TECHNOLOGY
Delivering measurable improvement with AI
While AI is more than capable of delivering meaningful and measurable improvements in healthcare delivery, many organisations are still struggling to bridge the gap between expectation and outcome. Here, Demetri Papazissis, co-founder & CEO of Superbo AI, explains how estates and facilities leaders can approach the implementation of AI in a way that is practical, sustainable, and valuable.
Artificial intelligence is now firmly embedded in the healthcare conversation. It appears in strategy documents, vendor briefings, innovation programmes, conference agendas, and boardroom discussions across the sector. Yet for all the visibility AI now enjoys, the operational reality on the ground remains mixed. Many healthcare organisations are still struggling to translate AI ambition into meaningful, measurable improvement. This gap between expectation and outcome is often misunderstood. The instinct is to assume that when an AI initiative stalls, disappoints, or quietly fades from view, the problem must lie in the technology itself. In most cases, that is not the real issue. The models are not the weakest link. The real weakness usually lies in the operational environment they are being introduced into. Healthcare organisations are trying to deploy AI into
Data sits across multiple systems.
systems, workflows, governance structures, and teams that were not originally designed to support it. That is why so many initiatives fail to move beyond pilot stage. It is not because the intelligence is insufficient. It is because the organisation is not yet set up to absorb, govern, and operationalise that intelligence effectively. This is where the conversation needs to mature. The question is no longer whether AI is capable. It clearly is. The more important question is whether healthcare organisations are approaching implementation in a way that is practical, safe, and sustainable. For estates and facilities teams, this is a particularly important moment. Much of the public attention around AI in healthcare still focuses on clinical use cases, diagnostics, and direct patient-facing applications. Those areas are understandably high profile, but they are also among
the most complex and sensitive environments in which to deploy new technologies. By contrast, estates and operational functions offer something both less glamorous and, in many cases, more valuable in the short term: a realistic path to execution. That matters because the future of AI in healthcare will not be decided by the most impressive demonstration. It will be decided by which organisations can make it work, consistently, in the real world.
The problem is rarely the model There is a persistent narrative around AI that suggests if something underdelivers, the technology simply was not advanced enough. In practice, that is rarely what happens. More often, organisations fail to see value because
they have underestimated the discipline required around implementation. Data sits across multiple systems with inconsistent quality and ownership. Processes are fragmented. Teams do not have clear accountability for how AI outputs should be reviewed, acted on, or escalated. Governance is treated as a later-stage concern rather than an initial design principle. End users are introduced to new tools without enough thought being given to workflow fit, adoption, or day-to-day usability. Under those conditions, even strong technology quickly loses momentum. This is especially true in healthcare, where operational
complexity is high and tolerance for disruption is understandably low. Estates and facilities leaders are already navigating intense demands around compliance, cost control, service continuity, maintenance performance, infrastructure resilience, capital planning, energy pressures, and workforce constraints. They do not need AI to generate more noise. They need it to reduce friction. That is why the real test for AI in healthcare is not whether
it can produce a clever output. It is whether it can fit into an operational setting in a way that improves decisions, saves time, reduces avoidable effort, and supports better outcomes without creating new risks or new layers of confusion. In other words, success depends less on model
sophistication and more on execution quality. That distinction matters. It shifts the conversation away
from abstract capability and towards operational readiness. It encourages organisations to stop asking, ‘What can AI theoretically do?’ and start asking, ‘Where are our current operational bottlenecks, and what would meaningful improvement actually look like?’. That is a healthier question. It is also a more productive
one. 184 Health Estate Journal October 2026
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