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.
