In Malaysia's public construction sector, cost management relies predominantly on traditional evaluation methods, resulting in inefficiencies, pricing inconsistencies, and a lack of transparency. To address these issues, digital tools, such as J-Selaras, have been implemented to standardise contract rates using statistical algorithms. This study examines the variability in contractor pricing across three construction work categories (staircase, external floor finish, painting, and decorating) by employing statistical diagnostics to support Malaysia's digital transformation agenda for public procurement. Utilising procurement data from the Public Works Department of Malaysia (JKR), this study adopts a quantitative research methodology. The analysis employed Z-score standardisation, coefficient of variation (CV), and descriptive statistics to compare the tender rates from various sources. The data were organised into a matrix format, facilitating integration with digital systems, such as J-Selaras. The findings indicate significant pricing variability in External Floor Finishes (CV: 104.14%) and moderate variability in staircases (CV: 55.94%), whereas paintings and decorations exhibit consistent pricing (CV: 5.71%). These results underscore the potential for effective implementation of AI and digital procurement tools to enhance rate rationalisation and evaluation transparency. This research validates the effectiveness of using Z-score analysis to standardise procurement data and supports the integration of statistical tools into automated systems. It proposes a multi-tiered digital transformation strategy that encompasses real-time anomaly detection, AI-assisted evaluation, and cloud-based procurement platforms to modernise Malaysia's cost management practices and enhance the efficiency of the public sector.