For law enforcement agencies, the conversation around artificial intelligence has shifted. The question is no longer whether AI belongs in policing, but how it can be applied in ways that improve operations without compromising accuracy, accountability or public trust. As staffing shortages persist and the volume of investigative data continues to grow, agencies are looking for practical ways to reduce administrative burdens while maintaining officer oversight and control.
One of the most promising ways to implement AI is workflow assistance. Rather than replacing officer judgment, purpose-built AI tools can help personnel organize information, draft more complete reports and quickly identify investigative leads hidden within large volumes of case data. The goal isn’t to automate policing — it’s to help officers and investigators spend less time sorting through information and more time acting on it.
That need has become increasingly apparent as the volume of information generated during even routine incidents continues to grow. Officers may have to review CAD notes, body-worn camera transcripts, witness statements and supplemental reports before completing a single report, while investigators often inherit case files containing hundreds of pages of records. Managing that information takes time, delays investigations and adds to the administrative workload. While AI won’t solve staffing shortages, it can help agencies manage information more efficiently and reduce some of the repetitive work that slows officers and investigators down.
Responsible AI keeps people in control
Concerns about AI in policing often center on whether the technology will replace human judgment or begin making consequential decisions without adequate oversight. That concern is especially important in public safety, where officers exercise discretion, investigators assess credibility and supervisors are accountable for the quality of agency records. Use of AI in public safety should not remove that responsibility — it should make it easier for personnel to carry it out.
Many public safety technology providers are now developing AI specifically for law enforcement rather than adapting consumer AI tools to police work. Among them is Mark43, a cloud-native public safety platform that includes CAD, RMS and AI capabilities designed to help agencies streamline report writing and investigations while keeping officers and investigators in control of the final product. Rather than treating AI as a standalone tool, the company has focused on embedding it within the workflows agencies already use, with an emphasis on transparency, security and human oversight.
Mark43’s approach to responsible AI rests on four principles that can also serve as a framework for agencies evaluating any AI solution for law enforcement:
• Human-first by design: AI should assist officers and investigators, not replace their judgment. Personnel remain responsible for reviewing, editing and approving reports, investigative summaries and other outputs before they become part of the official record.
• Built-in into existing workflows: AI is most effective when it’s built directly into the CAD and RMS officers already use. Working within existing public safety workflows allows AI to leverage authoritative agency data while maintaining established permissions, audit trails and review processes.
• Transparency and validation: AI-generated content should never be a “black box.” Officers and investigators need to understand where information comes from, verify it against source records and identify missing information or inconsistencies before reports are finalized. AI helps users identify gaps in information rather than making assumptions or generating its own data.
• Security, privacy, compliance and ethics: Because law enforcement data is highly sensitive, AI tools should operate within secure, CJIS-aligned environments and support agency policies governing data access, retention and accountability. Responsible AI solutions do not use customer data to train AI models, and enforce appropriate agency use through system guardrails to strengthen public trust.
Taken together, these principles reflect an important reality: In policing, the success of AI isn’t measured simply by how quickly it produces information, but by whether that information is accurate, defensible and supports the people ultimately responsible for every decision.
Why workflow integration matters
Agencies should also consider where an AI tool operates. A general-purpose application may be easy to access, but moving sensitive law enforcement information into a disconnected platform can create questions about data retention, access controls, privacy and compliance. It may also force officers to copy information between systems, creating another opportunity for mistakes.
AI that is built directly into the CAD and records systems an agency already uses takes a different approach. Because the technology works with authoritative agency data, it can use the same incident information, permissions and audit controls already associated with those workflows. Personnel do not need to transfer sensitive records into a separate tool, and administrators do not have to create an entirely new process for governing access.
Native integration also makes the technology more useful. An AI assistant working inside an RMS can draw from the CAD event, connected transcripts, officer notes and existing report content rather than relying on a user to manually enter every detail. It can also fit into established review and approval processes instead of requiring officers to learn a separate application.
Supporting stronger police reports
Report writing is one of the clearest examples of where AI can reduce administrative work without taking decision-making away from officers. The goal is not to have software independently author an official police report. The goal is to give the officer a well-organized draft based on information the agency already possesses, then help identify anything that may need clarification before submission.
Mark43’s ReportAI uses incident context such as CAD information, body-worn camera transcripts, event details and officer notes to generate a structured first draft. Instead of beginning with a blank narrative, the officer can review the proposed report, compare it with personal observations and make any necessary additions or corrections. The officer remains responsible for the accuracy and completeness of the final report.
The validation component may be just as valuable as the initial draft. ReportAI can review documentation for missing information, completeness, tone and alignment with policy requirements before the report moves forward. That does not guarantee a perfect report, but it gives the officer another opportunity to catch an omission before a supervisor finds it or the case reaches a prosecutor.
For patrol officers, this can reduce the time spent reconstructing an event from multiple sources. For supervisors, it can lead to fewer avoidable corrections and greater consistency across reports. The meaningful benefit is not simply that a report may be completed faster. A rushed or inaccurate report is no improvement. The value lies in helping the officer produce a complete, defensible report with less repetitive effort.
Turning case files into usable intelligence
Investigators face a related challenge on a larger scale. A complex case may include numerous narratives, supplements, interviews, transcripts and evidence records. Before deciding what to do next, a detective must understand what has already happened, who is involved, what information has been confirmed and where the gaps remain.
Mark43’s BriefAI is designed to synthesize that material into a more manageable case overview. It can highlight key events, involved parties, outstanding tasks and possible investigative gaps, giving detectives and supervisors a faster way to understand the scope and status of a case.
That summary is not intended to replace review of the case file, particularly when an investigator is preparing for an interview, warrant application or court proceeding. Instead, it helps the investigator get oriented more quickly. A detective receiving a newly assigned case can easily identify which records require closer examination, while a supervisor can assess whether important follow-up work remains overdue.
BriefAI also provides citations that connect its observations to the underlying source records, a critical component of transparency and defensibility. An investigator should not have to trust a summary without knowing where the information came from. Source-linked citations make it possible to verify the statement, read it in context and determine whether the system’s characterization is accurate.
Security and governance must come first
Any agency considering AI should establish clear rules before deployment. Those rules should identify which tools personnel may use, what types of information may be processed and who can access AI features. Agencies should also determine how AI activity will be logged and audited, how drafts will be retained and how errors or questionable outputs will be reported.
Security should also be part of the discussion from the beginning. Law enforcement systems contain criminal justice information, victim data, personally identifiable information and sensitive investigative records. Agencies should avoid placing that information into unapproved consumer AI platforms that do not meet departmental security and governance requirements.
Mark43’s ReportAI and BriefAI operate within a CJIS-aligned environment and are designed to maintain agency oversight, controlled data handling and auditability. Because the solutions are embedded within the existing platform, they can operate under established access permissions rather than relying on disconnected third-party tools to manage sensitive information.
A measured path to adoption
Agencies do not have to adopt every AI capability at once. For many, a gradual approach may be the most responsible path. A department could begin with report validation or case summarization, evaluate the results and gather feedback from officers and supervisors before expanding into AI-assisted drafting. This gives personnel time to understand what the technology does well, where it may fall short and how human review should be incorporated into daily practice.
Agencies should also measure more than just speed. Time savings are important, but they should be considered alongside report quality, correction rates, investigative progress, user confidence and compliance with policy. A tool that saves a few minutes but creates uncertainty about accuracy is not delivering meaningful operational value.
The most useful AI applications in law enforcement will be the ones that fit into existing workflows and systems, like Mark43. They will help an officer prepare a stronger report, show a supervisor where information is missing or give an investigator a clearer view of a complicated case. Used that way, AI is not a replacement for police personnel or a futuristic experiment. It is a practical tool for managing information, reducing repetitive administrative work and helping agencies make better use of the people they already have.
Visit Mark43 for more information.
