding the effectiveness of the management system.

Quality Auditors Will Need New Skills

This transformation will also change auditor competence.

The traditional auditor needs knowledge of auditing principles, management systems, processes and relevant requirements.

The future auditor may additionally need a basic understanding of data analytics and AI.

They do not necessarily need to become programmers.

But they should be able to ask important questions.

Where did the data come from?

Is the data reliable?

What assumptions does the system use?

What are the limitations of the AI?

Who reviews the output?

How are errors detected?

How does the organization control changes to the system?

These questions will become increasingly relevant as AI becomes embedded in business processes.

The Future Audit May Be More Continuous

Traditional audits often happen at defined intervals.

An organization may conduct an internal audit once or twice a year and then produce a report.

Digital systems create the possibility of more continuous monitoring.

Quality data can be monitored throughout the year, while AI or analytics can identify unusual trends between formal audits.

The auditor can then use this information when planning future audit activities.

This does not necessarily mean that formal audits disappear.

Instead, the audit programme may become more connected to continuously available performance information.

ISO 19011:2026 continues to emphasize managing audit programmes, risk and opportunities, conducting audits and improving audit programmes.

The Future Quality Auditor

The quality auditor of the future may therefore look quite different from the traditional image of an auditor carrying a checklist.

The future auditor may work with dashboards, data analytics, digital records and AI-supported analysis.

But the fundamental purpose remains.

The auditor still needs to determine whether the management system is implemented effectively, whether evidence supports conclusions and whether the organization can achieve its intended results.

Technology changes the tools.

It does not remove the need for professional judgement.

From Checklist Auditor to Quality Intelligence Professional

This may be one of the biggest changes in quality auditing.

The traditional audit question is often:

“Can you show me the procedure?”

A more data-driven audit may ask:

“Show me what the data says about how this process actually performs.”

And an AI-supported audit may go one step further:

“What patterns or risks can we identify before they become significant problems?”

This does not make the checklist irrelevant.

Instead, the checklist becomes only one part of a much broader evidence-based approach.

The auditor becomes increasingly involved in understanding how the organization's processes actually perform.

Human + AI Is the Real Future

The future of quality auditing is therefore unlikely to be simply human versus AI.

It is more likely to be human plus AI.

AI can process enormous amounts of information, identify patterns and support risk-based audit planning.

Human auditors can provide context, professional judgement, questioning, interpretation and ethical responsibility.

Neither side provides the complete solution by itself.

The strongest audit environment may be one where technology helps auditors see more, while human judgement helps organizations understand what those findings actually mean.

The question for the future is therefore not:

“Will AI replace the quality auditor?”

A more useful question is:

“How can quality auditors use AI to conduct more evidence-based, risk-focused and valuable audits while maintaining human judgement and accountability?”

That is where the next generation of quality auditing is beginning.

Quality Management and Climate Change: Why Climate Risk Is Becoming a Quality Issue

 By Yelna Yuristiary

Climate change is often discussed as an environmental issue. However, it is increasingly becoming a business, operational and quality issue as well.

Extreme weather, changing temperatures, heavy rainfall, flooding, water shortages and other climate-related conditions can affect an organization's ability to deliver products and services consistently. They can also affect suppliers, workers, materials, infrastructure and customers.

This raises an important question for Quality Management Systems:

Can climate change affect the organization's ability to achieve its intended quality results?

For ISO 9001 users, the answer is not something that should simply be assumed. Organizations are expected to determine whether climate change is a relevant issue for their Quality Management System and consider relevant requirements from interested parties. This climate-change consideration was introduced into ISO 9001 through the 2024 amendment and is carried forward in ISO 9001:2026.

Climate Change Is Not Automatically an Environmental Topic

When people hear “climate change,” they often immediately think about carbon emissions, renewable energy or environmental management.

Those issues can be important, but a Quality Management System looks at the issue from a different perspective.

The question is whether climate-related conditions can affect the organization's processes and its ability to consistently deliver conforming products or services.

For example, a construction company may experience extreme rainfall that affects earthworks, concrete placement, waterproofing activities or material storage. A manufacturer may experience temperature changes that affect production conditions or raw materials. A logistics company may face disruptions to transportation routes.

In these situations, climate conditions can become directly connected to quality performance.

What Does ISO 9001 Say?

The 2024 amendment to ISO 9001 added a specific requirement to Clause 4.1 asking organizations to determine whether climate change is a relevant issue.

Clause 4.2 also recognizes that relevant interested parties may have requirements related to climate change. ISO and the International Accreditation Forum have explained that the purpose is to ensure organizations consider climate change when determining their context and relevant interested-party requirements.

This does not mean that every organization must automatically create a climate-change program under its QMS.

Instead, the organization needs to consider the issue and determine whether it is relevant to the QMS and its intended results.

That distinction is important.

From Climate Risk to Quality Risk

Imagine a construction project working in an area where heavy rainfall is becoming more frequent.

The immediate concern may appear to be environmental or scheduling risk. But the consequences can also affect quality.

Rain can affect concrete placement, earthwork compaction, waterproofing, coatings, excavation stability and material storage. If activities continue under unsuitable conditions, the project may experience defects, rework or additional inspection requirements.

The quality team therefore needs to understand the connection:

Climate condition → process disruption → quality risk → potential defect → cost and schedule impact.

This is where climate considerations can become part of quality management.

Construction Projects Are a Good Example

Construction is particularly sensitive to environmental conditions because many activities take place outdoors.

Concrete quality can be affected by temperature, rainfall, transportation conditions and curing conditions.

Waterproofing work can be affected by moisture and surface conditions.

Painting and coating activities can be affected by temperature and humidity.

Earthworks can be affected by rainfall and soil moisture.

Marine construction can face additional challenges from waves, wind, tides and changing environmental conditions.

The quality manager does not necessarily need to become a climate scientist. The important responsibility is to understand whether changing conditions can affect the project's processes and quality objectives.

Quality Planning May Need to Change

Climate-related considerations can influence quality planning.

For example, a project may establish specific controls for concrete placement during very hot weather. The project may also establish procedures for protecting fresh concrete during rainfall or adjusting work schedules when environmental conditions exceed specified limits.

The same principle can be applied to waterproofing, coating, asphalt works and other activities that depend on environmental conditions.

This is an example of moving from simply reacting to defects toward planning for the conditions that can create them.

Climate Change and Supply Chains

Climate-related disruption does not only happen at the project site.

Modern organizations often depend on complex supply chains.

A supplier may experience production disruption, transportation problems or shortages of certain materials because of extreme weather. These problems can eventually affect delivery time, material availability and product conformity.

This means that climate considerations can also become relevant when evaluating external providers.

For a construction project, for example, quality management may need to consider the reliability of suppliers for concrete materials, steel, waterproofing products, coatings and other critical materials.

The objective is not to predict every possible climate event.

The objective is to understand which external conditions could affect the organization's ability to deliver quality.

Climate Change and Quality KPIs

Climate considerations can also influence Quality KPIs.

A project may normally monitor concrete cube test pass rate, waterproofing defect rate, finishing defect density, NCR trends and corrective-action closure.

But management may also need to understand whether certain quality problems are associated with environmental conditions.

For example, if waterproofing defects increase during periods of heavy rainfall, the organization can investigate whether surface preparation, moisture control or work scheduling needs to be improved.

This creates an interesting relationship between environmental data and quality data.

Instead of looking at quality data independently, organizations can begin to ask:

“What external conditions are influencing our quality performance?”

This is an important step toward predictive quality management.

Digital QMS Can Help

Digital Quality Management Systems can make this process easier.

If quality data, weather information, inspection results, material data and project records are available digitally, organizations can analyse relationships between them.

For example, a project could compare concrete quality results with temperature and delivery-time data.

Over time, patterns may become visible.

This does not mean that every organization needs artificial intelligence. Even a well-designed dashboard can help quality professionals identify relationships that would be difficult to see in paper-based records.

AI and predictive analytics may eventually provide more advanced capabilities, but the foundation remains the same:

Reliable data + relevant indicators + good analysis + informed human decisions.

Climate Change and ISO 9001:2026

The latest ISO 9001 edition, published on 16 September 2026, continues the climate-change consideration introduced in the previous amendment. At the same time, ISO 9001:2026 places greater emphasis on leadership, organizational culture, strategic alignment and the distinction between risks and opportunities.

This creates an important connection.

Climate change should not necessarily be treated as a separate activity sitting outside the QMS.

Where it is relevant, it can be considered within the organization's existing approach to context, interested parties, risks, opportunities, objectives, operational planning and performance evaluation.

In other words, organizations do not necessarily need a completely separate “climate quality system.”

They need to understand where climate-related issues interact with their existing management system.

Quality and Sustainability Are Becoming Connected

Another important trend is the growing connection between quality, environmental management and sustainability.

ISO 9001 focuses on quality management, while standards such as ISO 14001 address environmental management. These systems have compatible structures, making integration possible. ISO has also continued developing climate-related guidance, including ISO 14002-3:2026 on addressing climate-related environmental aspects and conditions within an environmental management system.

For organizations using integrated management systems, this can provide an opportunity to look at quality, environmental and operational risks together.

For example, a construction company may consider how climate conditions affect environmental performance, worker safety, construction quality and project delivery at the same time.

The systems remain distinct, but the risks and processes can be interconnected.

The Role of the Quality Professional Is Changing

This trend also changes the role of quality professionals.

The traditional quality professional might focus mainly on specifications, inspections, testing, NCRs, audits and corrective actions.

The modern quality professional increasingly needs to understand the wider context in which quality is produced.

That includes supply-chain disruption, digitalization, climate-related conditions, organizational culture and changing customer expectations.

The quality professional does not need to own every one of these subjects.

But they need to understand how these factors can influence the organization's ability to consistently achieve quality results.

The Future: Quality That Can Adapt

The traditional idea of quality is often associated with consistency.

Consistency remains important.

But in a rapidly changing environment, organizations also need the ability to adapt while maintaining quality.

This may mean adjusting construction methods, changing inspection frequencies, strengthening supplier controls, improving environmental monitoring or using new data sources to identify emerging risks.

The objective is not simply to maintain the same process regardless of circumstances.

The objective is to maintain the intended quality outcome even when circumstances change.

That is a much more dynamic view of Quality Management.

Quality Management Is Becoming More Resilient

Climate change is therefore not simply a new environmental topic for the Quality Department.

Where relevant, it can become part of a broader discussion about organizational resilience.

Can the organization continue to deliver conforming products and services when conditions change?

Can its suppliers continue to perform?

Can its processes adapt?

Can its quality controls identify new risks early?

And can management use reliable information to make timely decisions?

These are quality questions as much as they are business questions.

The future of Quality Management may therefore be less about creating a perfect system for a stable environment and more about creating a quality system that can remain effective when the environment changes.

That is the deeper meaning of climate change for QMS.

The question is no longer only:

“Are we producing quality today?”

It is also:

“Can our organization continue to produce quality when tomorrow's conditions are different from today's?”

That is where climate change, quality management and organizational resilience begin to meet.

Quality KPIs That Matter: Why Measuring Quality Is No Longer About Counting Defects

 By Yelna Yuristiary

For many organizations, quality is still measured by a familiar question: How many defects did we find?

The number of nonconformities, rejected materials, customer complaints, rework hours and failed inspections are often used as the main indicators of quality performance. These numbers are important, but they tell only part of the story.

Modern Quality Management Systems are moving toward a broader question: Are our quality processes actually helping the organization achieve better results?

This is becoming increasingly important with ISO 9001:2026. The new edition continues to require organizations to monitor, measure, analyse and evaluate the performance and effectiveness of their Quality Management System. ISO also emphasizes measurable quality objectives, relevant performance indicators and the use of data for informed decision-making.

From Quality Metrics to Quality KPIs

Not every measurement is a Key Performance Indicator.

A quality department may collect hundreds of numbers every month: number of inspections, number of test reports, number of NCRs, number of concrete tests, number of audits, number of documents reviewed and so on.

But having more data does not automatically mean having better quality management.

A KPI should help management understand whether an important quality objective is being achieved. In simple terms, a good KPI should answer three questions: What are we trying to achieve? How are we performing? What should we do next?

This is why the future of quality management is not simply about collecting more data. It is about selecting the right data.

Lagging KPIs: What Has Already Happened?

Traditional quality indicators are often lagging indicators. They measure the results of something that has already happened.

For example, a construction project may measure the number of concrete failures, waterproofing defects, finishing defects, NCRs or customer complaints.

These indicators are useful because they show the actual quality outcome. If the defect rate increases, management has evidence that something is going wrong.

However, the problem is timing.

By the time a defect appears in a final inspection, the organization may already have spent money on materials, labour and rework.

This is why relying only on lagging indicators can make quality management reactive.

Leading KPIs: Can We See the Problem Earlier?

Leading indicators attempt to provide information before a major quality failure occurs.

For example, instead of only measuring the number of concrete cube failures, a project could also monitor the percentage of pre-pour inspections completed correctly, concrete temperature trends, slump-test compliance, supplier performance and the timeliness of testing.

The purpose is not to replace the final quality result. Instead, these indicators provide earlier signals that can help the project team take action.

This creates an important shift:

Lagging KPI: What went wrong?

Leading KPI: What conditions could cause something to go wrong?

The combination is much more powerful than either one alone.

A Construction Example

Consider waterproofing works in a building project.

A traditional quality report might show the number of waterproofing defects found during inspection. For example, 15 defects were identified in one month.

That information is useful, but management still needs to ask why the defects occurred.

A broader KPI system could monitor inspection rejection rate, surface preparation compliance, inspection requests submitted before waterproofing, water ponding test results, recurring defect locations and corrective-action closure time.

Now the quality team has more information about the process, not just the final defect.

The same principle can be applied to concrete works. A project may track Concrete Cube Test Pass Rate as a lagging quality KPI while also monitoring mix-design compliance, batching accuracy, concrete temperature, slump compliance, delivery time and sampling procedures as leading indicators.

This creates a much clearer picture of quality performance.

The KPI Should Lead to a Decision

One of the biggest problems with quality KPIs is that organizations sometimes measure indicators simply because they are easy to calculate.

A monthly report may contain dozens of charts, but nobody asks what decision should be made from them.

A useful KPI should have a connection to action.

If the waterproofing defect rate increases, the team should investigate the process.

If corrective-action closure time is increasing, management may need to examine resources, responsibility or the effectiveness of the corrective-action process.

If supplier nonconformities are increasing, procurement and quality teams may need to review supplier performance.

The KPI therefore becomes more than a reporting number. It becomes a management signal.

Quality KPIs Should Be Connected to Business Performance

Another important development is the connection between quality and organizational performance.

Quality is not isolated from cost, schedule, customer satisfaction and productivity.

Poor quality can create rework, delays, additional material consumption, disputes and customer dissatisfaction.

For example, a project may have a low NCR rate but still experience significant rework. This could happen because not every quality problem is formally recorded as an NCR.

Therefore, quality KPIs should be connected with other business indicators.

A mature system might look at quality cost, rework, productivity, schedule impact, customer complaints and defect recurrence together.

This helps management understand the business impact of quality, rather than treating quality as a separate department's responsibility.

Pareto Analysis Can Make KPIs More Useful

Quality KPI data becomes even more useful when combined with established quality tools.

One example is the Pareto chart.

Suppose a building project records 100 finishing defects. Instead of simply reporting that there were 100 defects, the quality team can classify them into categories such as paint defects, tile defects, cracks, doors and windows, ceiling defects and other problems.

A Pareto analysis can then identify which categories contribute most to the total number of defects.

This changes the management question from:

“How many defects do we have?”

to:

“Which problems should we investigate first?”

The KPI tells us the size of the problem. The quality tool helps us understand where to focus the investigation.

From KPI Reporting to Quality Intelligence

This is where quality management is beginning to connect with the trends discussed in Digital QMS, Quality 4.0 and predictive quality.

When KPI data is collected digitally and analysed over time, organizations can begin to identify patterns.

For example, a project may discover that waterproofing defects increase after certain subcontractors are assigned to a particular area. A marine project may identify relationships between environmental conditions and coating defects. An infrastructure project may identify recurring quality problems associated with a particular material supplier.

The value is no longer in the individual KPI.

The value comes from the relationship between multiple indicators.

This is an important step toward predictive quality management.

Good KPIs Need Good Measurement

There is also a less visible issue behind every KPI: measurement reliability.

A KPI is only as useful as the data behind it.

If inspection records are incomplete, test results are unreliable, definitions are inconsistent or different project teams calculate the same KPI differently, management may make decisions based on misleading information.

This is why measurement systems and measurement resources remain important in quality management. ISO 10012:2026, for example, provides requirements for measurement management systems intended to ensure confidence in the validity and reliability of measurement results.

In other words, organizations should not only ask, “What is our KPI?”

They should also ask, “Can we trust the number?”

The Future Quality Dashboard

The future quality dashboard will probably contain fewer meaningless numbers and more meaningful indicators.

Instead of displaying 50 separate measurements, a project dashboard might show a balanced set of indicators covering prevention, process performance and results.

For example, a construction project could monitor quality planning performance, inspection compliance, supplier quality, testing performance, NCR trends, corrective-action closure, rework and customer-related defects.

The exact KPIs will depend on the organization's objectives, processes and risks. ISO 9001 does not prescribe one universal KPI list for every organization. Instead, organizations need to determine the objectives and measurements that are relevant to their own context and intended results.

Quality KPIs Are Becoming Management Tools

The most important change is therefore not the introduction of another dashboard.

It is a change in the way organizations think about measurement.

In the past, quality KPIs were often used to demonstrate that the quality department had completed its activities.

The emerging approach is different.

Quality KPIs should help management understand performance, identify emerging problems, allocate resources and improve processes.

The question is no longer simply:

“How many defects did we have?”

It is:

“What does our quality data tell us about the performance of our organization, and what should we do next?”

That is the real purpose of a Quality KPI.

And as organizations move toward Digital QMS, Quality 4.0 and predictive quality, the most valuable quality professional may not be the person who produces the most reports.

It may be the person who can look at the right indicators, understand what they mean, connect them to the process, and turn the data into a better decision.

Quality 4.0: How Technology Is Changing the Future of Quality Management

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.

Digital QMS: Why Quality Management Is Moving Beyond Paper and Excel

 For many years, quality management has depended on paper forms, checklists, Excel files, inspection reports and large amounts of documentation. These tools remain useful, but the way organizations manage quality is changing rapidly.

Today, companies are increasingly expected to manage quality information faster, more accurately and more effectively. This is where the Digital Quality Management System, or Digital QMS, is becoming increasingly important.

ISO describes a digital QMS as a way of delivering quality management digitally rather than relying mainly on paper forms and checklists. Digital systems can help organizations save time, reduce errors and improve access to quality information.

The basic idea is simple: quality management is moving from managing documents to managing information.

From paper forms to digital information

In a traditional quality system, an inspection may begin with a paper form. The form is completed, reviewed and signed before the information is transferred into an Excel file. Later, the data may be included in a monthly report for management.

This process can work, but it can also be slow.

By the time management sees a quality problem in a monthly report, several weeks may already have passed.

A Digital QMS can shorten this process. An inspector can enter information directly into a digital system, attach photographs and record the inspection result. The information can then become available to the responsible team and management without waiting for the end of the month.

The objective is not simply to eliminate paper. The bigger objective is to make quality information available when it is actually needed.

Why this matters in construction

Construction projects are particularly suitable for Digital QMS because they generate large amounts of quality information.

A single project may have hundreds or thousands of inspection requests, material approvals, laboratory test results, NCRs, audit findings, photographs and corrective-action records.

Traditionally, these records may be stored in different Excel files, folders or systems. One person may maintain the NCR register, another may manage inspection records, while laboratory results are kept separately.

The organization may therefore have a lot of data without having a clear picture of its overall quality performance.

A Digital QMS can connect these records.

For example, a concrete pour can be connected with its supplier, mix design, delivery information, temperature, slump test, cube test, location and final test result. If a problem occurs, the quality team can more easily trace the history of that particular work.

This creates something more valuable than a collection of documents: a connected quality record.

From quality records to quality KPIs

Digital QMS can also change the way quality KPIs are measured.

Consider a simple indicator such as the Concrete Cube Test Pass Rate. The organization needs to know how many tests were passed and how many tests were conducted.

The formula is straightforward:

Pass Rate = Passed Tests ÷ Total Tests × 100%

In a traditional system, someone may need to collect the results manually and calculate the KPI at the end of each reporting period.

In a digital system, new test results can be entered directly into the database and the KPI can be updated automatically.

The same principle can be applied to waterproofing defects, finishing defects, NCR closure, audit findings and other quality indicators.

This means the Quality Manager can spend less time compiling numbers and more time understanding what those numbers mean.

From monthly reports to faster decisions

The real value of digitalization is not the dashboard itself.

It is the speed at which information can lead to action.

Imagine that the number of waterproofing defects begins to increase. In a traditional system, the increase may only become visible when the monthly quality report is prepared.

With a Digital QMS, the trend can become visible much earlier.

The system can show that defects are increasing in a particular location, activity or subcontractor. The Quality Manager can then investigate the cause and take action before the problem becomes larger.

This creates a different quality-management cycle:

Information → Analysis → Decision → Action

rather than simply:

Information → Report → Filing

That is an important change in the role of quality management.

Digital QMS and predictive quality

Digital QMS is also closely connected to another important trend: predictive quality management.

Predictive quality requires reliable historical and current data. A digital QMS can provide that foundation.

For example, if an organization records inspection results, defect types, subcontractors, locations and corrective actions over several years, it can begin to identify recurring patterns.

The organization may discover that certain types of defects frequently occur under particular conditions.

This information can then be used to provide early warnings.

More advanced organizations may use data analytics or artificial intelligence to identify patterns that are difficult to see manually.

However, artificial intelligence is not a requirement for a Digital QMS. Even relatively simple tools such as trend analysis, Pareto charts and control charts can help organizations understand whether quality performance is changing.

The important principle is not the technology.

It is the ability to use quality information before a problem becomes a major failure.

Digital does not mean eliminating Excel

There is sometimes a misconception that digital transformation means companies must immediately stop using Excel.

That is not necessarily the case.

Excel remains a useful tool for many quality professionals.

The bigger problem is when important information becomes scattered across too many disconnected files.

A project may have one Excel file for NCRs, another for inspections, another for laboratory results and another for KPIs. Each file may be correct individually, but connecting them can become difficult.

The goal of Digital QMS is therefore not simply to replace Excel.

It is to create a more connected quality-information environment.

Technology cannot replace quality culture

Another important point is that technology alone cannot create a strong QMS.

A company can purchase sophisticated software and still experience poor quality.

If employees enter inaccurate information, the system will produce inaccurate information. If responsibilities are unclear, a digital notification will not automatically solve the problem. If management does not respond to quality warnings, a sophisticated dashboard becomes little more than a reporting tool.

This is why Digital QMS must be supported by good processes, competent people, clear responsibilities and strong leadership.

This direction is consistent with the broader development of ISO 9001:2026, which places greater emphasis on leadership, organizational culture, strategic alignment, risks and opportunities, while retaining core principles such as customer focus, the process approach and continual improvement.

Technology can support these principles, but it cannot replace them.

What does a mature Digital QMS look like?

Digital QMS maturity can be understood as a gradual journey.

An organization may begin with paper-based records. It may then move to electronic documents, followed by connected digital processes. At a more advanced stage, the organization can use data analytics to identify trends and recurring problems. Eventually, data and technology can support early warnings and predictive decision-making.

This means digital maturity is not determined simply by the software an organization purchases.

A company with expensive software but poor data and weak processes may be less digitally mature than a company using relatively simple technology effectively.

The real measure is whether the system helps the organization understand its quality performance and make better decisions.

Where should companies start?

Organizations do not need to digitize every quality process at once.

A practical approach is to begin with processes that generate large amounts of information or have significant quality risks.

For construction companies, this could include inspection requests, material inspections, concrete testing, NCR management, audit findings, corrective actions and quality KPI monitoring.

Once these processes are working effectively, other processes can gradually be integrated.

The objective should be to create a system in which quality information can move smoothly from the field to the Quality Manager and ultimately to management decision-making.

The future of the QMS

The evolution of quality management can be viewed as a gradual movement.

First came the paper QMS, where the main challenge was controlling documents.

Then came the electronic QMS, where documents became digital.

Now organizations are moving toward the Digital QMS, where quality information is connected.

The next stage is the Intelligent QMS, where data is analyzed to identify trends and patterns.

Beyond that is Predictive Quality, where information can provide early warnings and support proactive intervention.

This does not mean that traditional quality tools will disappear. Inspection, testing, audits, NCRs, corrective actions and root-cause analysis will remain important.

The difference is that these tools can increasingly become connected through digital systems.

The ultimate goal of Digital QMS is therefore not simply to create a paperless organization.

It is to create an organization where quality information is reliable, accessible and useful for decision-making.

As quality management continues to evolve, three questions may become increasingly important:

What is happening?

Why is it happening?

What should we do next?

A Digital QMS cannot answer every question by itself.

But when good people, good processes, reliable data and appropriate technology work together, it can help an organization answer those questions much faster.

And that may be the real transformation of quality management: not simply moving from paper to digital, but moving from documentation to information, from information to insight, and from insight to better action.