The Use of Artificial Intelligence in Government

The recent DigiGov Expo 2024 was a resounding success, bringing together leading experts in the public sector tech industry. With discussions ranging from future tech trends to citizen experience, the event provided invaluable insights into the public sector landscape. That being said, it would be a shame not to revisit what was discussed! 

Artificial intelligence (AI) in government is a topic that continues to gain momentum, and recent discussions at the DigiGov Expo shone a spotlight on the exciting yet complex landscape of AI in public services.  National Audit Office (NAO) tackled the topic head-on in their presentation ‘The Use of Artificial Intelligence in Government’. 

AI is already proving its potential in various areas, from automating repetitive tasks to transforming public services. But while governments are excited about what AI can do, it’s clear that scaling up its use across departments presents several hurdles that need to be overcome.

Since around 2018, AI has started finding its way into government operations, initially in small-scale pilots involving predictive technologies and data analytics. As the speaker pointed out, tools like predictive analytics are already helping governments forecast trends in key sectors such as healthcare and education. These models analyse vast data sets to inform decision-making, providing insights that would have taken much longer to gather manually. In addition, AI-driven automation has started reducing the manual burden on public servants, taking over mundane tasks like document processing and answering simple queries. 

Despite these promising developments, most AI initiatives in government are still at the pilot stage. Scaling these projects to cover entire departments, or the government as a whole, is where things start to get tricky. There are multiple reasons for this, and they were highlighted with great detail during the presentation. 

One major obstacle is the skills gap. Governments simply do not have enough personnel with the technical expertise required to implement and manage AI effectively. This gap isn’t just about coding and data science; it’s also about lacking business skills, particularly the kind that helps navigate complex AI projects within a large, often bureaucratic system. Government officials need to not only grasp the technology itself but also have the foresight to integrate AI into existing processes, many of which are governed by strict regulations. 

Another considerable challenge is the prevalence of outdated legacy systems. Governments are notorious for clinging to old technology, and AI demands and infrastructure that these legacy systems often cannot provide. Outdated infrastructure means that even if departments want to deploy cutting-edge AI solutions, their existing tech framework simply won’t allow for it without massive, costly overhauls. This rigidity makes integrating new AI tools a challenge, as the underlying systems they depend on are ill-equipped to support them. 

Data quality is another essential factor in the success of AI, and governments have plenty of it—but it’s often in poor shape. Much of the data is spread across various departments, inconsistently formatted, or difficult to access. AI needs well-structured, high-quality data to function correctly, and the scattered, inconsistent nature of government data poses a significant roadblock. Without quality data, AI cannot generate accurate predictions, nor can it deliver the efficiency gains that have been promised. 

Another fascinating point raised during the presentation was the environmental impact of AI. As AI systems become more complex and powerful, they require vast amounts of computational power, which in turn consumes a significant amount of energy. This raises important questions about the sustainability of AI in government. How does its increasing use fit with government initiatives around climate change and reducing carbon footprints? While AI promises operational efficiency, it comes with its own environmental considerations, and the balance between innovation and sustainability will be a key discussion in the years ahead. 

Despite these hurdles, the future of AI in government is full of potential. To overcome these barriers, several crucial steps must be taken. One of the most important is investing in skills—not just technical ones, but also managerial and strategic skills. AI literacy must become more widespread within government departments to ensure that employees at all levels can understand and harness the power of these technologies. Another vital step is the modernisation of legacy systems. While this is an expensive and time-consuming process, it’s essential if AI is to be fully embraced across departments. 

Improving data management is equally crucial. Governments need to clean up, standardise, and organise their data so that AI models can be trained effectively. This involves not just technical adjustments but also cross-departmental cooperation to ensure that data is accessible, usable, and high-quality. 

Finally, the presentation made it clear that government efforts would benefit from a more centralised approach to AI. Right now, individual departments are experimenting with AI in isolation. Without a coordinated, overarching strategy, these initiatives risk becoming fragmented and inefficient. A unified approach would allow for better collaboration between departments, the sharing of best practices, and a streamlined process for rolling out AI solutions across government. 

In conclusion, the use of AI in government is still in its infancy, but it is undoubtedly a technology that holds immense promise. The key lies in addressing the practical challenges: modernising systems, building skills, improving data quality, and developing a cohesive strategy. If governments can tackle these hurdles, AI could revolutionise how public services are delivered, bringing about a new era of efficiency, transparency, and innovation. 

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