Quality control has been built on the same foundations for decades: trained inspectors, statistical process control charts, sampling plans and audit checklists. These methods were designed for a world where defects could only be detected after they occurred, inspection was inherently manual and the gap between a quality event and a corrective action was measured in hours or days. That world is being dismantled.

Computer vision now catches defects that human eyes miss, at line speed and without fatigue. Digital twins flag quality drift before a single out-of-spec part is produced. IoT networks feed machine learning models that predict failures before they happen. And generative AI is beginning to do something even more disruptive: synthesizing training data, automating compliance reporting and putting quality expertise in the hands of every operator on the floor.
The transformation is real, but so are the risks. Model bias, data dependency, eroded human oversight and accountability gaps when an algorithm makes a wrong call are live challenges that companies are navigating right now, often without a clear framework to guide them.

This thesis builds that framework. Through literature review and case study analysis, the research maps the technologies reshaping quality management across both manufacturing and service settings (B2B environments driven by traceability and compliance, and B2C contexts where customer experience and scalability dominate) assessing their benefits, their failure modes and the strategic implications for companies integrating these tools for real.

If you want to work at the frontier where artificial intelligence meets one of the oldest disciplines in operations management, this is the thesis for you.