The opportunity areas describe where AI can add value. The enablers set the conditions for safe and consistent delivery — they are what every pathfinder depends on.
AI-ready rail data and architecture; secure, federated access.
Strengthening data infrastructure and integration to enable consistent access to trusted data across organisational boundaries. Alignment to national standards and assurance of data dependencies.
Procurement and contract models that support reuse and collaboration.
Commercial and procurement models that support reuse and collaboration across organisational boundaries — moving away from one-off, siloed AI procurement.
Proportionate, risk-based oversight of AI-enabled work.
Aligning AI adoption with existing safety and regulatory frameworks. Proportionate risk-based governance; lifecycle assurance covering monitoring, drift and retraining.
Clear pathways for safe AI deployment in a regulated environment.
Working with the ORR and RSSB to establish proportionate assurance routes — distinguishing low-risk decision support from safety-critical applications.
Role-based AI literacy; targeted skills and apprenticeship pathways.
Capability is treated as central to adoption. Role-based AI literacy across operational, engineering and customer roles. Sector capability baselining and targeted apprenticeship pathways.
Partnerships with academia, industry bodies and the supply chain.
Strategic partnerships and compute capability that support the development and deployment of models that can operate at system scale.
Shared compute capability and model development practices.
Compute capability that supports developing and deploying models at system scale. Model selection that fits the problem, risk profile and operating context, rather than transient technology trends.