This research explores how New Zealand farmers use the information from Precision Livestock Farming (PLF) systems to improve decision-making on farming operations. Farmers are being urged to improve operational efficiency under pressure to increase the food supply for an increasing population. By having PLF systems alerting real-time abnormality of individual animals, farmers can proactively solve livestock-related operational problems (Halachmi, 2015). Six case studies of New Zealand farmers were conducted, along with insights from a PLF system provider, an information analyst, and a business analyst. Qualitative data were collected in semi-structured interviews and were analysed with secondary data collected from public websites. Findings show how the information from PLF systems encourage farmers to reflect on assumptions and provide tools to timely test and improve on solutions for different types of decisions (structured, semi-structured, unstructured). Detailed decision-making process flow models present the steps farmers take to receive, interpret, and act on the information before and after they adopt PLF systems. This study fills the research gap concerning post-implementation use of PLF systems with empirical case studies. It proposes how PLF systems encourage the development of double-loop learnings for decision-makers that challenge their original mental models and create innovative solutions. Practically, this study discusses barriers to realise the full benefits of information from PLF systems and recommends procedures developed to reduce the impacts of these barriers.