Roy Wills, head of healthcare business and partnerships at Intellias, explains how AI can reduce workforce burnout

Spend a day in any public hospital setting and one issue quickly becomes obvious: it's not a lack of clinical expertise that's under pressure, it's time.

Time to sit with patients, time to finish a shift without hours of admin to complete, time to restore the human experience of healthcare.

For all the discussion around AI transforming diagnosis and treatment, the reality on the ground is far more immediate. Clinicians are navigating fragmented systems, rising demand and an administrative load that often overshadows the clinical work itself.

Which is why the most meaningful impact of AI right now isn't about clinical breakthroughs, it's about something far more practical and far more human – reducing the friction that gets in the way of care.

Burnout is a system issue, not an individual one

Burnout is no longer an isolated issue, it's built into the way the system currently operates. In the UK, surveys from organisations such as the British Medical Association (BMA) consistently show high levels of stress, fatigue and emotional exhaustion across the workforce.

The NHS Staff Survey National Results revealed less than a third (32%) felt there were enough staff to enable them to do their job properly, while most (74%) said they faced unrealistic time pressures sometimes, often or always.

Many clinicians report being consumed by documentation, fragmented IT systems, coding requirements and administrative processes, resulting in substantially limited direct patient interaction.

This isn't just a frustrating issue, it's a consequential one. Reduced patient contact impacts quality of care, while the cumulative administrative burden contributes directly to workforce attrition. When clinicians leave, access worsens, waiting times grow and pressure intensifies across the system.

Importantly, clinicians themselves are generally not resistant to AI – according to the think-tank, The Health Foundation, more than three quarters (76%) of NHS employees support the use of AI to help with patient care and an even greater proportion (81%) said they support the use of AI for administrative purposes.

But what they are resistant to is poorly designed technology. When asked, many express cautious optimism about AI – particularly when it is applied to reduce administrative load rather than interfere with clinical decision-making.

AI as an invisible assistant, not a visible obstacle

Where AI is beginning to show promise, is not in headline-grabbing diagnostics, but in quietly fixing workflow friction.

Ambient documentation tools - often described as AI scribes - are strong examples. Using speech recognition and natural language processing, these systems can capture consultations, generate structured notes and populate records in real time.

The ambition is simple – the technology faces into the background so the clinician can focus fully on the patient in front of them. Early evidence, including studies from the UK and global health systems, suggests these tools can significantly reduce documentation time, improve clinician satisfaction and generate cost savings.

For example, a large-scale NHS evaluation of ambient voice technology across multiple care settings found introducing AI-driven documentation can reduce administrative burden while improving how clinicians and patients interact.

Direct patient care time increased by 23.5%, documentation time fell by over 50% - saving around 47 minutes per shift - and clinician cognitive load and stress reduced significantly. This translated into a 13.4% increase in patient capacity per shift in emergency departments, alongside improved clinician satisfaction and more focused patient interactions.

While outcomes vary depending on implementation, the direction of travel is clear – when administrative burden is reduced, clinicians can regain both time and mental space.

And that really matters. Because much of burnout is not driven solely by workload, but by inefficiency, such as duplicated tasks, clunky interfaces and work that spills beyond clinical hours.

When those pressures are eased, clinicians are better able to practise at the top of their licence – not in theory, but in reality.

Small efficiencies generate system-wide impact

Documentation is only one part of a much larger administrative picture.

Inbox management, for example, has become a growing source of strain in primary care. Digital access routes - while improving patient convenience - have significantly increased message volumes. Many of these require clinical review, adding to already stretched capacity.

AI-driven triage and routing tools are beginning to help here too. By prioritising messages, directing them to the appropriate professional, or resolving simple queries automatically, these systems reduce unnecessary interruptions and protect clinicians' cognitive bandwidth.

What's often underappreciated is how these incremental improvements compound.

Clear documentation supports better coding. Better coding reduces delays and administrative rework. More efficient communication improves continuity of care and patient trust. And over time, these gains translate into both operational resilience and financial stability – critical factors for healthcare systems like the NHS navigating sustained pressure.

Re-humanising care, not replacing it

Of course, AI is not without risk. Poor implementation can add complexity, create new forms of work, or undermine professional confidence. Concerns about over-automation and workforce displacement are valid and must be taken seriously.

But the most effective applications of AI in healthcare share a common principle, they're designed to support clinicians, not substitute them.

They remove low-value tasks while preserving what matters most, which is clinical judgement, human connection and continuity of care.

In doing so, AI is helping to address something deeper than efficiency. It begins to restore a sense of professional purpose.

Because when clinicians have the time to listen properly, to think clearly and to finish their day without a backlog of unfinished notes, the impact is not just operational, it's human.

The real return on AI

Healthcare systems do not suffer from a lack of innovation. They suffer from a lack of capacity, particularly human capacity. As demand continues to rise, no amount of clinical advancement will compensate for a workforce that is overstretched and under-supported.

This is why the most important - and often overlooked - value of AI is not clinical at all.

It is its ability to rebuild the experience of delivering care. To give clinicians back the time, focus and energy that modern healthcare has gradually eroded. And in doing so, to strengthen the very foundation on which healthcare provision depends.