Yu Liu, CTO and Co-Founder, Heidi, says AI scribes and robust infrastructure could reduce clinician burnout and unlock the true potential of Digital Transformation in the NHS.
Imagine an NHS GP at the end of a 12-hour day. The last patient has left and they’re exhausted but there is still an hour or more to spend on notes before it is safe to go home.
The promise of Digital Transformation was reduced admin and better tools yet the foundation still isn’t there in healthcare.
Two-thirds (68%) of doctors do not believe that the NHS’ digital infrastructure currently provides a solid platform for AI applications that can make a difference for patients. AI-powered tools may sound compelling but they aren’t being used to their full potential. Clever models and innovative features are out there but the collective mindset just isn’t quite ready for them.
Introducing AI tools to a wider range of healthcare settings requires a total system shift in mentality. Architecture requires as much attention as algorithms; technical teams must consider whether models should be deployed on-premises, in secure sovereign clouds or at the edge. Equally, healthcare organisations must consider how tools behave when connectivity is unreliable and how they fit into live clinical workflows rather than idealised ones on a whiteboard.
For CIOs, building AI that is safe enough for the NHS and robust enough to move the dial on clinician workload means resisting the temptation to treat AI as another software category to procure. Instead it needs to be a long-term capability to weave into the fabric of care delivery. Nowhere is this more urgent than in the seemingly mundane but mission-critical world of clinical documentation.
Documentation: Where NHS pressure and AI potential collide
Clinicians are operating in a perfect storm: relentless demand, constrained budgets, chronic workforce shortages and rising burnout. Nearly half (47%) of NHS staff report that their role is affecting their mental health. These challenges aren’t confined to GPs; pressure is becoming a structural feature of the system, not an exception.
Simultaneously the NHS is evolving its digital governance. Integrated Care Boards (ICBs), the NHS organisations responsible for planning and funding most local health services, have new digital decisionmaking responsibilities. Digital will no longer sit neatly within centralised IT departments. Instead it is becoming part of everyone’s role with ICBs becoming accountable for oversight and coordination.
Within this context administrative load is becoming a pressure multiplier. With every new service, pathway and governance requirement, documentation obligations expand, which is a key driver of clinician burnout. This is where technology comes in, including AI scribes, which listen to patient consultations and automatically generate accurate, structured medical notes and documentation. Their potential has already been realised, with a Great Ormond Street Hospital (GOSH)-led trial finding that feelings of clinician overwhelm associated with notetaking and qualitative feedback reduced by 35% when using AI scribe technology.
For NHS CIOs, documentation is a pragmatic starting point. It is a low-risk, high-reward use case where AI can safely demonstrate value at school, provided that the technology is designed with the NHS clinical and technical context firmly in mind.
Architecting for NHS expectations on safety and resilience
For those introducing AI tools into their clinical workflows, perhaps for the first time, human oversight is the golden non-negotiable principle. AI outputs should always be treated as drafts to be reviewed by the clinician, with AI acting to support not replace professional judgment. The moment a system blurs that line, it risks losing the trust that is essential for adoption.
Data stewardship is equally vital. Patient-identifiable data must remain within environments controlled by the Trust, regardless of the infrastructure on which the AI tool is built. The approach should be the same for on-premises, sovereign cloud environments and hybrid models: keep sensitive data secured within NHS networks. Resilience is equally critical here; connectivity constraints cannot be an afterthought. AI documentation tools should be able to continue running when bandwidth drops, such as by using local processing, securely caching notes on the device and syncing back to clinical systems when connectivity returns.
Finally, user experience must recognise the full spectrum of digital confidence. Not every clinician is a digital native and not every clinician has the same use case for AI documentation. Interfaces should therefore be accessible, explainable and forgiving, with clear controls and feedback so that clinicians understand what the system is doing and how to correct it when needed.
The test for these tools is straightforward: would clinicians trust this tool with their notes on a busy Monday morning clinic, in the least digitally mature part of the estate, after only minimal training? If the honest answer is no, the architecture is not yet NHS-grade.
From isolated pilots to scalable AI platforms
Many AI initiatives demonstrate promising results in a single ward, clinic or Trust only to stall when it comes to wider rollout. This ‘pilotitis’ reflects a lack of clarity on how to move from the test phase to routine use at scale and it results in missed opportunities for the healthcare system as a whole.
There are three practical levers that can help CIOs break through this barrier, starting with standardised evidence and assurance. Each successful pilot should become an example to follow, with clear success metrics and examples of lessons learned.
Subsequent sites can then move more quickly, following paths already paved to adopt similar tools without starting from scratch. These use cases can also support the second lever; generating impact measurement that genuinely matters to boards. Senior leaders are looking for hard metrics, such as time saved per consultation, reduction in backlog, improvement in appointment availability and indicators of staff wellbeing, alongside traditional system-level KPIs. If those measures are defined and baselined early, they can underpin the case for spread and long-term investment.
Operational readiness comes next. Scaling a tool like an AI scribe from one clinic to an entire Trust requires a well-defined onboarding pathway, complete with identified ‘AI champions’, clear responsibilities for clinical, digital and operational teams and infrastructure that is prepared for the increased load. With all these elements in play, CIOs can frame AI scribes as part of a broader platform strategy rather than one-off solutions, making it easier to build momentum. Endless ‘pilotitis’ won’t move the needle; a repeatable pattern for safe AI deployment will.
Winning with skilled, AI receptive talent
Safe, governed and measurable deployments are impossible to achieve without good talent, especially early-career talent. A strong bench of engineers, product managers and clinical informaticians who understand both the technology and the realities of care is essential. This deep domain expertise cannot be brought in overnight; it has to be built through long-term collaboration between technical teams and senior clinicians who know where the real risks and opportunities lie.
As AI becomes more embedded in documentation and other workflows, the NHS will increasingly need engineers who are comfortable across a range of infrastructure environments, from secure on-prem and edge deployments to cloud-native development.
Designing these offline-capable, privacy-preserving systems isn’t a generic software development task and certainly not one that can be outsourced entirely to AI. This is a specialised skillset and should be championed in digital teams across the NHS.
AI projects can also be seen as opportunities for traditionally non-digital native teams to flex new skillsets. Those with roles in clinical safety and project management will need to learn from transformation leads and data engineers and vice versa for a deployment to be successful. In doing so each implementation can help expand individuals’ capacity to adapt the organisation’s use of AI over time, creating a virtuous cycle that benefits workforces as much as systems.
Healthcare is AI’s proving ground and AI is the CIO’s next decision
Healthcare is emerging as one of the most important growth frontiers for AI in the UK. The government champions new use cases for this technology in the sector on a regular basis, from cutting-edge healthcare research to new devices that will revolutionise diagnosis.
For CIOs, AI in the NHS is about more than market share but where the impact of AI on human lives is most visible and where public trust will be hardest-won or quickest to lose.
Systems that succeed will focus first on high-volume, high-friction work such as documentation, where even modest, consistent gains translate into more time with patients and less time at the keyboard. They will treat safety and assurance as part of the core design, not as paperwork to complete once the technology decision has already been made. They will also channel investment into people across clinical teams and the technical and operational roles that support them, so that capability grows in step with the tools themselves.
When that balance is in place, AI scribes can offer early, visible proof that AI can simplify the stack rather than add another layer of complexity. Used well, they give time back to clinicians and by extension to patients and families.
The next move now sits with NHS CIOs and their counterparts across Integrated Care Systems. The choices they make about architecture and governance and about the partners they trust will decide whether AI remains locked in pilot mode or matures into a safe, scalable capability that is woven into everyday care. The opportunity is significant and so is the responsibility to build for NHS-grade safety and scale from the outset.
