https://www.carijournals.org/journals/index.php/JTS <p>JTS is an open access journal that publishes research on technology and systems in various domains. It is hosted by CARI Journals, a global platform for academic excellence and knowledge dissemination. The journal has an ISSN, a DOI, and is indexed in several databases. It publishes monthly and provides certificates and prints to the authors. Publishing in CARI Journals is fast, efficient, and quality-assured.</p> CARI Journals Limited en-US Journal of Technology and Systems 2788-6344 <p>Authors retain copyright and grant the journal right of first publication with the work simultaneously licensed under a <a href="https://creativecommons.org/licenses/by/4.0/">Creative Commons Attribution (CC-BY) 4.0 License</a> that allows others to share the work with an acknowledgment of the work’s authorship and initial publication in this journal.</p> https://www.carijournals.org/journals/index.php/JTS/article/view/1824 <p><strong>Purpose: </strong>This study addresses the crucial challenge of managing risks associated with data and models in Agile Artificial Intelligence (AI) and Machine Learning (ML) projects. It aims to develop a systematic framework for effective risk control utilizing agile methodologies.</p> <p><strong>Methodology: </strong>The research is grounded in an interpretivist approach and utilizes a deductive method. It constructs a comprehensive framework for identifying and mitigating risks, integrating risk management seamlessly into Agile processes for AI and ML development.</p> <p><strong>Findings: </strong>The study introduces four technological themes critical for risk mitigation: dynamic distribution of resources, model robustness, risk integration, and quality assessment of information. These themes provide actionable strategies for reducing risks throughout the Agile AI/ML development lifecycle, ensuring that risk assessment and mitigation are integral to project planning and execution.</p> <p><strong>Unique contribution to theory, practice, and policy: </strong>The study contributes to both theory and practice by offering a detailed, actionable framework for risk management in Agile AI/ML projects. It advocates for the adoption of adaptive technologies and tools, continuous stakeholder engagement, and adherence to ethical standards. Recommendations include validation of the framework through empirical research and ongoing longitudinal evaluations to adapt and refine risk management practices. This approach aims to enhance the reliability and efficiency of project outputs in dynamic environments, providing a significant foundation for policy development in technology project management.</p> Ankur Tak Sunil Chahal Sunil Chahal Copyright (c) 2024 Ankur Tak, Sunil Chahal https://creativecommons.org/licenses/by/4.0 2024-04-24 2024-04-24 6 3 1 18 10.47941/jts.1824