F E A T U R E that learning shouldn’ t stop once the agent is up and running. Users are more likely to engage with AI if they understand how it fits into their work, where its boundaries lie and how to question or challenge its outputs. After all, AI adoption has the capacity to change how NHS trusts surface and respond to data – while also restructuring workflows and, sometimes, even cultures. It’ s therefore far more than a routine IT system upgrade.
As users learn how the technology works and evolves, continuing support can reinforce governance and stress the importance of retaining human oversight. Onboarding also plays an important part in helping users incorporate AI into established ways of working. A system that has technically gone live will not deliver its full value if users remain uncertain about when to use it, how to interpret its outputs or when to escalate a concern.
Users also need clear guidance on what to do when an answer appears incomplete, inaccurate or potentially biased. That includes manually checking it against approved source material, challenging the output and escalating the query where necessary. This makes human oversight part of everyday work rather than an abstract principle of governance policy.
Using Copilot data to improve skills and training
AI deployments could also help surface knowledge and training gaps. For example, Copilot’ s back-end query log can create an evidence base of the questions employees are actually asking – rather than leaving the trust to rely on assumptions about what they understand. This could enable the trust to identify recurring questions and knowledge gaps, showing where additional training or guidance may be needed.
Access to this usage data offers the trust an opportunity to respond by changing onboarding processes, using additional workshops and internal communications and improving the information at source. When handled with appropriate privacy and access controls, the data creates a feedback loop through which training, support and the underlying information can evolve alongside actual usage.
Conclusion
THE RESEARCH DEMONSTRATES THE SAME UNDERLYING TENSION THAT WILL BE FAMILIAR ACROSS MUCH OF THE NHS: ENTHUSIASM FOR AI ADOPTION MUST BE MATCHED BY A FINANCIAL AND CULTURAL COMMITMENT TO IMPROVE DATA QUALITY.
AI adoption shouldn’ t be measured in terms of whether a chatbot, Copilot or agent has gone live. The more meaningful test is whether a trust has connected the right data, established dependable workflows and controls and equipped its people to use, question and improve the technology. That means putting the operational, technical and cultural foundations in place in the right sequence, with retained human oversight as the capability develops. The prompt interface may be where many users first encounter AI. But it is the data, governance and organisational capability behind it that will determine whether AI systems deliver lasting value and the positive organisational changes that IT leaders within NHS trusts are looking for. �
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