The research published by Esin Benhür Aktürk (2026) in The Journal of Social Science (TJSS) examines the impact of AI-based decision-making processes on quality management and the strategic advantages this transformation provides to businesses.
The study reveals that artificial intelligence does not only increase operational efficiency; it also raises quality standards and creates sustainable competitive power.
According to the research, beyond accelerating decision-making processes, AI transforms quality management into a data-driven and predictive system.
COMPETITION NO LONGER COMES FROM PRODUCING BETTER, BUT FROM ESTABLISHING BETTER DECISION SYSTEMS.
KEY FINDING
Traditional quality management:
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control-oriented
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based on historical data
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reactive
The new model:
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data-driven
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predictive
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continuously learning
In other words, quality is no longer a result:
It is the output of a decision system.
CRITICAL MODEL
The structure presented by the research:
Artificial Intelligence → Decision Support Systems → Process Optimization → Quality Improvement → Performance
In this model:
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decision quality increases
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error rates decrease
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processes are optimized
WHAT ARE GLOBAL COMPANIES DOING AND HOW IS AI TRANSFORMING QUALITY?
According to the research, artificial intelligence:
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automates quality control processes
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analyzes customer feedback
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detects production errors early
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reduces risks through predictive maintenance
The Result:
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→ fewer errors
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→ higher customer satisfaction
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→ stronger operational efficiency
Siemens is a global exemplar that successfully implements AI-supported quality control systems in its production facilities.
By integrating computer vision systems and machine learning algorithms into its production lines, Siemens detects product defects in real-time and continuously improves the process.
Thanks to this system, costs have been lowered and quality problems caused by human error have been significantly reduced. The company has improved process performance by up to 20% through AI-based systems.
General Electric (GE) has also strengthened its decision-making systems through AI applications in its manufacturing processes.
Through its Predix platform, GE automates decisions related to maintenance, quality, and production by analyzing data from industrial equipment. By combining predictive maintenance and quality control, this approach has been effective in reducing production losses. With this system, GE has succeeded in reducing production downtime by 25%.
Bosch uses AI-based analysis tools to support quality management by classifying customer feedback and shaping product improvement decisions based on this data.
Particularly through Natural Language Processing (NLP) techniques, Bosch analyzes the customer experience more effectively and ensures feedback integration into product design processes. Consequently, decisions that directly contribute to customer satisfaction can be made.
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Toyota utilizes deep learning algorithms to pre-emptively detect errors on its production lines.
Image data collected via cameras are analyzed by trained AI models, allowing defective products to be rapidly identified and separated. Toyota’s system has revolutionized the quality control process by minimizing human-induced delays and overlooked errors. This application has also significantly lowered product return rates.
IBM Watson is utilized in the healthcare sector as a quality management and decision support system.
Particularly assisting doctors in cancer diagnosis and treatment planning, Watson analyzes medical literature to offer patient-specific decision recommendations. This application has contributed to reducing medical errors and increasing treatment quality. Furthermore, the contribution of Watson’s recommendations to clinical decisions is supported by high accuracy rates across numerous studies.
Amazon employs AI-based recommendation systems as decision support tools to improve customer experience and service quality.
By analyzing past purchase data, search history, and user behavior, the company provides personalized recommendations for every customer. This strategy not only increases customer satisfaction but also optimizes product inventory and logistics decisions.
Airbus uses a combination of augmented reality and artificial intelligence to enhance quality and reduce human error in aircraft manufacturing processes.
In complex assembly operations, AI systems lower the probability of technical personnel making errors and accelerate inspection processes. This application shortens aircraft production times while simultaneously strengthening quality assurance processes.
Unilever leverages AI-supported simulation and optimization algorithms to strengthen quality management in its supply chain processes.
Numerous variables, such as supplier performance, delivery times, and product quality, are analyzed simultaneously to select the most suitable suppliers. This system ensures the continuity of quality standards while reducing costs.
Alibaba has developed AI-supported customer service and quality analysis tools to minimize quality issues between manufacturers and consumers.
Thanks to NLP techniques used particularly in the analysis of voice feedback systems, customer complaints are instantly classified and directed to the relevant departments. Thus, the customer experience cycle is accelerated, and decision-making processes are conducted in a data-driven manner.
THE COMMON GROUND OF THESE COMPANIES: THEY DO NOT CONTROL QUALITY. THEY DESIGN QUALITY.
MAX ENERGY STRATEGIC ANALYSIS
The most powerful message of the research:
AI does not just improve quality; it redefines it.
Quality is no longer about:
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reducing human error.
It is about:
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preventing error before it even occurs.
STRATEGIC COMMENTARY
The biggest mistake companies make:
is viewing artificial intelligence merely as an automation tool.
However, the reality is:
this is a transformation of decision architecture.
The critical question every leader must ask themselves:
Does your company control quality, or does it design quality in advance?
This research directly impacts the following areas:
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quality management systems
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digital transformation strategies
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operational efficiency
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customer experience
MAX ENERGY COMMENTARY
This study validates a critical truth of the Max Energy system:
Quality:
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does not stem from the process;
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it is born from decision quality.
If decision quality drops:
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systems continue to operate,
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but results decline.
The winners in the age of AI:
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are not those who use more data,
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but those who establish superior decision systems.
Max Energy Leadership: