Maximising potential

The criminal justice system is often understood by outsiders as a single consolidated body. However, it’s actually a complex system of systems comprising organisations, agencies, teams and technologies working towards the same outcome. From policing and courts to probation, prisons and victim services, each part holds vital information, but that information can be difficult to share, interpret or act on quickly. This can result in delays, duplication of effort and administrative pressure that make it harder for frontline professionals to focus on the people and decisions that matter most.

This means police officers can spend more time navigating internal systems rather than investigating crime; probation teams must turn complex conversations into structured records; and courts face backlogs and capacity constraints.

All of this happens while victims, witnesses and defendants are left waiting for updates, decisions or resolution. Fairness in the criminal justice system depends on the timeliness and consistency of decisions, which means that inefficiency quickly becomes a justice issue.

This is where artificial intelligence (AI) has the potential to be genuinely transformative: where it can help the justice system make better use of the data, expertise and capacity it already has.

Understandably, the hype around AI has brought anxiety into public debate, particularly when it may be used to analyse sensitive data, such as that the justice system holds. However, caution shouldn’t become paralysis. AI can be a powerful tool in the criminal justice system, but any technology introduced into this environment must be held to an exceptionally high standard. As with any industry looking to adopt AI, those deploying it must evaluate how it can be integrated in a way that strengthens and augments the work that can be done, safely and securely.

The promise and potential of AI

There are already practical examples showing what this can look like. The Ministry of Justice has begun piloting large language models for summarisation and speech-to-text conversion. Tools such as Justice Transcribe, which converts speech into structured data, are being used in probation settings and piloted in courts to improve capacity and efficiency. These tools address real pressures such as turning lengthy conversations into usable records, reducing manual transcription, supporting case preparation and helping professionals spend more time on judgment-led work. AI in justice should not replace people with automated agents. Its value lies in reshaping how people and technology work together.

However, the justice system will not become fairer or more efficient simply because AI is introduced. Poorly implemented technology risks adding another layer of complexity to systems that are already fragmented. If AI is bolted onto outdated workflows, trained on poor-quality data or deployed without clear observability and accountability, it could entrench inconsistency rather than solve it. The opportunity for AI lies in redesigning how work flows across the criminal justice system and how it can be used to make the system more efficient and fairer by freeing up all those involved in its delivery from administrative tasks. This will give them more headspace for complex decision making and other value-add work.

Integrating AI successfully and safely

To successfully and safely integrate AI, the criminal justice system must become more data led. High-quality, interoperable and secure data is the foundation for meaningful AI. This is especially important when information moves from police forces to courts, from courts to probation and from national bodies to local services. If that data is inconsistent, inaccessible or poorly governed, AI risks magnifying weaknesses rather than improving productivity.

Strong foundations must also come with strong governance. Institutions must retain accountability, with human oversight built in from the start, particularly where decisions affect safety, freedom, rehabilitation or access to support. Clear guardrails are needed to manage risk, protect sensitive data and ensure AI tools are observable, auditable and explainable. In justice, innovation must not overrule responsibility.

This is why the most effective route is likely to be gradual, controlled and evidence-led, rather than a significant transformation overnight. Controlled trials of the technology can help organisations test what works, understand risk and build confidence before scaling. The aim should be real operational benefit, rather than integrating AI for AI’s sake.

The justice system also relies on public confidence, which will only be maintained if people can see AI being used carefully and transparently. Sensitive data must be protected in controlled environments, with safeguards around access, storage and use. The system must also be clear that AI is an assistive tool and that human judgement remains the final layer.

A fear of getting AI wrong is understandable, particularly where mistakes carry serious consequences, but avoiding experimentation altogether carries its own risk. If the justice system does not explore how AI can be used safely, it will miss opportunities to reduce pressure on staff, improve access to justice and address inefficiencies that already affect fairness. The challenge is to create an environment where responsible experimentation is encouraged.

Ultimately, AI should be part of wider justice modernisation rather than a standalone solution. Its success will depend on the architecture around it: data quality, governance, organisational culture, public trust, a clear understanding of the outcomes the system is trying to achieve. Without those foundations, AI risks becoming another fragmented initiative. With them, it can help build a justice system that is more efficient, joined up and better able to serve the people who rely on it.

Mike Hill  is the Atos UK&I Head of Technology responsible for the capabilities, services, resources and platforms across the UK&I business lines; spanning Cyber, Modern Infrastructure, Data & AI, Digital Workplace and Digital Applications. Prior to joining Atos in May 2025, held several senior CDIO and CIO roles, including CDIO at the Cabinet Office and CIO for the Home Office Police & Public Protection Technology organisation.