This year, the healthcare industry will see AI shift from experimentation to execution. From predictive analytics that support earlier diagnosis to automation that reduces administrative burden on clinicians, AI promises to reshape how care is delivered and data is managed.
In 2025, the UK government oversaw the world’s largest AI trial in healthcare, demonstrating how Microsoft 365 Copilot could save the NHS hundreds of thousands of staff hours each month while reducing costs and improving patient care. This momentum is further reinforced by the Medicines and Healthcare products Regulatory Agency’s ”call for evidence” on AI regulation, signalling expected widespread adoption and heightened scrutiny in the UK.
These efforts support the government’s commitment to shift the NHS from analogue to digital through the 10 Year Health Plan which aims to make the NHS the most AI-enabled care system in the world. For healthcare organisations and providers, it is clear that AI will play a central role in future care models and the readiness of their data will determine how much value it can deliver.
However, many healthcare leaders still struggle to effectively manage the vast data sets AI requires. Critical patient information remains fragmented across paper and electronic health record systems, imaging repositories, research databases and operational systems. As AI tools demand more contextual and trustworthy data, this fragmentation fundamentally limits their efficacy. Ultimately, the challenge is no longer a lack of data, but an inability to unify and govern it at speed.
The issue with legacy data management systems
Traditional approaches to data integration, which rely heavily on copying and consolidating data into central warehouses or lakehouses are struggling to keep pace with the needs of modern healthcare. While lakehouses promise flexibility, critical data often remains outside the platform across clinical systems and cloud environments which require complex extract, transform and load (ETL) pipelines that increase cost and latency. Data must be ingested and transformed before use, delaying insights and limiting AI effectiveness.
Lakehouses also lack a universal semantic layer, consistent governance across distributed sources and true personalised data self‑service. Logical data management addresses these gaps by unifying data virtually, delivering governed, real‑time access without replication -enabling trusted AI‑driven decisions at the point of care.
Governing data with logical data management
For organisations looking to move beyond static views of data and enable real-time intelligence to achieve continuous operational decision making, logical data management platforms provide a strong alternative to legacy methods and are well suited to deliver.
Rather than physically moving or replicating data from source systems, a logical data layer uses data virtualization technology to create a unified, real-time view of information. This means the data stays in its original location and queries are executed dynamically across multiple sources through an automated, optimised process. Unlike traditional ETL or replication approaches, this method eliminates delays and minimises risks associated with copying sensitive healthcare data. By reducing complexity for users working in highly stressed environments such as healthcare, logical data management makes data readily accessible to clinicians, analysts and AI applications alike.
The efficiency gains enabled by logical data management platforms are also clear. Instead of spending months engineering data pipelines for each new analytics or AI initiative, organisations can rapidly expose trusted, governed datasets for immediate use. Data definitions and policies can then be applied consistently across use cases, reducing confusion and improving confidence in insights. Most importantly, AI tools can operate on up‑to‑date information – supporting decisions that reflect current clinical and operational reality.
A driver for regulated, compliant data
Governance plays an important role as AI regulation continues to evolve. Regulators, including the MHRA, are increasingly focused on transparency and responsible use of AI in healthcare. Logical data management helps organisations meet these expectations by embedding governance directly into the data access layer. It becomes possible to clearly demonstrate where data originates and who has access to it without compromising security or patient privacy.
For leaders, becoming AI ready should be about how data supports better outcomes for patients, clinicians and the organisation as a whole. A resilient data foundation is one that delivers business‑ready data and scales to support future innovation without constant re‑engineering. Logical data management delivers precisely this foundation, enabling healthcare organisations to extract more value from their existing data investments.
An example of successful implementation can be seen in one of Canada’s largest public health service providers which deployed a logical data management layer across its data estate. The platform sat above both the enterprise data warehouse and a new Cerner patient system, unifying transactional and analytical data without replication. This approach accelerated system changes, improved turnaround times by 30–50%, enabled near‑real‑time reporting, strengthened governance and allowed teams to respond rapidly to evolving clinical and operational needs.
Preparing for tomorrow’s AI landscape
As AI adoption accelerates across healthcare, organisations that can access their data in real time, with full confidence, will have a distinct advantage. They are better positioned to respond to regulatory change, introduce new AI‑enabled capabilities and deliver higher‑quality care efficiently. Logical data management platforms turn fragmented healthcare data into a strategic asset. By doing so, healthcare organisations are positioned to be AI ready and deliver the best patient care.
By Errol Rodericks, EMEA & LATAM Product & Solutions Marketing Director, Denodo
