Artificial intelligence (AI) and data are reshaping public administration, serving as core enablers of proactive public services (PPS), a next-generation model for human-centric governance. PPS can empower governments to automate eligibility checks, personalise delivery and anticipate citizens’ needs through timely interventions with minimal user engagement. While promising greater efficiency, inclusion and personalisation, PPS adoption and delivery remains fragmented due to conceptual ambiguity and limited empirical grounding. To address this gap, we conducted a systematic literature review synthesising current knowledge on PPS, identifying key motivators, benefits, levels of proactivity, transition strategies, barriers, and open issues. Our findings reveal unresolved tensions around automation, personalisation, consent, and ethical AI, underscoring the need for normative frameworks and methodological innovation. Positioned within the pragmatic research tradition, this study serves as a problem- and objective-centred entry point into the design science research process. It provides a structured foundation for theorising and designing actionable frameworks to guide the transformation of reactive systems into AI-enabled, personalised public services. By bringing together fragmented information systems knowledge with practical insights, this work supports evidence-based digital transformation and advances the responsible design and deployment of next-generation public services.