Ultimate Guide to Better Quality Management with AI & Automation
In today’s world, Total Quality Management is an indispensable concept that remains relevant as organizations take proactive quality approaches. As we move towards Industry 4.0, TQM plays an important role while adopting AI-based systems. Artificial intelligence (AI) is a powerful tool for businesses to analyze data, automate tasks, and make better decisions. Globally, AI is being used to drive improvements in the following areas: risk modeling and analytics, customer service automation, predictive analytics for equipment maintenance, marketing automation, and customer segmentation, to name a few.
The ROI for AI initiatives is calculated based on the impact on cost savings or revenue growth. For a successful AI integration with quality management, it is important to focus on these seven guidelines – understandability, flexibility, efficiency, software quality, quality assurance, durability, and software quality requirements.
However, quality leaders and business decision-makers continue to struggle with
- Integrating AI into day-to-day operational processes
- Understanding the impact of implementing AI-powered capabilities,
- Finding the right vendor to partner with for their AI-enabled automation needs
This ultimate guide will take you through the challenges in quality and the various facets of AI that will help businesses across industries.