Healthcare AI Spending Doubles, But 60% of Projects Stay in Pilot Phase
Healthcare organisations are investing heavily in artificial intelligence and seeing returns, yet most are unable to scale beyond early experiments. New research from Riverbed found that while AI spending in the sector has more than doubled, the majority of AI projects remain stuck in the pilot stage.
The survey of healthcare leaders and technical specialists revealed a stark contradiction. Ninety-one percent said return on investment from AI operations initiatives has met or exceeded expectations. Only 31% said their organisation is fully prepared to operationalise its AI strategy across the enterprise.
Data Quality Emerges as the Primary Bottleneck
The biggest obstacle to scaling AI is data. Just 49% of healthcare decision-makers said they are fully confident in the accuracy of their organisation's data to deliver reliable AI outcomes.
Confidence drops further on other critical measures. Only 32% rated their data as excellent for relevance and suitability, while 38% said the same for consistency and standardisation. Yet 88% agreed that improving data quality is critical to AI success.
In healthcare, this gap carries real consequences. Inaccurate or incomplete data can affect patient diagnostics, treatment recommendations, and operational decisions. AI systems are only as reliable as the information they are trained on.
Learn more about addressing this challenge with our guide to Data Analysis and explore AI for Healthcare implementation strategies.
Infrastructure Complexity Slows Progress
Healthcare organisations face another scaling challenge: sprawling IT environments. The average organisation uses 13 observability tools from nine different vendors, creating fragmented operations and silos across IT teams.
Ninety-five percent of organisations said they are consolidating tools and vendors to reduce complexity, while 93% are considering new vendors. Unified communications tools have become essential - employees now spend 43% of their work week using these platforms, and 64% said they are critical to effective operations.
Yet satisfaction remains low. Only 42% said they are very satisfied with performance, citing issues including limited call visibility, dropped calls, and high support requirements.
Data Movement Becomes Strategic Priority
As organisations scale AI, the movement and sharing of data itself is becoming central to success. Ninety-three percent of healthcare respondents view data movement and sharing as important to their broader AI strategy.
Seventy-two percent plan to establish an AI data repository strategy by 2028. Top concerns include the cost of data movement and storage, data security and compliance, and network performance.
The Gap Between Ambition and Execution
Healthcare organisations are willing to invest in AI, and leaders remain optimistic about returns. Early pilots are delivering measurable value. But until providers strengthen data quality and simplify the infrastructure supporting it, scaling AI will remain difficult.
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