This article is part of the Police1 Leadership Institute, an initiative focused on the challenges facing law enforcement leaders tasked with guiding their agencies through rapid operational and technological change. Throughout this series, Police1 will explore what AI adoption means for police leaders — not just in terms of tools, but in leadership responsibility. That includes evaluating emerging technologies, managing legal and ethical risk, leading organizational change and ensuring innovation strengthens public trust rather than undermines it.
By Lieutenant Oscar A. Martinez
The Los Angeles County Sheriff’s Department’s (LASD) 2019 Recruitment, Hiring, and Retention Process Improvement Report provides a sobering picture of the department’s recruitment challenges. [1] While the report was published in 2019, the structural issues it identified — lengthy hiring timelines, inefficient assessments, weak data systems and high attrition — remain common across large law enforcement agencies and continue to shape recruitment debates today.
Lengthy hiring timelines: The report found that LASD’s hiring process is significantly longer than those of competing agencies. Because qualified applicants often apply to multiple jurisdictions, many accept the first job offer they receive. By the time LASD completes its vetting, top candidates are often gone.
Inefficient assessments: While LASD’s written and physical tests see pass rates as high as 78% and 96%, respectively, background investigations filter out roughly 74% of candidates. This imbalance means that costly and time-consuming investigations are conducted on large numbers of candidates who were unlikely to succeed in the first place.
Weak data infrastructure: The department’s personnel data is stored in hard-copy files, making it difficult to analyze trends such as attrition or diversity gaps. Without a modern human capital management system, LASD lacks the ability to make timely, data-informed decisions about recruitment or retention.
High academy attrition
Attrition at LASD’s recruit academies averages more than 20%, higher than comparable agencies. This represents a significant loss of time, money and potential talent, with each dropout reducing the department’s ability to fill vacancies.
Retention struggles: Between 2013 and 2018, LASD lost more than 2,500 sworn personnel, primarily due to retirement but also disability and lateral transfers. Combined with high recruitment attrition, this cycle has left the department struggling to maintain authorized staffing levels.
Taken together, these challenges suggest a system stretched beyond its limits. To remain competitive and meet the expectations of modern candidates, LASD and similar agencies must look beyond incremental reform. Artificial intelligence (AI), particularly generative AI, offers an opportunity to streamline communication, improve assessments, reduce attrition and support data-driven workforce planning in ways traditional approaches cannot.
Recruitment challenges
The ability of law enforcement agencies to recruit and retain qualified personnel directly affects public safety, community trust and organizational effectiveness. Yet agencies nationwide face unprecedented recruiting pressures. Shrinking applicant pools, generational shifts in career preferences, increased competition from private-sector employers and negative perceptions of policing have made attracting and retaining talent increasingly difficult.
While AI offers clear operational efficiencies for law enforcement, legislative and policy frameworks addressing privacy, bias and oversight are still developing. [2] Agencies increasingly encounter AI-powered technologies in both criminal investigations and administrative operations. As officers confront cases involving AI misuse, they also recognize that integrating these tools responsibly can improve efficiency and expand organizational capacity. As governance continues to evolve, law enforcement leaders must balance innovation with the protection of constitutional rights and civil liberties. [2]
The challenges identified in the LASD report demand more than incremental fixes. Generative AI offers measurable potential. Deloitte estimates AI can save recruiters up to 23 hours per hire, while Hilton Worldwide reported a 75% reduction in time-to-hire after adopting AI tools. [3] For LASD, similar applications could shorten hiring timelines, digitize personnel records and support predictive analysis of attrition and staffing needs. Adoption, however, must be deliberate. Grounding AI use in defensible data and transparent processes allows agencies to pursue practical innovation while maintaining public trust. [2]
Generative AI to support, not replace humans
Advances in AI present a practical opportunity for law enforcement recruitment. Generative AI systems — capable of creating content, identifying patterns and producing human-like interactions — are already reshaping industries ranging from healthcare to education. Rather than memorizing data, generative AI predicts sequences, enabling adaptable and context-aware responses. [4] Its ability to automate workflows and analyze large datasets makes it particularly relevant for recruitment and workforce management. [5]
In law enforcement, generative AI has shown promise in supporting communication, information management and administrative efficiency. [6] Importantly, these tools are most effective when used to support, not replace, human judgment.
While AI can improve the efficiency of initial candidate screening, human–AI collaboration remains essential to preserve empathy, cultural fit and fairness. [7] Private-sector organizations have already demonstrated measurable efficiency gains, including reduced time-to-hire, illustrating what may be possible for public-sector agencies willing to adapt proven practices.
Approximately 88% of companies now use some form of AI in early-stage candidate screening. AI is no longer experimental; it is a widely adopted tool that can streamline hiring, improve candidate evaluation and modernize human capital management when implemented responsibly.
Selecting the right AI recruitment tool
When selecting an AI recruiting tool, the choice should go beyond cost and focus on how well the platform aligns with organizational needs.
Agencies must first identify their biggest hiring challenges, whether that means losing candidates due to slow timelines or struggling to reach diverse applicant pools. The features of the tool should also match the scale and structure of the organization, since some platforms are designed for small teams while others support large, complex workflows.
Fairness and transparency are essential considerations, with modern tools expected to include explainable AI and bias-mitigation safeguards to meet compliance standards. Recruitment technology should also provide clear analytics and measurable returns. Research shows that nearly three-quarters of recruiting teams now evaluate their tools based on ROI metrics to guide decision-making. [8] To address each of these challenges, generative AI can be a competitive difference in attracting and selecting qualified candidates.
Why generative AI is the answer
In recruitment, generative AI offers capabilities beyond traditional and predictive AI systems. Traditional AI is rules-based and task-specific, while predictive AI analyzes historical data to forecast outcomes such as attrition or staffing shortages. Generative AI goes further by creating new content and adaptive solutions.
Working alongside human recruiters, generative AI can support personalized outreach campaigns, customized study materials and adaptive training resources refined through feedback. Microsoft notes that generative AI’s ability to create original text, images or code makes it especially valuable for tailored recruitment and candidate support applications. [9]
Generative AI also addresses long-standing recruitment barriers. Table 1 highlights how specific challenges align with AI-enabled solutions. These examples illustrate conceptual alignment rather than vendor-specific outcomes. The primary challenge for agencies lies in managing adoption, training staff and integrating AI into existing systems.
Table 1: How AI can help recruitment challenges
| Challenge | Why it matters | Where AI fits |
|---|---|---|
| Lengthy hiring timelines | Qualified candidates accept offers from faster agencies | AI chatbots and automated scheduling |
| Ineffective assessments | Resources spent on candidates unlikely to succeed | Predictive analytics and AI-augmented testing |
| Outdated data systems | Limited visibility into workforce trends | AI-enabled dashboards and forecasting tools |
| Academy attrition | Roughly 20% of recruits fail to complete training | Prediction models and personalized support |
| Retention struggles | Overtime costs and staffing instability | Turnover analysis and wellness indicators |
While there are upfront costs, the long-term benefits include fewer vacancies, reduced overtime and a more equitable, community-reflective workforce. For law enforcement agencies, AI-powered recruitment software can cost from several hundred to several thousand dollars per month, depending on size, features and customization. These platforms often include candidate analytics, background check integrations and compliance support. Larger departments may benefit from scalable solutions that manage high applicant volumes and complex hiring workflows, with vendors typically offering tailored quotes to fit public safety needs. [10]
Encouraging adoption
Adopting AI does not mean replacing human judgment. Instead, it frees human staff to focus on the elements of recruitment that require personal expertise, such as evaluating character, conducting interviews and mentoring recruits. AI handles the rest — communication, data analysis and predictive modeling (see Table 2).
Table 2: Traditional recruitment vs. AI-enhanced recruitment
| Area | Traditional approach | AI-enhanced approach |
|---|---|---|
| Communication | Delayed updates and applicant uncertainty | Automated updates and 24/7 chatbot support |
| Screening | Generic testing and process bottlenecks | AI-augmented writing and personality tools |
| Data management | Paper-based files and manual tracking | Digital dashboards with predictive analytics |
| Candidate outreach | One-size-fits-all messaging | Personalized, generative AI-driven content |
| Academy support | Standardized training programs | Adaptive and individualized support models |
At the same time, ethical considerations remain central. While AI can improve recruitment by making processes more efficient, fair and inclusive, it also raises concerns about bias, transparency and oversight. These risks must be weighed against the benefits. When implemented responsibly, AI can help correct inequities rather than reinforce them and demonstrate a department’s commitment to professionalism and accountability. In doing so, AI adoption can strengthen public trust, particularly at a time when law enforcement legitimacy faces heightened scrutiny. [11]
Ethical and practical considerations
With proper safeguards, AI can promote fairness by standardizing assessments, reducing subjective bias and applying transparent criteria to all applicants. Generative AI can also support efforts to build a workforce that reflects the community by enabling multilingual, culturally relevant outreach and identifying where certain candidates may face higher dropout rates. Predictive analytics can then guide targeted interventions such as mentorship or additional training support.
Equitable recruitment is not only a fairness issue but a public safety and legitimacy issue. Communities are more likely to trust agencies that visibly reflect them. When used responsibly, AI is not a shortcut but a practical opportunity to support more inclusive, representative and effective law enforcement organizations.
Future implications
The use of AI in law enforcement recruitment offers significant advantages but also raises important concerns. On the positive side, AI can streamline hiring by automating resume screening and candidate evaluations, improve candidate selection by focusing on relevant skills and reduce human bias through standardized, objective assessments. At the same time, challenges remain. While AI can deliver efficiency gains, it also introduces legal and ethical risks, particularly for organizations that depend on diverse and culturally sensitive staffing. [11]
Algorithmic bias in training data may unintentionally disadvantage underrepresented applicants, qualified candidates with non-traditional backgrounds could be overlooked and agencies must navigate strict compliance requirements related to anti-discrimination laws and the Americans with Disabilities Act. [11] In addition, because law enforcement relies heavily on judgment, communication and cultural competence, AI systems may struggle to fully assess these human-centered skills. Taken together, these considerations suggest that while AI can modernize recruitment in agencies like LASD, it must be paired with safeguards that prioritize fairness, equity and community trust.
The use of AI in recruitment is only the beginning. Over time, generative AI could support the entire employee life cycle.
- Retention: AI can analyze exit interview data, identify indicators of burnout and suggest wellness interventions.
- Training: AI can support adaptive learning platforms for deputies beyond the academy.
- Community engagement: AI can help generate communication strategies that strengthen public trust and transparency.
By embracing AI thoughtfully today, agencies position themselves to leverage even greater benefits tomorrow.
Measuring impact through recruitment metrics
To ensure AI adoption delivers meaningful results, agencies must track the right recruitment metrics. Recruiters face intense competition for qualified candidates, limited budgets and the need to operate across multiple platforms. Tracking key performance indicators allows organizations to measure efficiency, cost and candidate experience across the hiring pipeline. [12]
Cost Per Hire (CPH) and Cost Per Click (CPC):
- Cost per hire: Total recruitment spend ÷ number of hires.
- Cost per click: Total ad spend ÷ number of clicks.
- Why it matters: High CPH signals inefficiencies in hiring strategies; high CPC suggests issues with ad design or the platforms used.
Time to Hire and Time to Fill
- Time to hire: Number of days from first candidate interaction to hire.
- Time to fill: Number of days from job requisition approval to offer acceptance.
- Benchmarks: Average time to fill is 48 days in the U.S. (53 in Europe; 47 worldwide).
- Why it matters: Both metrics highlight bottlenecks and inefficiencies in the recruitment pipeline.
These metrics provide recruiters with data to diagnose problems in cost efficiency and hiring speed. Tracking both financial and timeline (time to hire, time to fill) KPIs allows organizations to improve strategies, allocate resources better, and shorten recruitment cycles.
Conclusion
The recruitment challenges facing LASD reflect a broader national crisis in law enforcement staffing. Traditional hiring systems are often too slow, inefficient and resource-intensive to meet modern demands. Generative AI offers a practical path forward. It can streamline hiring, enhance assessments, reduce attrition, personalize outreach and support data-driven workforce planning.
AI is best understood as an augmentation of human intelligence. For law enforcement leaders, this means equipping their organizations with tools that improve efficiency while reinforcing fairness, transparency and accountability. Encouraging the adoption of AI is not merely about operational gains; it is about ensuring agencies are staffed with the right people to serve and protect communities in the 21st century.
Caution remains necessary, but it must be paired with deliberate action. To meet today’s staffing challenges and tomorrow’s public safety demands, law enforcement leaders must engage AI thoughtfully and responsibly.
References
- County of Los Angeles. Sheriff recruitment, hiring, and retention process improvement report. Prepared for the County of Los Angeles; 2019.
- Ezeh N, Widgery A, Canada C. Artificial intelligence and law enforcement: the federal and state landscape. National Conference of State Legislatures.
- Turkkan B. The pros and cons of using AI in recruitment. USEH International.
- MIT News. Explained: Generative AI. How do powerful generative AI systems like ChatGPT work, and what makes them different from other types of artificial intelligence?. Massachusetts Institute of Technology.
- TechTarget. What is GenAI? Generative AI explained.
- PenLink. How generative AI can help law enforcement keep communities safe. PenLink Blog.
- Amitabh U, Ansari A. Hiring with AI doesn’t have to be so inhumane. World Economic Forum.
- IQTalent Partners. AI recruiting ROI: Measuring success and impact.
- Microsoft. Generative AI versus different types of AI. Microsoft AI.
- People Managing People. Recruiting software pricing guide 2025.
- Abrams Z. Addressing equity and ethics in artificial intelligence. Monitor on Psychology. 2024;55(3).
- Hirematic. Recruiting metrics you need to monitor for hiring success.
About the author
Lieutenant Oscar A. Martinez is a veteran law enforcement professional with a California law enforcement agency and United States Marine Corps combat veteran. Throughout his career, he has served in diverse assignments including custody, patrol, professional standards, public information, and executive aide roles. His leadership is marked by integrity, innovation, and a deep commitment to accountability and community engagement. Martinez is recognized for his ability to modernize operations and mentor the next generation of law enforcement leaders. He holds master’s degrees in Organizational Leadership and Criminal Justice, and is currently enrolled in the California POST Law Enforcement Command College.
This article is based on research conducted as a part of the CA POST Command College. It is a futures study of a particular emerging issue of relevance to law enforcement. Its purpose is not to predict the future; rather, to project a variety of possible scenarios useful for planning and action in anticipation of the emerging landscape facing policing organizations.
