What it takes to implement AI in Africa: health-system lessons from developing an ML-enabled maternal risk stratification algorithm in Tanzania

Background Machine learning (ML) has growing potential to support early identification of high-risk pregnancies in resource-constrained settings. However, most studies focus on model development and predictive performance, with less attention to the health-system processes required to generate ML-ready data and translate risk information into clinical action. The Mlinde Mama Project in Tanzania combined Group Antenatal Care (G-ANC), digital maternal health systems, and development of an ML-enabled risk stratification model for hypertensive disorders of pregnancy (HDP). This study examined the health-system conditions shaping the pathway from routine care to actionable ML-enabled risk information. Methods We conducted a retrospective mixed-methods implementation analysis of this project that was implemented between 2022 and 2024 in Geita, Tanzania. The analysis triangulated endline evaluation findings, quantitative exit interviews with pregnant women, focus group discussions with women and healthcare workers, key informant interviews, project implementation records, routine data from Tanzania's Unified Community System (UCS), and documented ML development experience. Relevant quotations from the final evaluation report were systematically screened, selected, and coded. An abductive analysis combined inductively derived themes with the Non-adoption, Abandonment, Scale-up, Spread and Sustainability (NASSS) framework and a study-specific ML implementation pathway. Results Seven themes emerged across three phases of the implementation pathway. ML readiness began before the algorithm, with reliable clinical measurement and documentation; patient participation and task-sharing redistributed data generation. The paper-to-digital transition shaped which information became ML-ready data. The potential value of ML depended on workflow redesign rather than prediction alone. Technology created an efficiency paradox in which task-sharing reduced workload while staffing shortages and confirmatory work generated new burdens. Advanced analytics depended on basic infrastructure, while sustainability relied on teamwork, trust, user demand, and local ownership. Conclusions Our findings suggest that successful ML implementation in maternal health begins with health-system readiness before the algorithm itself. A critical paper-to-digital transition stage was a major determinant of data quality and ML readiness. ML-enabled maternal risk stratification should therefore be approached as a sociotechnical intervention spanning measurement, digitization, clinical confirmation, and follow-up. This implementation readiness is critical for the successful development of an ML-enabled risk stratification system. Crucially, our findings resonate with the key domains of the NASSS framework, underscoring how addressing multidimensional complexities, from technological design to organizational readiness, is vital for successful scale-up and long-term sustainability.