The Official Publication of the Ontario Association of Chiefs of Police 15 • The limitations and risks of AI-generated outputs; and • How to appropriately rely on, question and document AI-assisted work. Training should extend beyond technical staff to include front- line members, supervisors and command staff. AI-driven efficiencies should be planned deliberately. Time saved through automation must be reinvested thoughtfully – whether into investigative quality, community engagement, supervision or member wellness – rather than assumed to be automatically beneficial. Without intentional planning, efficiency gains can introduce new pressures, unreal- istic performance expectations or unintended risk. LABOUR RELATIONS AND ROLE EVOLUTION The introduction of AI-enabled tech- nologies has important implications for labour relations and the evolution of policing roles. While AI is often framed in terms of automation and efficiency, in policing it functions pri- marily as decision support rather than decision replacement. Governance frameworks must clearly reinforce that AI does not supplant professional judgment, discretion or accountability. As AI becomes embedded in operational workflows, supervisors retain responsibility for ensuring that AI-assisted outputs are appro- priately relied upon, reviewed and documented. As AI capabilities mature, the nature of police work is likely to evolve incrementally rather than dramatic- ally. Members may spend less time on repetitive administrative tasks – such as manual transcription, data entry or evidence sorting – and more time interpreting information, exercising judgment, engaging with commun- ities and articulating decision-making. This shift places greater emphasis on analytical skills, critical thinking and the ability to clearly explain how information, including AI-assisted out- puts, informed operational actions. Supervisory and management roles will also evolve. Supervisors may be required to review AI-assisted outputs for reasonableness, ensure that appropriate human oversight is applied and intervene when technol- ogy begins to influence outcomes in unintended ways. At the command level, leaders must ensure that performance expectations, operational directives and productivity measures do not implicitly encourage over-reliance on automation or speed at the expense of investigative quality, fairness or officer discretion. From a labour relations perspec- tive, transparency is essential. Police services should proactively engage associations and members when introducing or expanding AI-enabled capabilities. Clearly communicating what AI will and will not be used for – particularly in relation to performance monitoring, discipline or surveillance – helps mitigate mistrust and misinfor- mation. Establishing clear boundaries around acceptable use, audit access, data retention and oversight supports confidence that AI will not be applied in ways that undermine collective agreements or workplace protections. Ultimately, successful AI adoption in policing depends on treating mem- bers as informed professionals and active participants in change. When governance, training and labour considerations are addressed together, AI can enhance operational effectiveness while preserving pro- fessional autonomy, public trust and workplace confidence. SCALABLE, PROVINCE-WIDE MODELS AI governance is not a challenge any single service should solve in isolation. There is a clear opportun- ity for Ontario-wide collaboration through shared standards, templates and best practices. Scalable models could include: • Members on the OACP AI Committee reports to the OACP Common Police Environment Group-Information and Technology Sub-Committee to assist with governance and process; • Common AI risk assessment frameworks; • Shared definitions and taxonomy for AI-enabled technologies; • Provincial guidance on transpar- ency and disclosure; and • Collaborative engagement with academic, legal and tech- nical experts. By aligning approaches, police services can reduce duplication, improve consistency and strengthen public trust. CONCLUSION AI is no longer optional or peripheral in policing. It is embedded, operational and accelerating. Trustworthy AI will not be achieved through technology alone, but through disciplined governance, informed leadership and a commitment to transparency and accountability. Ontario police services that move beyond hype and operationalize AI responsibly will be better positioned to protect public trust, support their mem- bers and adapt confidently to a future where AI is inseparable from modern policing infrastructure. Christine Robson is a technology leader with more than 30 years of experience delivering secure, innovative IT solutions. Currently the IT Manager at Durham Regional Police Service, she oversees enterprise strategy, cybersecurity and 9-1-1 emergency systems. Beyond her technical expertise, Robson is a formally recognized thought leader in generative AI and data governance. A dedicated mentor and advocate for women in technology, she is known for her inclusive leadership and her ability to drive complex organizational change while building high-perfor- mance, collaborative teams.
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