AI Action Plan Explorer
The Ministry of Justice (MOJ) is at the forefront of responsible AI adoption across government. One year on from publishing the AI Action Plan for Justice, this explorer highlights progress against our ambitions. We are committed to being transparent about how we use AI and will continue to work with industry, academia, civil society and the wider public sector to realise its benefits safely and responsibly.
- Year One snapshot
- to
- Accurate as of
Explore how AI can improve scheduling and make better use of resources
Read the full detail of action 2.2 on GOV.UK. (opens in a new tab)
Year 1 progress
In Year 1, discovery work commenced to understand the potential for AI to improve planning, attendance and operational efficiency.
The MOJ is developing AI-enabled tools to identify case-readiness issues, help caseworkers prioritise the tasks needed to progress cases and support better listing decisions. This includes reviewing the existing backlog to identify cases that may require intervention, could be resolved earlier or are ready to be listed. By helping cases progress more efficiently and making better use of available court capacity, these interventions aim to reduce delays and deliver swifter access to justice.
Year 2 priorities
In Year 2, we will pilot and evaluate AI interventions to help reduce the Crown Court backlog and support faster case progression. We will also work with industry and justice-sector partners to develop robust benchmarks and safe evaluation standards for AI in legal settings. This will allow us to test models consistently, understand their strengths and limitations, and build the evidence needed to support their safe and effective use across the justice system.
Case studies (1)
Data-driven Scheduling & ListingPilot
HMCTS is exploring how advanced analytics can improve listing and make better use of resources across courts and tribunals. Following a 12-week discovery phase, opportunities were identified to optimise hearing room capacity and support the judiciary to list cases more efficiently.
By applying data-driven insights to listing decisions, the approach aims to reduce inefficiencies, improve utilisation of courtrooms, and support smoother case progression. Work has been informed by engagement with Listing Officers and the judiciary to ensure it reflects listing practices and constraints.
Pilots are underway to test data-driven approaches in selected sites within the Crime jurisdiction. This will help assess the practical benefits of data-driven scheduling and inform potential wider rollout.
1. Strengthen Our Foundations
We will strengthen AI leadership, governance and ethics, data and digital infrastructure, and procurement to support AI adoption.
7 actions, 0 case studies: 5 Progressing, 1 Mature, 1 Complete.
2. Embed AI Across the Justice System
We will develop a suite of core AI products to address common, cross-cutting challenges across justice, using a clear “Scan, Pilot, Scale” approach.
12 actions, 13 case studies: 3 Early Stage, 3 Progressing, 5 Mature, 1 Complete.
3. Invest in Our People & Partners
We will invest in talent, training and workforce planning to accelerate AI adoption and transform how we work. We also plan to strengthen our partnerships to support AI-driven legal innovation.
5 actions, 2 case studies: 2 Progressing, 3 Mature.
Actions in 2. Embed AI Across the Justice System
Action 7 of 12- 2.1Mature
Scale AI productivity tools across the system to free up time
1 case study - 2.1.1Complete
Equip every staff member with a secure AI assistant
- 2.1.2Mature
Accelerate insight with AI-powered search and knowledge retrieval
2 case studies - 2.1.3Mature
Use speech and translation AI to free frontline staff from admin burdens
3 case studies - 2.1.4Progressing
Automate repetitive admin tasks with AI-powered agents
2 case studies - 2.1.5Mature
Support the use of AI coding assistants to accelerate development and modernise legacy systems
- 2.2Progressing
Explore how AI can improve scheduling and make better use of resources
1 case studyYou are here - 2.3Early Stage
Explore AI solutions for personalised education and learning
- 2.4Early Stage
Explore how public facing AI assistants could improve access to justice
1 case study - 2.5Early Stage
Improve policy and strategic decision making through AI
- 2.6Mature
Improve decision making through non-generative AI
3 case studies - 2.7Progressing
Evaluate success regularly and adjust our approach as we go
About this snapshot
This explorer will not be updated in real-time. It represents a snapshot of our Year One progress ( to ) against the AI Action Plan for Justice, accurate as of .
In Year One, we committed to establishing strong foundations, building capability and delivering early wins. In Year Two, our priority is to scale what works to improve outcomes more widely across the justice system. All future work will be taken forward in line with appropriate engagement and consultation requirements, including with trade unions where relevant.
