The Impact of Automation and AI on Public Procurement Performance in Tanzania

Influence of Artificial Intelligence-Driven Procurement Systems on Procurement Performance in Public Institutions in United States

Purpose: The aim of the study was to analyze the influence of artificial intelligence-driven procurement systems on procurement performance in public institutions Methodology: This study adopted a desk methodology. A desk study research design is commonly known as secondary data collection. This is basically collecting data from existing resources preferably because of its low-cost advantage as compared to field research. Our current study looked into already published studies and reports as the data was easily accessed through online journals and libraries. Findings: The findings indicate that Artificial Intelligence-driven procurement systems significantly enhance procurement performance in public institutions by improving efficiency, transparency, supplier selection, decision-making, and cost management through automation and data-driven processes. However, their successful implementation depends on organizational readiness, adequate digital infrastructure, skilled personnel, high-quality data, and supportive regulatory frameworks. The literature also reveals limited empirical evidence on the influence of AI-driven procurement systems in public institutions within developing countries, highlighting the need for further context-specific research. Unique Contribution to Theory, Practice and Policy: The technology–organization–environment (TOE) framework, resource-based view (RBV) & dynamic capabilities theory may be used to anchor future studies on analyze the influence of artificial intelligence-driven procurement systems on procurement performance in public institutions. Procurement professionals should receive continuous training in artificial intelligence, data analytics, and digital procurement to strengthen their ability to utilize AI technologies effectively. Governments should formulate comprehensive national policies and regulatory frameworks that guide the adoption, governance, and ethical application of Artificial Intelligence within public procurement.

HUMAN-AI COLLABORATION AND PROJECT MANAGEMENT PERFORMANCE IN KANO STATE, NIGERIA

The findings reveal that human - AI collaboration has a significant positive effect on project management performance, particularly in improving cost efficiency, schedule adherence, and quality delivery, indicating that organisations with strong digital infrastructure, skilled personnel, and supportive leadership derive greater benefits from AI integration.

A research framework for the evaluation of operational impacts of AI solutions in logistics use cases

A research framework for analysing the added value of generative AI in logistics organizations, with a focus on “difficult to automate” tasks and processes is presented.

Cybersecurity, and Artificial Intelligence

Artificial intelligence in public procurement: a multilevel dynamic capabilities perspective

This study explores how artificial intelligence (AI) adoption in public procurement emerges through dynamic capabilities across individual and organisational levels. A qualitative study was conducted with 20 professionals from 13 Finnish public organisations engaged in AI-related procurement initiatives. Using the Gioia methodology, interviews and supplementary documents were analysed to identify enablers, barriers and transformation mechanisms. The findings show that dynamic capabilities – sensing, seizing and transforming – are distributed across levels and shaped by bottom-up and top-down mechanisms. Individual experimentation feeds organisational learning, while leadership and governance structures enable or constrain scaling. Most organisations remain in the early phases, with transformation hampered by silos, vendor lock-in and weak orchestration. The study advances dynamic capabilities theory by demonstrating its multilevel nature in the public sector. It provides actionable insights into aligning individual initiatives with organisational structures to achieve sustainable, AI-enabled procurement transformation.

Identifying the Key Challenges in the Implementation of Artificial Intelligence in Procurement in Nigeria

The integration of Artificial Intelligence (AI) into global procurement practices promises unprecedented gains in efficiency, transparency, and cost reduction. However, developing economies like Nigeria face unique, often compounded, obstacles to realising this potential. This paper conducts a systematic review of recent literature to identify and categorise the key challenges hindering the successful implementation of AI in both public and private sector procurement in Nigeria. The review reveals that the barriers are multi-layered, spanning foundational, infrastructure deficits (e.g., unreliable power supply and inadequate broadband), data quality and governance issues (uncleaned, unstructured, and inaccessible data), and organisational and human factors (resistance to change, fear of job displacement, and a significant digital skills gap). Furthermore, the lack of a robust regulatory framework specific to AI deployment in public contracting introduces significant ethical and accountability risks, such as algorithmic bias. This study synthesises these challenges, providing a coherent framework for policymakers and organisational leaders to develop targeted intervention strategies, emphasising data foundational work, capacity building, and urgent infrastructure investment to unlock AI's transformative value in Nigerian procurement.