Cost Optimisation in the NHS: How AI, Agentic AI and AI Agents Are Driving Change

In the face of growing demand, rising costs and constrained resources, the National Health Service (NHS) in England is under acute pressure to deliver more with less. Cost optimisation is no longer a choice but a necessity. Fortunately, the rise of artificial intelligence in healthcare offers new pathways for sustainable efficiency gains. The emergence of agentic AI and AI Agents, which act autonomously, initiate workflows and interact intelligently with humans and data, represents a major step forward for the NHS digital transform

The cost challenge in the NHS

The NHS faces significant financial and operational challenges. These include ageing populations, complex chronic diseases, workforce shortages, outdated IT infrastructure and heavy administrative burdens.

A briefing from the Royal College of Radiologists states that “AI, the greatest value to the NHS lies in automating or augmenting administrative and organisational tasks, thereby improving patient pathways” because clinicians currently spend too much time on non-clinical work.
rcr.ac.uk

One pilot deployed by NHS England found that AI-driven appointment management software at one trust reduced did-not-attend (DNA) rates by around 30 percent and generated estimated savings of £27.5 million per year for that trust alone.
NHS England

A recent trial of a productivity tool, Microsoft 365 Copilot, across 90 NHS organisations found that staff saved around 43 minutes per day on routine tasks. This equates to around 400,000 hours every month and “millions of pounds every year” in potential cost savings.
GOV.UK

These results demonstrate the scale of opportunity. Cost optimisation enabled by technology is central to the NHS AI roadmap.

The role of AI in NHS cost optimisation

Artificial intelligence in healthcare delivers value in two broad ways: operational efficiency and clinical effectiveness.

The NHS highlights that “AI has the potential to give health and social care practitioners back time to care by removing time-consuming repetitive tasks” and that AI can “decrease costs”.
digital-transformation.hee.nhs.uk

Operational efficiency

• Automation of back-office functions including finance, scheduling and data subject access requests (DSARs).
  One vendor estimated the cost of manually processing a DSAR in the NHS at around £1,258. An AI-powered redaction workflow reduced the cost per case to around £17.
  Microsoft
• Reduction in missed appointments (DNAs) which frees up capacity and reduces waste.
  NHS England
• Optimisation of resource allocation, staff rotas and patient pathways.
  The Royal College of Radiologists notes that “AI will add the greatest value by augmenting or replacing existing administrative and organisational tasks.”

Clinical effectiveness

• Improved diagnostics, reduced delays and optimised treatment pathways. These changes reduce complications, shorten hospital stays and ensure efficient use of high-cost clinical equipment.
  LSE Blogs
• Better patient experience and outcomes, fewer readmissions and more effective prevention efforts.

In short, intelligent automation supports both cost reduction and enhanced clinical value.

Agentic AI and AI Agents: the next frontier

While many NHS AI deployments currently focus on narrow tasks such as predicting DNAs or automating form completion, the next phase of transformation is centred on agentic AI and AI Agents. These systems can initiate actions, collaborate across multiple data sources, adapt in real time and manage workflows from start to finish.

In practice, this can include:

• AI Agents that detect an incoming referral, check relevant history, suggest optimal scheduling, allocate the correct slot, flag potential DNAs and trigger reminders or alternative options.
• Agentic AI that forecasts demand for elective surgery, diagnostics or outpatient capacity, then reallocates staff and equipment dynamically to minimise idle resources.
• Generative AI tools that draft clinical letters, summarise consultations, prepare care pathways and communicate with staff and patients, reducing administrative burdens and accelerating time to care.

These capabilities make it possible to shift from reactive cost management to proactive cost optimisation and value creation across the NHS.

Strategy, governance and implementation

To harness AI effectively, several strategic elements are required:

• AI strategy alignment
  AI should support the NHS 10-Year Health Plan and broader digital transformation objectives. It must improve patient experience and workforce efficiency rather than exist as standalone automation.
• Data-driven healthcare
  High-quality data, interoperability and secure information sharing are critical foundations for reliable agentic systems.
• AI governance and ethics
  Systems must be transparent, explainable, fair and aligned with NHS principles. Patient and staff trust is fundamental.
• Change management
  Technology alone does not create transformation. Staff training, workflow redesign and cultural alignment are essential.
• Measurement and outcomes
  Clear KPIs such as reduced DNAs, improved utilisation of outpatient capacity and better patient outcomes are required to measure value.
• Scalable deployment
  The NHS has many successful AI pilots. The challenge is scaling these solutions across the system in a consistent and cost-efficient way.

Impact on patient experience and frontline care

Some worry that cost optimisation might reduce the quality of care. In reality, when AI is applied strategically, it improves both efficiency and patient experience.

Administrative workloads are reduced, allowing staff to focus more on clinical activities. Optimised pathways shorten waiting times. Predictive models improve flow, reduce DNAs and ensure that patients receive care at the right time.

Agentic AI that connects data across care pathways and triggers timely interventions strengthens both experience and outcomes. The ideal scenario, better care at lower cost, becomes achievable.

Real-world evidence: cost and time savings

• The Microsoft 365 Copilot trial across the NHS resulted in an average of 43 minutes saved per staff member per day. This equates to around 400,000 hours per month.
  GOV.UK
• A DNA reduction pilot at a trust delivered a 30 percent reduction in missed appointments with potential savings of £27.5 million per year.
  NHS England
• DSAR processing costs dropped from around £1,258 manually to around £17 using an AI-powered workflow.
  Microsoft

These examples demonstrate the scale at which AI and agentic AI can drive cost optimisation.

Challenges and caveats

AI offers significant value, but there are challenges:

• Legacy IT systems.
• Fragmented data and limited interoperability.
• Ethical, regulatory and governance issues.
• Workforce implications that require training, reskilling and role redesign.
• Difficulties measuring value when scaling from pilot environments to full deployment.

With strong governance, strategy and leadership, these issues can be mitigated.

Conclusion: the path ahead

Cost optimisation in the NHS is a strategic priority. AI, including agentic AI and AI Agents, is emerging as a critical enabler of that transformation. By automating repetitive tasks, optimising resources, improving patient pathways and freeing clinicians to focus on care, the NHS can achieve significant efficiency gains without compromising quality.

Success requires alignment with digital transformation goals, investment in data, strong governance, meaningful measurement and a culture of adoption. The journey from pilot to system-wide deployment will not be easy. However, the evidence is clear. Artificial intelligence in healthcare has the potential to deliver substantial cost savings and improved outcomes.

Now is the time for NHS leaders to move beyond experimentation and place AI, AI Agents and agentic AI at the centre of the next era of healthcare innovation and value-based care.