With ever-increasing demands on limited resources, local councils need to find efficiencies wherever possible. The UK’s Local Government Reorganisation (LGR) is one of the most visible examples of central government directing its local counterparts to simplify service delivery for residents, with cost saving efficiencies as one of the key aims. But seizing this once-in-a-generation realignment multiplies the opportunities it offers. As systems, processes and data are aligned, there is space to not only ask what best practice looks like now, but also what should it look like in the future.

The Data Age is characterised by global corporations leveraging data to increase margins and maximise profits; with UK local governments having access to large amounts of valuable data from their communities, data must now be seen as the enabler of AI and automation. By using data to drive significant efficiency savings and freeing up staff to focus on frontline work, local government can align with the UK Central Government’s “Blueprint for a Modern Digital Government”.

Newly amalgamated local authorities could be forgiven for thinking they could shortcut straight to an AI-led future. But this would be a false economy, even if pre-combined authorities were already following best practice. As we discussed previously, to be truly useful, data should be governed like a physical asset – cleansed and stored in an organised, well governed warehouse where it’s easily accessible to those who need it. As well as ensuring data is secure, of high quality and meets regulatory standards, this best practice will provide a firm foundation for growth and adaption in a fast-paced digital environment.

Moving from analytics to AI

One of the original benefits of a data warehouse was that it simplified building and issuing high quality dashboards and reporting packs, giving business SMEs access to business intelligence tools to create rich, interactive dashboards at the point they are needed. But whilst dashboards created from enterprise-wide data are important, they’re not future technologies. As councils are merged, completing an evaluation of needs and producing dashboards that use data from the newly combined sources should be completed at the earliest opportunity. Because without access to up-to-date data, teams will fall behind modern standards.

Indeed, one of the stated aims of the current government is to “Harness the power of AI for public good”. For many areas of local government, this has meant starting to implement generative AI assistants, such as Microsoft Copilot. Meanwhile, others have gone further by creating bots or agents that excel at specific tasks. For example, some AI assistants can act as an experienced Social Care worker, trained on the departmental book of knowledge, and able to interact conversationally with more junior staff when they have role-related questions.

But whilst AI may be largely interchangeable with Gen AI in the public consciousness, there are many other AI capabilities being introduced at local government level. Useful applications include water level sensor information being analysed by machine learning models alongside historical data in order to predict the risk of flash floods; computer vision tools being used in social care to alert staff when care home residents are in a ‘pre-fall’ state, allowing them to quickly intervene; and drones being used to inspect buildings for structural defects, reducing the workload of planning and enforcement authorities.

The importance of human oversight in AI

While these examples are not yet widely implemented across local government authorities, they demonstrate that AI – in many forms – is already being trialled on real-world situations, creating genuine benefits for residents. To scale these ideas, we must not understate the importance of ensuring the foundations of data governance and trusted, accessible data. The bar for AI services to gain public trust is (understandably) higher for public entities than it would be for a private business, with a greater focus on outputs being morally fair.
AI ethics is a key component of responsible AI governance that every organisation implementing AI must consider. Some aspects mirror good data governance, such as ensuring data privacy and protection. But others focus on aspects of reliability, repeatability and explainability. For instance, if a member of staff was to make a planning decision, that decision should be consistent with other similar decisions, the same if the application was re-submitted without alteration and completely explainable. Any bot created to complete the same process should be held to the same exacting standards.

One solution is to ensure that a human remains in the loop to review or approve all key decisions and outputs made by AI. But wherever the human and AI interact within the system – be that as a reviewer or someone who interacts with the outputs – training and support is vital. Council staff need to understand the data, systems and outputs they are interacting with if they are to fully leverage them. Ensuring comprehensive guidelines and support is even more important in the public sector, where the average age of employees is older than the private sector and there are fewer digital natives adept at using AI tools.

Integrating AI for public good

The LGR is creating change across multiple levels of administration in all areas of the country. But at the dawn of the AI age, this presents an opportunity for those with strong data foundations to build community focused services for the future. At RSM, we have a full spectrum of experience in digital transformation to support you in building AI solutions for the public good. If you’d like to discuss how we can assist you in building AI agents or advanced predictive models to support key front-line workers, or to guide you through implementing a best practice AI Governance framework, please speak to Data, Analytics and Insights Partner Sarah Belsham, or Advanced Analytics Director Shakie Kawuyu.

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