In summary:
- AI helps police officers work more efficiently by improving decision-making and speeding up data analysis, without replacing human judgement.
- Responsible use of AI in policing depends on strong foundations such as strategy, governance, data, technology, culture and skills.
- Police forces like Cheshire are already using AI, and organisations can begin their journey with a free AI Maturity Self-Assessment tool.
Chief Constables have a great opportunity to explore ways Artificial Intelligence (AI) can solve many policing challenges. However, it’s not about using AI for the sake of it but finding the right tool for each problem. AI can improve officers' productivity, front-line operations and threat detection, but it's important to use it where it makes the most sense.
AI is already present in policing through the phones in officers’ pockets, the satnavs in their cars and the standard office software they use in the course of their daily work. Having an AI strategy maintains technological leadership, aligning innovation with national public safety policies. It promotes AI literacy, ethical governance, and controlled deployment, ensuring compliance and maintaining internal ethical standards. On the tactical front, AI tools could be directly deployed to frontline officers, enabling real-time data analysis, predictive policing, and automated monitoring, enhancing decision-making and reducing administrative burden. Chesire Police is already leading the way in the UK by developing an AI capability to tackle stalking. This capability will analyse incoming incident reports and help identify staking behaviours.
AI doesn’t replace people, it empowers them
There is a common misconception that AI possess intelligence beyond humans. In reality, while AI outperforms humans in certain tasks such as processing vast amounts of data at speed or identifying complex patterns, it should be considered an enabler to empower people, enhance decision-making and drive efficiency rather than replace the humans themselves.
A key strength of AI – at this stage – is that it adds capacity and consistency. In traditional investigations for example, evidence is acquired, sifted and prioritised by humans in structured teams following set processes. Links are identified and explored; locations and actions are verified, and relationships are proven. Checks and balances ensure that processes are followed and that the conclusions drawn are valid.
For example, a properly configured AI system might process in minutes a volume of material which would take a team of 100 officer’s months, scanning and connecting data across different sources. Ultimately enabling more work to be done, quicker, with fewer people, at a lower cost. This can already be seen in technologies like retrospective facial recognition which enables identification searches to be completed in seconds rather than days.
AI is clever and can learn from data within certain limits. But it doesn’t always behave as expected. When used on a large scale, it can sometimes give strange or incorrect results, this is commonly known as ‘AI hallucination’, that’s why strong governance and human oversight are essential. Having a ‘human in the loop’ means there’s always someone checking what the AI is doing, making sure it makes sense, and stepping in when needed. This helps keep things safe, fair and accurate. and is especially important when dealing with content or decisions of an ethical nature.
What are the benefits of AI for public safety?
The potential benefits of such technology in policing are clear:
Enhanced speed and consistency by streamlining various processes, making them faster and more reliable,
Pattern recognition across vast datasets,
Identification of complex associations in serious crime,
Increased operational efficiency by reducing back-office waste allowing organisations to run more smoothly and effectively.
The application of such capacity to modern police challenges could make a major contribution towards both force effectiveness and efficiency, with dividends for both public safety and the public purse.
Yet, alongside its promise comes risk. Bias, transparency gaps, and questions of accountability remain critical concerns. For AI to succeed in such a sensitive sector, public trust must be at the core of its design, deployment, and governance.
The UK Government’s AI Playbook highlights a central truth: AI is not a magic solution, but a set of tools that must be implemented responsibly, with clear boundaries and safeguards in place. In policing, this need for caution and integrity is magnified. Decisions informed by AI can shape people’s lives, liberty, and safety. Any misstep risks not just reputational damage, but a fundamental erosion of public trust.
Six pillars for responsible AI in policing
Our approach draws on six key pillars for AI readiness: strategy, governance, data, technology, culture, and expertise. Each is vital in mitigating risk:
Governance ensures fairness, accountability, and clear oversight structures, aligning with the National Police Chiefs’ Council’s Covenant for Using AI in Policing (NPCC).
Data quality reduces the likelihood of bias and ensures AI recommendations are grounded in accurate, representative insights.
Culture and expertise foster human oversight, embedding the skills and ethical mindset required to question, challenge, and validate AI outputs.
Technology and infrastructure ensure systems are scalable, interoperable, and secure, protecting sensitive data against cyber threats.
Together, these create a framework where AI is a trusted partner that supports officers in decision-making but never replacing human judgement. For early adoption of AI in policing, internal admin processes are where Chief Constables should start. Quick wins, low risk, frees officers up for front line activity, builds public trust.
How do we practically weave AI into the fabric of policing in an ethical and effective way? The answer lies in all the six areas we referenced above.
In the second part of our AI in policing mini-series we’ll discuss how AI can help police officers spend more time in communities by reducing admin tasks, improving response times, and making better use of resources.
Take the first step today
Using our six-pillar framework, the AI maturity lite self-assessment evaluates your organisation across the key foundations of successful AI adoption. The personalised report you’ll receive outlines your scores in each area, highlights priority focus points, and gives you clear, actionable next steps.
AI maturity isn’t just a tech question, it’s a business imperative. And the organisations that act now will be the ones creating real competitive advantage tomorrow.
Complete our free online AI Maturity lite Self-Assessment today to get your personalised report and take the first step in building a business case that gets real backing.