Enhancing Safety and Quality Standards in Project Management: Modern Strategies and Approaches

Artificial intelligence is entering day-to-day project work, yet gains in safety and quality remain uneven. This thesis asks where AI truly helps and under what conditions. A targeted literature review plus a survey of project managers (N = 21) synthesize evidence across construction, manufacturing, IT and energy. Four recurring levers emerge: early-warning models for schedule risk; computer vision for PPE breaches and unsafe conditions; predictive QA to prioritize inspections; NLP assistants that reduce requirements ambiguity and improve traceability. Outcomes depend more on clean data, lightweight integration with existing tools and human-in-the-loop oversight than on complex models. The thesis delivers an eight-step PMO checklist and governance practices to scale responsibly. Limitations include a small sample skewed toward large enterprises and cross-sector heterogeneity; findings are directional but actionable.