Artificial intelligence and allied digital technologies are entering professional and educational practice in low- and middle-income settings faster than the evidence base describing that entry has matured. This review synthesises thirteen studies published in the Journal of Contemporary Academic Research and Methodologies (JCARM), spanning accounting education, public procurement auditing, organisational leadership, language pedagogy, primary classroom instruction, robotics control, urban infrastructure management, computer vision, electoral systems, and time series forecasting, alongside relevant international literature. Studies are grouped under three themes: adoption within professional and organisational practice, adoption within teaching and learning, and the structural conditions conditioning both. A consistent pattern emerges across otherwise unrelated domains. Where research evaluates a technical artefact in isolation, it reports objective performance metrics; where research evaluates the same class of technology embedded in human institutions, it overwhelmingly measures perception, attitude, and self-reported readiness rather than realised outcomes. This asymmetry is argued to be a structural property of an early-stage field rather than a defect of individual studies. A second cross-domain finding is that the constraints reported , unreliable electricity, weak connectivity, absent local hardware supply, and inadequate professional preparation , recur with striking uniformity irrespective of discipline, suggesting that infrastructure operates as an unmeasured moderator across the entire corpus. The review proposes a staged research agenda moving from perception measurement toward outcome evaluation, and argues that the field's most useful near-term contribution is to treat infrastructural capacity as a variable rather than a limitation.