Applying AI in Local Government: Turning potential into practice

AI is already here, even if you don’t see it

AI is following a similar trajectory to cloud computing, but at a far greater pace. Initially seen as a standalone innovation, it is now embedded in tools many councils already use. Enterprise capabilities like Microsoft Copilot and AI-enabled Enterprise Resource Planning (ERP) systems integrate AI into everyday workflows, often without users realising it. This seamless integration means AI is becoming part of the backbone of IT services, rather than a separate entity.

There’s a common fear that AI will lead to widespread job losses. In reality, most current AI applications and use cases still require a ‘human in the loop’, to ensure oversight and accuracy to make confident decisions based on the output. While AI will reshape how services are delivered and influence organisational structures, these changes will be gradual rather than disruptive overnight.

On a technical level, AI represents the natural progression from descriptive and diagnostic analytics toward predictive, prescriptive, and ultimately adaptive intelligence. Where traditional logic based analysis seen in yesterday’s IT systems was largely retrospective, AI introduces a forward-looking dimension through predictive and adaptive capabilities. By harnessing machine learning, natural language processing, and real-time data ingestion, AI transforms static analysis into a dynamic decision engine capable of providing human like reasoning to complex problems.

As AI becomes woven into ERP systems, productivity suites, and IT service frameworks, it ceases to be a standalone technology and instead forms a key foundation for digital operations. While automation accelerates, the ‘human-in-the-loop’ becomes key to providing governance, ethical oversight, and contextual judgment to ensure that AI-driven outcomes translate into confident, accountable decisions. The result is not overnight disruption but a gradual reconfiguration of workflows, where intelligence is integrated and adaptive rather than isolated and reactive.

Where councils are already seeing the impact:

Local authorities are beginning to explore AI in targeted areas, including:

• Public ticketing systems: Councils are using AI to automate ticketing processes for services like parking enforcement. This reduces manual workload, speeds up response times, and improves accuracy in issuing and tracking tickets.
• Predictive maintenance: AI-powered sensors and image recognition can detect early signs of road damage, such as cracks or potholes, before they become costly repairs.
• Social services: AI can analyse data from multiple sources to identify early indicators of homelessness or vulnerability. By spotting patterns, such as missed rent payments or frequent service requests, councils can intervene earlier.
• Housing services: Routine queries about housing applications, repairs, and eligibility can be automated through AI-driven chatbots and virtual assistants. This frees up staff time for complex cases and improves the speed and consistency of responses for residents.
• Waste collection: Multiple London boroughs implemented AI-powered smart bins with fill-level sensors and image recognition. These systems alert collectors only when bins are full, optimising collection routes, cutting waste collection costs. 

Making AI work for local government: Challenges and opportunities

The greatest opportunity lies in task automation - streamlining transactional, routine processes so staff can focus on strategic decision-making. However, councils face challenges in prioritising investments, given limited budgets. Governance and risk management are critical, especially as AI capabilities become embedded in enterprise application.

Yet, the effectiveness of these AI tools hinges on one foundational element: data quality. Poor data governance and inconsistent data hygiene can undermine even the most advanced AI systems, leading to biased outputs, inaccurate predictions, and eroded public trust. Local authorities often manage vast, fragmented datasets across multiple legacy systems, making it difficult to maintain a single source of truth. Without robust data standards, cleansing protocols, and accountability frameworks, automation risks amplifying existing inefficiencies rather than solving them.

AI governance is fundamentally a business risk. Councils therefore need to establish guardrails to ensure ethical use, maintain transparency, and manage risks associated with autonomous decision-making. Upskilling the workforce is equally vital to ensure staff can leverage AI effectively without compromising service quality.

Looking ahead

Agentic AI, systems capable of making decisions and executing workflows with minimal human intervention, is on the horizon. While not yet mainstream, its adoption could redefine how councils operate. For now, the focus should remain on building foundational capabilities, experimenting with practical use cases, and embedding governance frameworks.

Successful AI adoption hinges on a strong governance framework that balances innovation with compliance and ethics. The Forvis Mazars AI Governance Framework stands out by aligning with global standards like the EU AI Act and ISO 42001, offering a risk-based approach that addresses bias, privacy, transparency, and accountability. Covering key domains such as AI lifecycle management, security, data quality, and stakeholder engagement, it ensures responsible practices while remaining flexible to evolving regulations. By embedding AI strategy into business objectives and fostering human oversight, this framework enables organisations to harness AI’s transformative potential confidently and responsibly.

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