AWS re:Invent 2025 - AI, Cloud & Public Sector Transformation: PwC & AWS Driving Change Now (AIM115)
Source: Dev.to
Overview
AWS re:Invent 2025 – AI, Cloud & Public Sector Transformation: PwC & AWS Driving Change Now (AIM115)
In this session Catherine (PwC UK), Andy (PwC UK – Local Public Sector) and Nicola (NHS Medway Hospital) discuss how they used AWS services—including Amazon Connect, Amazon Lex, AWS Lambda and Amazon Bedrock—to modernise healthcare contact centres. Key outcomes highlighted:
- Reduced DNA (Did‑Not‑Attend) rates by 4 %
- Automated 80 % of 110 000 annual calls within six weeks
- Saved tens of thousands of patient minutes each month
Andy also shared lessons from local‑government deployments, achieving 55 % call automation in Birmingham with 80 % customer satisfaction. The solution now offers 24/7 multilingual support for diverse patient populations. Future plans include extending the approach to cancer services, prison healthcare, HR/finance automation, pre‑operative questionnaires, and exploring voice‑to‑voice AI and agentic interfaces.
This article is auto‑generated from the original presentation. Typos or minor inaccuracies may be present.
Transforming UK Public Services: Lessons from Local‑Government Success with Amazon Connect
Catherine, a PwC director on the health team, introduces the discussion with Andy (local‑public‑sector lead) and Nicola (NHS Medway). They note that the NHS is Europe’s largest employer, yet faces waiting lists for ~10 % of the population and a legacy‑heavy technology landscape with limited digital, AI, and cloud adoption.
Key Learnings from Local‑Government Deployments
- Speed of Delivery – Amazon Connect can be deployed for a full contact‑centre migration in 10‑15 days, releasing ≈30 % of capacity within 10 weeks.
- Reusable Assets – Once built, the solution components become repeatable assets, reducing effort for new industries.
- Rapid Modernisation – Councils face tight budgets and rising citizen expectations; the ability to “fail fast” and iterate encourages adoption.
These principles were transferred to the health sector to accelerate transformation.
Addressing NHS Medway’s Contact‑Centre Crisis
Watch the video segment on YouTube (starting at 3 min 20 s)
Nicola describes the challenges faced by NHS Medway, a highly deprived area with:
- ≈120 000 annual inbound calls, half of which went unanswered.
- Long wait times (up to a year) for routine appointments.
- A low average reading age (≈6 years) among the patient population, limiting self‑service.
- Staff exposed to high volumes of angry callers, affecting morale.
Problem Framing
Rather than asking “Can AI fix this?”, the team asked “Where are our pain points and how can AI support them?”. They found that ≈80 % of calls were simple, self‑serviceable queries (e.g., “Where do I park my car?”).
Outcomes & Benefits
- Automation of high‑volume, low‑complexity interactions reduced unanswered calls and freed staff for more complex cases.
- Multilingual, 24/7 support improved accessibility for diverse patient groups.
- Iterative improvement with staff engagement ensured the solution matched clinical language and intent.
Future Roadmap
- Expand automation to cancer services, prison healthcare, HR/finance processes, and pre‑operative questionnaires.
- Develop voice‑to‑voice AI and more advanced agentic interfaces for richer patient interactions.
- Continue staff training and governance to sustain rapid deployment and continuous improvement.