Implementing AI-driven automation to improve a Government service

Project overview

Our consultancy partnered with a central government department to address a long-standing challenge: a critical citizen-facing service was heavily reliant on manual case processing. While effective in principle, the service was resource-intensive, slow, and vulnerable to human error. The department wanted to explore whether artificial intelligence (AI) could streamline routine tasks without compromising the fairness, accuracy, or accessibility that citizens expect from public services.

The project ran over a 12-month period and covered the full lifecycle of delivery, from initial discovery and service mapping through to the design, build, and implementation of an AI-driven automation solution. Our consultancy provided a multidisciplinary team — business analysts, service designers, data specialists, and delivery leads — working hand in hand with the department’s own teams. Together, we aimed to demonstrate how responsible AI could reduce cost and turnaround time, while improving both user and staff experience.

The problem

The department’s service handled thousands of requests each week. Each submission required manual review by a caseworker, involving repetitive checks, data entry, and cross-referencing with internal systems. As demand increased, the process became unsustainable. Citizens faced long waiting times, staff morale suffered under the weight of repetitive work, and the department was spending an increasing share of its budget on temporary staff to manage backlogs.

At its heart, the problem was not simply inefficiency. Long processing times created frustration and uncertainty for citizens who depended on timely outcomes. From a government perspective, the service carried operational risks: backlogs threatened compliance with statutory response times, while inconsistent manual checks created the potential for errors. There was also a strategic imperative to modernise, as ministers had made efficiency and digital transformation key priorities.

Any solution had to balance three objectives: reduce the administrative burden on staff, deliver faster outcomes for citizens, and maintain — if not enhance — the quality and fairness of decision-making.

"The team felt like an extension of our own. They quickly understood our service and worked alongside us rather than over us, which made collaboration seamless.”"

Service Manager

Research and discovery

We began with an in-depth discovery phase, mapping the end-to-end service journey to understand where automation might add value. This included interviews with caseworkers, workshops with operational managers, and focus groups with citizens who had used the service. We complemented this qualitative insight with quantitative analysis of processing data, identifying where the greatest bottlenecks occurred.

One key finding was that nearly 60% of caseworker time was spent on routine checks that required no discretion or judgement. These tasks were essential but repetitive, leaving less time for the nuanced, human-centred parts of the service where staff expertise really mattered. We also learned that citizens valued transparency above all else. They were less concerned about how their request was processed, and more concerned about being kept informed and receiving clear explanations for outcomes.

This evidence gave us confidence to explore AI automation for the highly structured, rules-based tasks, while reserving complex cases for human review. It also highlighted the need to design safeguards to ensure fairness, accountability, and explainability of any AI-supported decision.

"What impressed me most was how much effort went into knowledge transfer. By the end of the project, our team felt confident maintaining and evolving the solution independently.” 

Operational Lead

Design approach

Our consultancy worked with the department to co-design an AI-driven automation solution that was practical, ethical, and aligned with government standards. We used agile delivery methods, iterating through prototypes and testing them with real data in a controlled environment.

The solution integrated natural language processing and machine learning models to extract key information from submissions, automatically validate it against internal records, and flag anomalies for human review. Importantly, we built the system around a “human-in-the-loop” design, ensuring staff retained oversight and the final say on complex or ambiguous cases.

Collaboration was central to our approach. Business analysts worked closely with policy and legal teams to translate legislation into clear business rules. Service designers created user journeys that showed how citizens and staff would interact with the new system, ensuring that transparency and accessibility were maintained. Data scientists developed and trained the AI models, while our delivery managers ensured that progress was tracked, risks managed, and dependencies across teams coordinated.

We placed strong emphasis on explainability, developing user-friendly dashboards that showed why the AI had made certain recommendations. This not only built trust with staff but also created a foundation for accountability and future audit.

Outcome and impact

The new AI-driven automation solution transformed the service. Routine checks that once took caseworkers several hours could now be completed in minutes. Average processing time per case dropped by 40%, enabling the department to clear its backlog and meet statutory deadlines consistently for the first time in years.

Financially, the department reduced operational costs by an estimated 25%, primarily through a decrease in temporary staffing requirements. Just as importantly, staff reported higher job satisfaction, as they were freed from repetitive tasks and able to focus on the aspects of their role that required judgement, empathy, and expertise.

For citizens, the benefits were immediate. Waiting times were cut significantly, and the improved transparency features meant that users received more frequent updates and clearer explanations of outcomes. User satisfaction scores increased by 18% in the months following implementation.

The project also created a reusable framework for responsible AI adoption within government, setting a precedent for how automation can be introduced in a way that balances efficiency with accountability.

"They balanced technical expertise with clear communication. They made sure our staff understood not just what was built, but why — leaving us stronger as a service team.” 

Head of Digital Delivery

Reflection

This project demonstrated that AI in government is not about replacing people but enabling them to focus on what matters most. By combining rigorous discovery with a human-in-the-loop design, we showed that it is possible to deliver measurable improvements in speed and cost while maintaining fairness, trust, and transparency.

What made this engagement unique was the depth of partnership. Our consultancy worked side by side with the department’s own teams, building internal capability so that the solution could be owned and sustained long after our involvement ended. We learned that open dialogue, clear ethical frameworks, and iterative testing are critical when introducing AI into public services.

As government continues to face growing demand and fiscal pressures, projects like this show how AI can be part of the solution — not as a replacement for human service, but as a tool that enhances it. We are proud to have supported this department in taking a major step forward and look forward to bringing the same approach to other areas of public sector digital delivery.