AI
Bridging the healthcare AI ROI gap
Despite significant investment in AI across the healthcare sector, a substantial proportion of AI initiatives remain confined to pilot programs, failing to deliver measurable return on investment at scale. Sathiyan Kutty, chief AI officer at Emids, examines the structural and operational factors that have widened this ‘ROI gap’, explores where genuine value creation is beginning to emerge, and sets out the conditions required for healthcare organisations to move from fragmented early wins to repeatable, enterprise-wide outcomes.
The healthcare sector has been pouring money into AI for years now and, if anything, that spending is accelerating. However, if we look at how many of those investments have delivered real, measurable value at scale, the picture is considerably less impressive. According to a recent study by MIT, 80 per cent or more of healthcare AI projects never move beyond pilot phase, with some analyses finding that 95 per cent of enterprise AI pilots fail to show any meaningful ROI. Healthcare is a harder environment for AI than most
sectors because the space is more regulated, more fragmented, and deeply dependent on institutional knowledge that has been built up over decades and is rarely documented in structured form. Other sectors like retail and financial services have had an easier run of it than healthcare organisations, and that reality deserves more honest conversation than it typically gets. The ROI gap has become more visible as the sector has matured. In the early years of healthcare AI, the absence of measurable returns could be explained by the novelty of the technology and the expectation that deployment pipelines would improve over time. That explanation has become harder to sustain. Adoption has accelerated and tools have become more sophisticated, yet the gap between the promise of AI-driven transformation and the reality of operational outcomes at scale remains wide. Understanding why that gap exists is the first step toward closing it.
The learning gap between AI systems and enterprise workflows One of the least discussed contributors to the ROI gap is the mismatch between how AI systems learn and how healthcare organisations operate. Adoption has risen sharply, with 22 per cent of healthcare organisations now implementing domain-specific AI tools, a seven-fold increase over 2024, but what has not kept pace is the integration of those tools into the broader operational fabric of the organisations using them. Staff are logging in, running queries, and interacting with these systems, but widespread usage is not the same as structural integration. Most AI systems in healthcare today operate with limited context. They can complete discrete tasks but struggle to account for the rules, exceptions, and dependencies that define real-world workflows. In a sector where process complexity is the norm, this becomes a fundamental limitation. A deeper issue is that these systems rarely learn in ways that mirror how enterprises evolve. In many cases,
feedback loops are weak or non-existent. Decisions made by the system are not consistently captured, evaluated, and fed back into model improvement in a structured way. As a result, systems remain static even as the environments they operate in continue to change. In healthcare, where policies, regulations, and operational practices are constantly evolving, this lack of adaptive learning creates a widening gap between system outputs and operational reality. This also highlights the difference between task intelligence and workflow intelligence. Task-level AI can execute a defined function, but workflow intelligence requires an understanding of sequence, dependency, and consequence across multiple steps. Without this, systems cannot effectively manage real-world variability, which is where most operational complexity resides. The result is adoption without transformation. High engagement can create the impression of success, while masking the absence of meaningful operational change. Tools that operate in isolation may generate activity, but they rarely reshape how work gets done.
Where ROI Is beginning to emerge Despite the persistence of the ROI gap, there are areas where genuine value creation is becoming visible. The clearest early signals are coming not from clinical AI
September 2026 Health Estate Journal 89
A medical professional using a digital interface incorporating data analytics and AI.
AdobeStock / Nikon
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