Artificial intelligence has become healthcare's latest ‘whizz-bang' technology, dominating boardroom conversations, filling conference agendas and prompting ambitious predictions about how services could be transformed.
The excitement is understandable. Used well, AI could improve screening programmes, reduce administrative pressure and give healthcare professionals more time with patients.
However, successful transformation is rarely determined by the sophistication of the technology itself. While innovation grabs attention, it is the less visible work around data, infrastructure, governance, processes and people that ultimately determines whether change delivers lasting value.
Throughout my career, I have learned technology alone rarely drives successful transformation. Real progress comes from aligning people, processes, information and systems around a clearly defined problem. Without that alignment, even the most advanced solutions struggle to achieve their potential.
That is why, amid all the enthusiasm surrounding AI, healthcare needs to ask a more fundamental question: are the foundations ready to support it?
A Ferrari still needs a road
AI is often presented as though introducing the technology automatically transforms a service. It is an appealing idea because everybody wants the Ferrari: something powerful, sophisticated and faster than what came before. But there is little value in putting a Ferrari in a stable. It needs a road, a destination and someone capable of driving it.
The same principle applies to AI. Meaningful value depends on trustworthy data, secure infrastructure, clear governance and people who understand how outputs should be used.
One of the biggest challenges is the fragmented nature of healthcare data. Information is stored in different places, captured in different formats and governed in different ways. The data may exist, but that does not mean it can be accessed, shared or interpreted consistently. Yet we are asking AI to draw reliable conclusions from that landscape.
Before asking what AI can do for healthcare, organisations need to address standards, interoperability, information sharing and accountability. They must ensure information can move safely and effectively across services and organisational boundaries.
None of this generates the excitement of a new AI tool, but it is far more important. Without strong foundations, healthcare risks building increasingly impressive capabilities on systems that cannot properly support them.
Start with the problem and bring people with you
The focus on technology can also lead organisations to start in the wrong place. Too often, the journey begins with an impressive AI product, followed by a search for somewhere to deploy it. A more effective approach is to start with the healthcare challenge, understand the surrounding service and then decide whether AI has a role to play.
This matters because every activity in healthcare forms part of a wider pathway. An AI tool may generate an excellent recommendation, but if it cannot fit into existing workflows or reach the right professional at the right time, its technical performance will have little practical impact.
Some of the least successful programmes I have encountered involved technology that worked exactly as intended but failed because the wider environment around it was never adapted.
For all the discussion about models, platforms and technical capabilities, AI adoption is ultimately a people challenge. Technology does not improve care simply because it has been installed. Healthcare professionals need to understand when outputs can be trusted, when human judgement should take precedence and how the technology fits within their day-to-day work. They need training, support and time to adapt.
We should also be honest about how we assess risk. An organisation may reject an AI system because it is ‘only' 87% accurate while continuing to rely on a manual process that delivers lower consistency. That is not an argument for adopting AI regardless. It is an argument for fairly comparing new approaches with the methods they are intended to replace.
Patients must be part of this conversation too. Most people do not care about AI for its own sake. They care whether it helps them access services more quickly, receive a diagnosis sooner or improve their experience of care. If organisations cannot clearly explain why AI is being used and what difference it will make, building trust will be difficult.
Building the conditions for meaningful change
What gives me confidence is that conversations across healthcare are beginning to mature. The most valuable discussions are not about silver bullets or technology for technology's sake. They are about practical use cases, data readiness, governance and workforce capability.
I am optimistic about AI's potential in healthcare, but success will belong to organisations that strengthen their foundations, prepare their workforce and remain focused on solving real problems. The Ferrari may attract the attention, but it is the road, the driver and the destination that determine whether it gets anywhere.
