This pilot study investigates the adoption trends and challenges of artificial intelligence (AI) and machine learning (ML) in the telecommunications sector, focusing on network analytics and security. Using a two-part design—literature review and survey-based statistical analysis—the study provides early insights into industry perceptions and readiness. With data from 31 telecom professionals in a leading Philippine service provider, results reveal a low level of current implementation but high expectations for future adoption. Despite the small sample size, which limits generalizability, this study contributes a foundational analysis of key drivers and obstacles of AI/ML adoption. It highlights the importance of workforce upskilling, integration strategies, and stakeholder engagement in supporting AI readiness within telecom organizations.

With the increasing popularity of Raspberry Pi (RPi) comes the increase in available use cases and projects that utilize this single-board computer. One of these uses cases is the RPi Cluster, where multiple nodes are connected to a local network to form one logical resource pool. Implementing this build, however, can be a tedious and needs manual intervention such that each node needs to be configured to the cluster, one-by-one. While easily implementable if the scale is relatively small, the task becomes complex if several nodes need to be configured all at once. To save time, as well as to automate the whole process, developers and hobbyists would use Docker Swarm as an alternative. This paper explores the possibility of going beyond the popular Docker Swarm by proposing the use of OpenStack, a Cloud Computing Platform, as an alternative. Document analysis and review of existing research were used to prove the build’s technical feasibility. After comparing five relevant use cases, it can be concluded that deploying OpenStack to the RPi Cluster, or OpenStack-on-RPi, is feasible, though further research and testing that will optimize this use case is recommended.