Building the Foundations for Safe AI Adoption in the NHS

The NHS is under pressure to improve access to care, reduce waiting times, address workforce challenges, and deliver better outcomes for patients. AI is increasingly supporting this work, from AI-assisted documentation and triage to predictive analytics and more personalized models of care.

AI also has a role in supporting the NHS 10 Year Health Plan for England and its ambition for a more connected, digitally enabled health service. As adoption expands, NHS leaders face a practical governance question: how do Trusts establish consistent oversight of AI across the data, systems, teams, and third-party suppliers involved?

This becomes particularly important as AI moves from individual pilots into everyday clinical, administrative, and operational workflows. Trusts need visibility into where AI is used, what data supports it, how risks are assessed, and who owns decisions throughout the lifecycle.

Strong governance provides the structure for answering those questions consistently. It also gives clinicians and staff clearer guidance on how approved AI tools should be used, where human oversight is required, and how concerns should be escalated.

Key Takeaways

• AI adoption across NHS Trusts is expanding from isolated pilots into clinical, administrative, and operational workflows.
• Safe AI adoption depends on coordinated governance across clinical safety, information governance, cybersecurity, procurement, privacy, data, and risk.
• Clear AI inventories, defined accountability, consistent assessments, and supplier oversight give Trusts greater visibility into how AI operates across the organization.
• Governance processes need to evolve alongside AI use cases, supporting ongoing monitoring and review after deployment.

AI Adoption Is Becoming Part of Everyday NHS Operations

Across the NHS, AI is increasingly supporting patient care and operational efficiency. Trusts are exploring virtual care, remote monitoring, AI-assisted documentation, predictive analytics, and other applications designed to improve access and workforce productivity.

As these technologies move into everyday workflows, the governance requirements surrounding them become more interconnected.

Consider an AI-assisted documentation tool used by clinicians. Governance extends across the information processed by the tool, the supplier providing it, its intended purpose, clinical safety considerations, staff guidance, and the processes used to identify and respond to issues. Each area requires oversight, while decisions made in one area often affect another.

This makes AI adoption an organizational governance issue rather than an isolated technology decision. Trusts need processes that connect the teams responsible for different elements of risk and accountability.

 
Scaling AI Introduces New Governance Responsibilities

Pilot programs tend to have defined boundaries. The participants, technology, data, and intended purpose are relatively easy to identify.

Broader adoption introduces additional dependencies. Different departments may procure AI-enabled services. Existing technologies may introduce new AI capabilities. Suppliers may update products or models. Staff may encounter AI across an increasing number of everyday workflows.

Governance needs to provide visibility across this activity.

An up-to-date AI inventory gives teams a shared view of the systems and use cases operating across the Trust. Each entry should connect the AI system with its purpose, supporting data, accountable owner, supplier, assessments, and relevant governance decisions.

Risk classification then helps determine the appropriate level of review. An AI use case affecting patient care requires different consideration from a lower-risk administrative application. A consistent process helps teams apply oversight according to patient impact, data sensitivity, clinical use, and potential harm to individuals or services.

AI Requires Connected Governance Across Teams and Systems

AI governance spans responsibilities that already exist across NHS Trusts.

Clinical safety teams consider potential effects on patient care. Information governance and privacy teams oversee the appropriate use of patient and staff data. Cybersecurity teams assess security and resilience. Procurement teams engage suppliers. Data teams consider the information supporting AI systems. Risk functions contribute oversight and escalation.

Effective governance connects these responsibilities through defined ownership and repeatable workflows.

Without that coordination, individual controls may exist while the overall picture remains fragmented. One team might complete an assessment while another manages the supplier relationship, with limited visibility between the two. An AI system might have an identified owner without a defined process for reassessment when its purpose or underlying technology changes.

Connecting these activities helps Trusts maintain a documented record of how decisions were reached, which safeguards were required, and who remains accountable after deployment.

Supplier Oversight Needs to Continue After Deployment

Many AI capabilities used by NHS Trusts are delivered through third parties, making supplier governance part of ongoing AI oversight.

Before deployment, Trusts need information about supplier testing, model validation, data retention, AI training practices, monitoring processes, and incident procedures. Contractual requirements should establish expectations around privacy, transparency, accountability, security, and information governance.

Oversight also continues throughout the supplier relationship.

For example, a supplier may update an AI-enabled service after its initial approval. A change to the model, data sources, product functionality, or supporting processes may affect the assumptions behind the original assessment. Connecting supplier management with AI governance helps teams identify when changes require further review. 

Staff Need Clear Guidance for Using AI Responsibly

Governance also needs to work for the people using AI.

Clinicians and staff need to understand which tools have been approved, how they should use them, where human oversight is required, and what to do when an AI-supported output raises concerns.

Training therefore needs to connect AI literacy with practical responsibilities. Guidance should reflect different roles across clinical, operational, digital, procurement, and governance teams rather than treating AI awareness as a single organization-wide exercise.

Clear escalation processes are equally important. Staff need routes to report concerns and potential risks so the relevant governance teams are able to investigate and respond. 

Governance Should Scale Alongside AI Adoption

Deployment represents one stage of the AI lifecycle. Governance continues as technologies, risks, use cases, and requirements change.

Trusts need ongoing visibility into whether deployed AI remains within its approved purpose and risk profile. Monitoring should also capture assessment outcomes, mitigation actions, incidents, governance decisions, and relevant changes to suppliers or supporting data.

This creates a continuous record of AI governance activity and gives accountable leaders greater visibility into where additional action is required.

For NHS leaders, the priority is establishing repeatable governance processes that remain workable as adoption expands. Clear ownership, an accurate AI inventory, proportionate risk assessment, supplier oversight, workforce guidance, and ongoing monitoring provide the foundations for that approach.

Building Governance Before AI Adoption Expands

As AI becomes embedded across NHS services, Trusts need governance processes that provide consistent visibility and accountability across systems, data, people, and suppliers.

Establishing those foundations early gives teams a clearer way to assess new use cases, coordinate decisions, support staff, and maintain oversight as adoption grows.

The next step is understanding where those foundations already exist and where further work is required. Use the AI Readiness Checklist for NHS Trusts to assess your current governance approach and identify areas for action.

Download the AI Readiness Checklist for NHS Trusts to assess your organization's approach across leadership and accountability, AI policies, inventories and data governance, risk assessment, privacy, transparency, supplier oversight, workforce readiness, and monitoring.

Questions NHS Leaders Are Asking About AI Governance