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Reengineering Quality for the AI Era
The lessons of Deming, Juran, Crosby, and Ishikawa are not obsolete—they are the foundation upon which the next generation of quality management must be built.
The quality pioneers of the twentieth century—Deming, Juran, Crosby, and Ishikawa—shared a common belief: quality could not be delegated. It had to be led from the top of the organization. Their principles transformed manufacturing and business performance worldwide and remain relevant today.
However, the business environment they helped shape has changed dramatically. Global supply chains, digital manufacturing, cloud computing, artificial intelligence, and advanced analytics are creating new opportunities and risks. While compliance, audits, and performance metrics remain important, they are no longer sufficient on their own.
Quality leaders now face a new challenge: how to reengineer quality systems for an increasingly intelligent and connected world.
The world has changed in many ways, and we must reimagine exactly what is important not only today but for the challenges ahead. One example: Harvard Business Impact indicates “the most urgent challenges facing senior leaders: transforming their operations and organizations to compete in the age of artificial intelligence (AI).” SAP cloud ERP business research finds that SAP Build and its AI-powered capabilities play a critical role in modernizing and accelerating their extensions and improving productivity. Don’t dream about it; Scientific American, the oldest American magazine, indicates that giants like Rigetti, IBM, and Google are investing $1.2 billion in quantum computing, which should supercharge AI. It also begs the question: “Will computers based upon quantum physics really change the world? Research quantum computing now and get ahead of your competition.” I recommend you read several recent articles in Quality concerning “Using AI and Inspection for Beginners.”
Artificial intelligence is rapidly becoming a strategic business tool. Organizations are using AI to predict equipment failures, identify process variations, automate inspections, analyze customer feedback, and improve decision-making. Rather than replacing quality professionals, AI has the potential to enhance their effectiveness by providing insights that were previously difficult or impossible to obtain.
This transformation requires a shift in thinking. Traditional quality systems were designed to detect problems after they occurred. Modern quality systems should focus on predicting and preventing problems before they impact customers. AI, machine learning, and advanced analytics can help organizations move from reactive quality management to predictive quality management.
Recommend research and define the impact of the following activities:
- Which AI chatbot to use (ChatGPT, Gemini, DeepSeek, Grok, Perplexity), and what is the cost?
- What quality activity is most important to explore with your AI chatbot?
- What computing companies are researching quantum computing and their potential success?
Quality leaders should begin by evaluating where AI can provide the greatest value. Potential applications include automated visual inspection, root cause analysis, supplier quality monitoring, document management, and corrective action effectiveness. Organizations should also explore emerging standards such as ISO/IEC 42001, which provides a framework for responsible AI management and governance.
The future of quality will belong to organizations that combine proven quality principles with modern technology. The lessons of Deming, Juran, Crosby, and Ishikawa are not obsolete—they are the foundation upon which the next generation of quality management must be built.
Reengineering quality is not about abandoning the past. It is about applying timeless quality principles to new technologies and new business realities. Organizations that begin this journey today will be better positioned to improve performance, enhance customer satisfaction, and remain competitive in the years ahead.
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