QA/QC: The Trends for 2025
2025: The 5 Top Trends in Quality Assurance
What will shape quality assurance (QA/QC) in 2025? How will trends like sustainability and hyperautomation influence the way QA teams work? And will the development continue toward cloud-based solutions? The answers to these questions reveal how companies can increase efficiency, conserve resources, and set new benchmarks in quality assurance.
The year 2025 will bring fundamental changes to quality assurance (QA/QC). New technologies, rising demands for efficiency and sustainability, as well as increasingly complex production processes, make it clear that isolated systems, manual testing procedures, and a lack of automation are reaching their limits. Companies must realign their quality assurance efforts: shifting away from mere error detection and becoming an integrated part of the value chain—lowering costs, improving processes, and driving innovation.
Germany, as a leading hub of innovation, provides ideal conditions for this transformation. With the High-Tech Strategy 2025, policymakers are promoting research and innovation to deliver new impulses for business and science. It becomes evident: Research, development, and quality assurance must become more closely intertwined to maintain competitiveness while also addressing global goals like sustainability and resource conservation.
Companies must realign their quality assurance efforts: shifting away from mere error detection and becoming an integrated part of the value chain—lowering costs, improving processes, and driving innovation.
Quality assurance holds enormous potential to optimize processes and improve resource efficiency. Data-driven processes and modern technologies not only enable more precise results but also help to use resources more efficiently, minimize waste, and extend product lifespans. In this way, quality assurance becomes a driver of sustainability and innovation.
In this article, we present the five key trends that will shape quality assurance in 2025. From hyperautomation to data-based analytics, these developments show how companies can optimize processes, work more sustainably, and meet the continuously increasing quality demands of the future.
Trend #1: Sustainability
In 2025, sustainability will take center stage in quality assurance like never before. More and more companies are required to reduce their environmental footprint and comply with stricter regulations. A prime example is DIN SPEC 91446, which defines clear requirements for data quality in plastic recycling. Companies must ensure that material data is thoroughly documented.
However, QA/QC will extend beyond simple material testing: it becomes a key lever to improve resource efficiency and extend product lifespans—from material selection to reintegration into the material cycle.
A concrete application of this is the increased use of recycled polymers. Quality assurance teams test these materials not only for mechanical and chemical properties but also evaluate their durability and compatibility with existing production processes. QA/QC ensures that recycled materials achieve the same performance as primary materials while significantly reducing the environmental impact.
Reducing waste and scrap is another focus area in quality assurance. Precise testing methods, such as non-destructive testing with modern sensor technology, enable errors to be detected and corrected early, preventing material losses. This allows production processes to be made more resource-efficient, helping companies meet sustainability targets while also reducing costs. Part of that is quality assurance’s efforts to extend the lifespan of products. With growing demands for sustainability and resource conservation, QA teams are actively developing and validating standards for product longevity. Comprehensive tests—from corrosion checks to load simulations—ensure that products are not only reliable and safe but can also be used sustainably throughout their entire lifecycle.
Sustainability in quality assurance is not just a corporate responsibility—it is a genuine opportunity. Companies that make their processes environmentally friendly improve resource utilization, enhance product quality, and achieve clear competitive advantages—through lower production costs, more efficient operations, and a stronger market position.
Trend #2: Cloud Refactoring
By 2025, migrating applications and processes to the cloud—known as cloud refactoring—will become the new norm for companies. This shift offers not only greater flexibility and scalability but also fundamentally transforms the role of quality assurance.
Moving QA processes to the cloud unlocks new possibilities: automated testing environments identify and resolve errors faster, while cloud-based platforms allow tests to be run in parallel. This significantly improves testing efficiency and shortens time-to-market.
Another advantage of cloud technology is improved collaboration between teams across different locations or departments. Access to shared testing resources and real-time data—regardless of location—ensures greater consistency and quality in QA processes. At the same time, transitioning to the cloud brings new challenges. Security aspects, data integrity, and compliance must be carefully reviewed and adapted to meet the requirements of cloud environments.
Companies should therefore invest in targeted training and specialized tools to prepare their QA teams for these new demands. Overall, cloud refactoring enables quality assurance to become more agile and efficient. Companies that embrace this trend early can strategically optimize their QA processes and gain a competitive edge.
Trend #3: Automation and Cost Efficiency
In 2025, automation in quality assurance will evolve from a tool for productivity gains to a cornerstone of cost reduction and resource optimization. For QA teams, this means transitioning from reactive to proactive processes, where intelligent technologies take over routine tasks and support decision-making.
One clear example is the use of AI-powered systems for monitoring and analyzing production parameters in real time. Automated testing procedures can detect potential errors or deviations early in production phases. This not only reduces scrap but also prevents costly rework. In this way, automation directly lowers costs without compromising quality.
Another benefit lies in resource optimization. Automated testing systems can run processes in parallel, significantly reducing time and workload. Combining robotics and machine learning connects physical and cognitive tasks, massively easing the burden on QA teams. This creates space to focus on complex analyses and strategic, high-value tasks. However, automation also brings challenges—ensuring data security and integrating new technologies into existing processes. But companies that address these hurdles early improve cost control and secure decisive long-term competitive advantages.
Automation, therefore, redefines quality assurance. In a world where cost pressures and quality requirements continue to rise, it becomes a key driver for ensuring future business success.
Trend #4: Data-Driven Quality Assurance
Quality assurance in the industry is now more data-driven than ever—and this trend will continue into 2025. Vast amounts of data from testing procedures, production processes, and external sources offer new opportunities to monitor product quality in real time and make informed decisions. The key lies in not just collecting data but using it strategically to make processes more precise and efficient.
For example, companies combine data from various sources—measuring devices, production lines, and laboratories—to generate meaningful insights. These connections allow for a better understanding of how production parameters influence product quality and enable targeted process improvements. However, to ensure smooth data flow between systems, standardizing data formats is essential. Harmonized structures provide a reliable foundation for analyses and ensure that results are transparent and reproducible.
Data-driven quality assurance not only improves the efficiency of testing processes but also enhances traceability in decision-making. Companies that effectively use their data resolve quality issues faster, optimize resource usage, and strengthen customer satisfaction in the long run.
Trend #5: Predictive Quality Control
Data-driven approaches lay the foundation for efficient processes—Predictive Quality Control, however, takes it a step further. By leveraging machine learning and artificial intelligence, future quality problems can be predicted before they occur. This forward-looking approach enables companies to act proactively, avoiding errors and costly production stoppages or recalls.
A concrete example highlights the potential of Predictive Quality Control: In production lines, sensor technologies continuously capture data such as temperature, pressure, or material stress, comparing it in real time with historical patterns. If deviations indicating potential defects are detected, the system triggers automatic alerts and enables targeted corrective actions. This ensures that defective products never reach the next production phase.
Predictive Quality Control also enhances efficiency: while intelligent systems handle routine monitoring tasks, production and quality teams can focus on strategic priorities. This not only saves time and costs but also ensures consistently high product quality. This approach transforms quality assurance from a reactive process into a proactive system that prevents errors and drives continuous improvements. Companies that implement Predictive Quality Control early will gain a clear advantage in an increasingly data-driven industry.
Conclusions - the Five QA/QC Trends in 2025
The five presented trends — from sustainable processes and automation to data-driven and predictive quality assurance — illustrate the profound transformation that quality assurance will undergo in 2025. At the heart of this transformation is a key concept: Material Intelligence.
Material Intelligence connects data, technologies, and processes intelligently to increase efficiency, prevent errors, and secure long-term product quality. QA teams can make data-based decisions, detect quality issues early, and drive continuous improvements. This approach is especially critical at a time when companies must optimize costs while meeting growing demands for sustainability and transparency.
Through Material Intelligence, quality assurance evolves from a reactive to a proactive process. Intelligent systems monitor production processes, prevent manufacturing defects, and ensure compliance with standards. Quality is not just maintained but actively enhanced—ranging from optimized production processes to resource savings and innovative product designs.
Material Intelligence is no longer a future promise—it is already a reality. Companies that tap into this potential early will build the foundation for efficiency, competitiveness, and sustainable success—today and in the future.
For more information on LabVs Material Intelligence Platform (MIP), please check out this page.
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