- AI tool predicts NHS staff resignations using workforce models and data
- Royal Berkshire NHS wins award for innovative employee retention technology
- New model explains reasons for possible staff departures before decisions are made
An AI forecasting tool built for the Royal Berkshire NHS Foundation Trust in the UK has been credited with predicting staff resignations before they happen.
The project, developed in collaboration with the University of Reading, uses workforce data to identify what drives employees to make the decision to leave.
It won the Aiconics AI Enterprise Business of the Year award at the 2026 National AI Awards, after judges weighed in on its real-world application.
AI model explores workforce patterns behind possible departures
The system was designed to alert managers earlier to retention issues within a workforce of around 7,500 NHS staff.
Unlike the Trust’s old reactive process, this model actually explains the reasoning behind each prediction, rather than just spitting out an outcome.
“This award reflects what is possible when academic expertise in AI and forecasting is applied directly to a real-world problem facing the NHS,” said Shixuan Wang, professor at the University of Reading.
The model identifies specific factors linked to resignation risk, so HR teams can truly understand why a prediction was made instead of treating it as a mystery.
The initiative links directly to the NHS’s workforce targets, tackling staff turnover, reducing disruption and looking at ways to retain more staff.
It brings together academic research and operational health care data, which has not been simple, and questions remain about their ability to scale or hold up over time.
The Royal Berkshire NHS Foundation Trust provides acute and specialist care across Berkshire, serving around one million people through its hospitals and services.
Before this, the Trust relied on reactive reporting, meaning managers were often only informed of a retention issue once someone had already decided to stand down.
Researchers used data analysis to create an AI tool that supports workforce planning while leaving the final say to human decision-makers.
Throughout development, the team paid close attention to combining operational know-how and academic rigor, without losing sight of the responsible use of AI in the healthcare environment.
Recognition comes as organizations explore predictive AI systems
“The entries for the National AI Awards 2026 were extremely impressive, with companies spanning a wide range of sectors and innovations,” said Fergus Bruce, CEO of the National AI Awards.
The organization said this year’s entries showed measurable value, responsible innovation and truly practical results across different sectors.
As LLMs are increasingly found in workforce management, interest in predictive tools for organizational decisions continues to grow.
People from different backgrounds shaped this project, spanning data analysis, strategic human resources research and healthcare workforce operations.
The forecasting tool is intended to give managers more work opportunities, not replace them, since employment decisions are still a matter of human judgment.
Whether tools like this will spread more widely will depend on accuracy, trust, privacy concerns, and whether they actually provide useful results.
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