AI in Quality Guarantees Productivity, Efficiency, Top-line Growth, and Cost Benefits for Businesses
This study analyzes the factors driving and restraining the use of AI in quality management. It also highlights key user cases and profiles the companies impacting this space. The base year is 2023, and the forecast period is from 2024 to 2028.
The rapid advancement of AI has led to its use across sectors, particularly quality management, as is evident in the growth of predictive quality analytics and enterprise quality management systems (EQMS). With increasing competitive intensity, it has become essential to proactively avoid quality issues instead of relying on reactive approaches.
AI-driven predictive quality management tools can preempt quality issues early in the production process, ensuring waste reduction and enhancing overall product quality. Digital technologies such as machine learning (ML), natural language processing (NLP), and advanced analytics in EQMS solutions drive user adoption and result in informed business decisions, innovation, and heightened productivity.
While the unclear return on investment (RoI) and a lack of awareness about these technologies present challenges, vendors are now responding by highlighting the increasing number of practical use cases. However, the full potential of AI in quality management cannot be unlocked without access to clean, reliable data. Therefore, formulating a strong data strategy before embarking on AI projects will be imperative to success.
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Companies Mentioned (Partial List)
A selection of companies mentioned in this report includes, but is not limited to:
- ComplianceQuest
- IQVIA
- ETQ
- Honeywell (Sparta Systems)
