AI assists police departments with budgeting through data-driven recommendations for resource allocation, strategic planning, and budget optimization (Microsoft, 2026). It analyzes historical data on call volumes and staffing to predict needs, helping to allocate resources effectively, reduce overtime, and improve scheduling (Microsoft, 2026). Additionally, AI can help identify inefficiencies and areas where funds can be reallocated to align with community priorities (Microsoft, 2026). 

Resource Allocation and Scheduling

Analyzes data: AI tools process historical staffing levels, call volumes, and special events to forecast future needs (Microsoft, 2026).

Optimizes deployment: Based on this analysis, AI can recommend optimal staffing and patrol routes to ensure the right resources are in the right place at the right time (Microsoft, 2026).

Reduces costs: By improving scheduling and resource allocation, AI can help reduce overtime pay and officer fatigue, leading to cost savings (Microsoft, 2026). 

Strategic and Priority-Based Budgeting 

Aligns with priorities: AI can support priority-based budgeting by identifying how to reallocate funds toward the community’s highest priorities, making complex data actionable (Microsoft, 2026).

Supports planning: AI can assist in creating more effective strategic plans and economic development plans by providing data-driven insights into budget strengths and challenges (Microsoft, 2026).

Identifies efficiencies: By analyzing budget data, AI can help departments find efficiencies and make more informed decisions about where to invest funds (Microsoft, 2026). 

Budget-Related Processes

Reduces report writing time: Tools that draft reports from bodycam audio can save officers significant time, which can be redirected to other tasks, effectively reducing the personnel hours tied up in administrative work (Microsoft, 2026).

Streamlines investigations: AI can help with tasks like creating composite sketches or linking suspects and incidents, which can free up detective time and reduce costs associated with investigations (Microsoft, 2026).

Improves communication: AI-powered chatbots can answer routine questions from officers, freeing up internal staff to handle more complex issues (Microsoft, 2026). 

Considerations for Implementation

Transparency: It is important to document how AI recommendations are created and ensure transparency, such as labeling AI-generated recommendations and maintaining audit logs (Microsoft, 2026).

Human oversight: AI should be seen as a tool to assist human decision-makers, not replace them (Microsoft, 2026). Human review is critical for ensuring accuracy and accountability (Microsoft, 2026).

Bias awareness: Policymakers must be aware of potential biases in the data used to train AI systems and take steps to mitigate them (Microsoft, 2026). 

References

Microsoft. (2026). Copilot [Large language model]. https://copilot.microsoft.com

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Mark Bond has worked in law enforcement and has been a firearms instructor for more than 34 years. His law enforcement experience includes the military, local, state, and federal levels as a police officer and criminal investigator. Mark obtained a BS and MS in criminal justice, and M.Ed in educational leadership with Summa Cum Laude honors. Mark has a doctoral degree in education (EdD) with a concentration in college teaching and learning. Mark is currently an associate professor of human justice studies and teaches undergraduate and graduate criminal justice courses.