Considering the question, “What’s in it for us as quality professionals?” Maria could think of four salient benefits:
• Focus on strategic tasks: AI automates routine tasks, allowing professionals to concentrate on strategic initiatives, problem-solving, and process improvement.
• Enhanced accuracy: AI algorithms analyze data with greater precision, reducing human error and ensuring consistent quality standards.
• Improved efficiency: Automation streamlines quality processes, saving time and resources that can be reallocated to other areas, thus boosting productivity.
• Data-driven quality: AI provides data-rich insights for informed decision-making and better quality outcomes, doing so more efficiently and quickly than ever.
Maria was convinced that AI presented a significant opportunity to enhance her organization’s capabilities.
They needed time to digest the information and consider its implications for their organization.
Maria also reached out to some colleagues in her professional circle, asking whether they or their organizations were using AI for quality management in areas like predictive analytics, inspection, monitoring, process optimization, or data management.
Over the past month, Maria had delved deeper into AI herself, presenting her findings to her quality management team. They needed time to digest the information and consider its implications for their organization.
The responses varied, but they indicated a limited involvement with AI. One person said, “I don’t get into that level of detail with my clients. I work at the strategic or executive level. If the IT or operations folks use it, I wouldn’t know. It would be unreliable for process documentation, which needs to be data-driven and verified.”
Sitting on her terrace with a cup of coffee, Maria recalled how a group member had written and published a book over a weekend with ChatGPT as her “partner.” She even created a character, Dave, to interact with. Now that was interesting, and a capability that had never entered Maria’s mind. What if she could create a “virtual quality inspector” she could converse with and ask questions?
Maria was convinced that AI presented a significant opportunity to enhance her organization’s capabilities and achieve new levels of quality excellence. By embracing AI and developing the necessary skills, her department could become even more valuable to the company. Moreover, their involvement in operational excellence could help explore AI use in other areas, similarly to how they deployed their Six Sigma Black Belts across departments.
Undoubtedly, there would be more work to do along the way. Coffee break over—time to move forward. Maria had her work cut out before the next meeting on the subject.
منبع: https://www.qualitydigest.com/inside/innovation-article/enhancing-quality-management-ai-081424.html
Understandably, she still had concerns about integration with existing processes and technologies, cost, resistance to change, reliability, and maintenance. But as she recalled her productive discussions and research, she felt good about the positive implications. She and her team would continue exploring security and governance issues, and prepare a proposal for a pilot program to evaluate an AI system on a smaller scale before full deployment. Stakeholder engagement to ensure buy-in and alignment was also on her mind. With a smile and another sip of coffee, she knew the next steps were clear:
• Conduct a current state analysis at the organization.
• Assess reservations and concerns.
• Reach out to industry associations for examples of organizations currently using AI.
• Identify possible partners to help with integrating the technology.
During an informal presentation to her management team, Maria explained that AI is rapidly transforming quality management, offering powerful tools for quality professionals. She highlighted several elements of AI:
• Automated quality control: AI algorithms and machine vision can automate tasks like defect detection, improving efficiency and accuracy while reducing human error. For instance, AI-powered cameras can inspect pills on a production line with greater precision than human eyes.
• Predictive quality: AI can analyze vast amounts of data to predict potential quality issues before they occur, allowing for proactive maintenance and preventive measures that minimize downtime and ensure consistent quality.
• Risk management: By identifying patterns and trends in quality data, AI helps in risk assessment and mitigation. Analyzing historical data, AI can pinpoint areas prone to problems and prioritize quality control efforts.
• Improved decision-making: AI provides data-driven insights by analyzing complex datasets, uncovering hidden trends and patterns that inform better decision-making.
Another said, “I have one apprentice using AI to help her write the QMS for companies. She mainly uses it to improve her writing skills and manage her dyslexia.”
Thinking about the possible applications of AI in business, she felt astounded by the progress AI had made since her team’s initial exploration. AI was no longer a mere plaything but a serious business tool with profound implications for both business and everyday life. As a seasoned quality professional, she realized that with this progress came the need for careful consideration of risks, governance, and even regulatory aspects—topics familiar to her team. In addition to ChatGPT, there were other AI models, like Gemini and Claude, each with their unique strengths and nuances.
Maria took a moment to reflect on how much had changed in just over a year since she first explored ChatGPT with her informal innovation group. Back then it was more of a novelty, a fun challenge to see whether artificial intelligence could answer their mundane questions correctly. Sometimes it didn’t. But other times, it offered insights that made them ponder how AI might revolutionize quality engineering.
As she thought further about implications of AI for her employer, three salient points stood out in her mind:
• Training and upskilling: Integrating AI into their systems means quality professionals need to develop new skills to work effectively with AI tools and interpret AI-generated data.
• Data quality: The effectiveness of AI relies heavily on the quality of data, requiring vigilance in ensuring data accuracy, integrity, and security.
• Ethical considerations: Potential biases in AI algorithms must be considered, ensuring AI-driven quality processes are ethical and responsible.