Editor’s note: This article is part of the Police1 Leadership Institute, which examines the leadership, policy and operational challenges shaping modern policing. In 2026, the series focuses on artificial intelligence technology and its impact on law enforcement decision-making, risk management and organizational change.
Artificial intelligence (AI) has rapidly transitioned from a futuristic concept to a daily operational reality in policing. For police chiefs and command staff, acquiring new tools is often the easiest step. The true challenge — and the common point of failure — lies in integration: aligning people, policy, training and governance so technology improves operations without increasing risk.
In many agencies, AI initiatives succeed or fail less because of the software itself and more because of culture, process and accountability. Agencies that treat AI like a routine “software update” often end up with expensive tools that are underused or misused in ways that create legal, ethical and reputational exposure. This article outlines a practical framework for internal readiness, focusing on cultural adaptation, tiered workforce upskilling and governance structures that reduce risk and increase operational value.
The cultural pivot: Redefining the mission
Resistance to AI often stems from a fundamental fear among the rank and file: replacement. If officers believe technology is being introduced to marginalize their role, adoption will stall. Command staff must shape the narrative before a single piece of software is deployed.
Consider a common scenario: An agency rolls out a new AI-enabled report-writing tool without clear guidance. Officers assume it is meant to reduce staffing or scrutinize productivity. The tool becomes a rumor magnet, usage is inconsistent and supervisors lose confidence in the output — turning a promising efficiency gain into a morale issue.
Communicating the vision
The message from leadership must be clear and consistent: AI is a force multiplier, not a replacement for sworn personnel. The goal is to automate repetitive work so officers can focus on human judgment, problem-solving and service.
Modern policing involves significant administrative work, including transcribing body-worn camera footage, manual data entry, triaging tips and combing through hours of video evidence. These tasks are well suited for automation. By offloading repetitive tasks to AI, officers can focus on what algorithms cannot replicate: victim support, critical decision-making, interviews and community engagement.
When officers understand that AI is designed to reduce burnout, not headcount, resistance can shift into operational curiosity.
Upskilling the workforce: Data literacy as a core competency
Once the culture is receptive, the workforce must be capable. As agencies adopt data-driven tools, data literacy becomes a core competency for modern policing. This does not mean every officer must become a data scientist. It does mean personnel should understand what data AI systems rely on, what outputs they produce, where their limitations lie and how AI use must be documented for accountability.
A one-size-fits-all approach to AI training will fail. As agencies adopt AI-enabled tools, training cannot be generic because the risks, responsibilities and decision-making authority attached to AI vary significantly by role. Effective agencies align training to responsibility rather than title, creating clear expectations for executives, specialists and frontline personnel and reducing the likelihood of misuse.
Command staff: Strategy, oversight and liability
For executives, AI training is not about learning how the software works but about understanding where organizational risk lives. Chiefs and command staff must be able to evaluate systems that produce outputs without transparent reasoning, recognize how bias and data gaps can shape results and anticipate privacy, civil rights and due process implications. Most importantly, they must understand where legal exposure arises when AI informs decisions that affect liberty, charging or enforcement activity. Without this foundation, leaders cannot set meaningful policy, approve appropriate use cases or defend the agency’s decisions when challenged.
Analysts and specialists: Validation before action
Crime analysts and technical specialists sit at the critical junction between algorithmic output and operational use. Their role is not to accept AI results at face value, but to test, validate and contextualize them before they reach investigators or patrol. This includes assessing data quality, comparing outputs against known baselines, documenting confidence levels and limitations and ensuring AI-generated insights are integrated into workflows in a way that preserves human judgment. When done well, analysts act as a firewall that prevents unvetted outputs from becoming operational assumptions.
Patrol and investigators: Application with limits
Frontline personnel need practical, scenario-based training focused on how AI tools should — and should not — be used in daily operations. Officers and investigators should be trained to treat AI as a lead generator, not a truth teller. This distinction is critical when generative tools are used to summarize evidence or draft narratives. Officers must understand the risk of “hallucinations,” where systems produce plausible but unsupported statements, and the requirement that any AI-assisted output be verified against reports, recordings and physical evidence before it enters the justice system.
Governance starts before purchase
Internal readiness begins at the point of purchase. Historically, agencies have acquired technology in silos — one unit buys one tool, another unit buys a different tool — leading to fragmented data, inconsistent policies and unnecessary cybersecurity exposure.
When possible, agencies should move away from isolated point solutions and toward integrated platforms and interoperable systems. The goal is not “one vendor for everything,” but systems that can securely connect so data from license plate readers, computer-aided dispatch and records management systems can be synthesized responsibly and support a more complete picture of crime patterns rather than disjointed snapshots.
Agencies should establish a standing AI governance committee to review and approve AI tools, use cases and policy controls. This body should include operational commanders, IT and cybersecurity leadership, legal or risk management representatives, training and professional standards personnel and, when possible, records or public information staff to support transparency and documentation readiness.
At a minimum, governance review should define approved and prohibited uses, require human review for consequential decisions, establish validation and testing processes, set data management rules, enforce audit logging and access controls, mandate training before access, define documentation standards, outline incident reporting procedures and require periodic performance and policy reviews.
A critical function of AI governance is ensuring data ownership and control remain with the agency, not the vendor. Contracts should be closely scrutinized to define whether and how agency data may be used, including whether vendor models are trained on that data and under what terms. Agencies must also understand how information is stored and secured, what breach notification requirements apply, who has access through subcontractors and how data will be handled if the relationship ends.
A common and preventable procurement failure occurs when contracts allow broad vendor reuse of uploaded data under vague “service improvement” clauses. When community groups later challenge this practice as unauthorized secondary use or monetization of sensitive information, agencies are left defending decisions they did not fully understand. Clear contract language, paired with strong governance review before purchase, can prevent these disputes before they become public controversies.
Strong governance not only reduces internal risk, it also positions agencies to explain AI use clearly and defensibly when questions arise.
Leadership is the differentiator
The era of AI in policing is here, but the difference between successful modernization and a preventable crisis lies in leadership discipline. Agencies that prioritize culture over code, invest in tiered training and enforce strict governance can harness AI to reduce workload, improve consistency and strengthen public trust — while preserving the essential human element of policing.
References
California Police Chiefs Association. Leading intelligently on AI.
International Association of Chiefs of Police. Artificial intelligence resource hub.
Police Executive Research Forum. Policing and artificial intelligence: Promise and peril.
U.S. Department of Justice. Artificial intelligence applications in law enforcement: An overview.
