The NHS is entering what one of its senior technology leads calls a defining moment for digital quality and interoperability.
As healthcare and insurance systems strain under rising demand, workflow pressures and widening inequalities, the UK’s largest public health provider is accelerating its push toward agentic AI, federated data, and automated assurance frameworks.
In a wide-ranging conversation following this week’s QA Financial Healthcare & Insurance Forum London 2025, one the event’s speakers, Kartik Taneja, Business Intelligence and Automation Lead at the NHS, outlined the organisation’s strategic shift.
As Taneja put it: “The simple truth is NHS is at its crossroads. It is facing rising demand, workflow pressure and widening health inequalities.”
He pointed to the recently published Fit for the Future health plan, describing a moment where “incremental change is no longer enough. We need radical transformation and technology is at the heart of this journey.”
For QA and testing teams, that shift signals a profound change: AI-driven systems will increasingly underpin diagnostics, operations, monitoring, and patient-facing services.
Taneja described agentic AI, autonomous systems capable of sensing, deciding and acting across complex workflows, as central to that transformation.
“We need to move from a system that is often fragmented and reactive to one that is seamless, proactive, and centered around the patient,” he explained.
“That’s where the role of agentic AI comes to the fore… where processes get automated, diagnostics are done in a proactive manner and care plans are dished out using agentic AI coordinating the entire care delivery system.”
Breaking healthcare’s data silos
At the core of this shift is a federated data model spanning the NHS and its ecosystem of private-sector partners. As Taneja emphasised, “a federated data platform is a prerequisite to roll out the agentic AI for a simple reason that if we don’t have joint up information, the agents working on that data will be handicapped.”
He outlined four pillars required to enable this interoperability: unified data standards; embedded AI in clinical and operational roles; patient-empowerment tools; and a stronger public-private innovation model.
“We work with tech company, researchers, innovators, while upholding NHS values,” he shared.
For QA teams, particularly those working on test automation, data assurance and system integration, this architecture represents both a technical and cultural shift.
WATCH OUR RECENT PODCAST WITH KARTIK TANEJA HERE
Deploying agentic AI safely inside a system as vast and heterogeneous as the NHS is no small undertaking. “This is a big step change for NHS and NHS being a large organisation, it’s at various levels of maturity in terms of data and systems underlying them,” Taneja cautioned.
He noted that existing frameworks such as TOGAF provide a structured approach, but real progress requires coordinated experimentation.
“I would personally suggest something like a strategic business unit sort of structure within our local ICSs… piloting out these solutions, taking up the use cases… and then rolling them out and learning from them in a sort of a self-generating cycle.”
For QA leaders, this amounts to a clear invitation: testing teams must be deeply embedded in shaping, validating and scaling these agentic-AI-driven workflows.
Connected care
Looking ahead, Taneja framed AI as a foundational enabler of the NHS Long Term Plan. “AI becomes an enabler for the entire vision,” he said, pointing to areas such as “clinical decision support, operational efficiency, patient engagement, remote monitoring, population health, quality and safety.”
His vision is one where local use cases build toward national capability—measured, iterative, and aligned with public-service values.
“It is implemented through real world use cases… and measure their success overall in alignment with the vision laid out in the long term plan,” he said.
As QA, automation and testing specialists across healthcare and insurance prepare for the next technology wave, Taneja’s message is clear: the raw capability of agentic AI matters far less than the quality, assurance and interoperability frameworks wrapped around it.
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