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Purpose Law enforcement agencies in the United States are relying on state fusion centers for intelligence to develop actionable, data-driven reports that increase efficiency and improve investigations in crime prevention and homeland security. This study assesses the extent to which artificial intelligence and machine learning (AI/ML) are increasingly shaping intelligence operations in law enforcement and the functions of state fusion centers in supporting intelligence-led policing (ILP). The reliance and integration of AI/ML is improving analytic accuracy, situational awareness and information and data sharing and collaboration among law enforcement and homeland security agencies. This study examines the state of contemporary academic literature, assesses AI/ML applications used in law enforcement and builds a conceptual and theoretical framework centered on ILP policing. It also relies on empirical data, case study applications, and DHS assessments to explore the degree to which AI-driven processes and analytics enhance criminal intelligence, investigative efficiencies, situational awareness and predictive policing. The analysis, while focusing on the opportunities and challenges of using AI/ML tools in law enforcement, also highlights the need for ethical governance, transparency and accountability when relying on advanced technologies for crime prevention and policing. Design/methodology/approach This study utilizes qualitative methods, including a thematic content analysis of government and think tank/practitioner reports as well as academic literature on the benefits, costs and ethical factors regarding variations in the implementation of AI/ML tools for law enforcement intelligence products and resource allocation. For cross-validation of operational outcomes, it examines publicly available information in the DHS Fusion Center Annual Assessment, Bureau of Justice Statistics, LEMAS and RAND Corporation assessments of intelligence-led policing. Findings Qualitative Results Federal and state sources report fusion centers and law enforcement agencies integrating advanced analytic and ML-enabled tools into each step in the criminal intelligence lifecycle process. However, ethical and structural challenges limit and constrain technology-driven narratives in fusion centers. Given these challenges, there is a consistent qualitative and thematic pattern: state fusion centers now function as criminal intelligence analytic hubs or resources that leverage the most contemporary analytic and data-driven tools for criminal intelligence and law enforcement investigations. Interrelated themes describe AI/ML technologies in terms of shaping, constraining and complicating the intelligence lifecycle in fusion centers and law enforcement operations. Seven specific themes emerged from latent coding are illustrated in the chart. Research limitations/implications There are limitations on the collection of quantitative data since DHS, leading think tanks and NGOs do not disclose specific figures on the proportion of AI/ML tools. The DHS Fusion Center Annual Assessment process monitors technology adoption and the growth of analytic capabilities throughout the national network of fusion centers; however, the specific quantitative statistics are not disclosed in public summaries (DHS, 2024). Second, publicly available data and information constitute the bulk of empirical sources. Consequently, this study relies primarily on qualitative narrative reporting, not quantitative performance metrics. Third, publication bias is likely present in industry and government sources as these reports may provide overly optimistic observations and conclusions while overlooking ethical dilemmas, failures and challenges. Moreover, qualitative thematic analysis could reflect broader structural narratives as opposed to empirical outcomes. Finally, since AI/ML adoption varies across fusion centers and according to technology levels, qualitative themes identified in this study must be read as representative patterns and not as universally generalizable. Originality/value Fusion center utilization of AI/ML technologies is as much an operational tool as it is a policy, governance and ethical challenge. Successful and professional use in support of law enforcement is about placing technological innovations firmly within institutional accountability and constitutional guardrails. On the one hand, AI/ML tools are enhancing analytical intelligence production by accelerating analytic workflows, predictive modeling and expanding data/information integration capabilities. AI/ML are extending ILP concepts by offering improvements in situational awareness and threat identification and operational efficiencies. On the other, substantial constraints hinder responsible use of these technologies. In the absence of standardized oversight frameworks, data-quality issues, algorithmic bias and the lack of professional development, workforce capacity and critical skills on the part of fusion center analysts will cancel the benefits of these tools.