AI maturity in the public sector

Artificial intelligence (AI) and unstructured data are reshaping industries by providing new opportunities for efficiency and growth. In the public sector, AI and unstructured data go hand in hand, with many already making use of the combination. However, few have concrete processes in place to unlock the true potential. Shifting the focus from standalone AI capabilities to AI with unstructured data will allow for improved efficiency, strengthened decision-making, and a greater value to customers. With the growing push from government initiatives, the ability to make this transformation is gaining momentum within organisations.

Findings are based on research conducted by Iron Mountain alongside independent market research specialist Vanson Bourne. Data in this report is based on 212 IT and data decision-makers in the public sector excluding healthcare, who have knowledge or involvement in their AI strategy. You can read the global report

Three key takeaways:

• The public sector is turning to AI to reduce costs and overhead (61%)
• Fewer public sector organisations are more advanced with their AI maturity when compared to the global average (35% vs a 40% global average), with more than half looking to scale IT capabilities to see improved AI value
• Unstructured data is widely used within the sector, yet improvements need to be made for organisations to experience its true value
  • 80% often leverage unstructured through AI tools (compared to a 74% global average)
  • But 74% report they’re not highly effective at making their unstructured data trustworthy for AI applications

Iron Mountain commissioned independent market research specialist Vanson Bourne to conduct this piece of research. The study included surveying 1,400 IT and data decision-makers who have knowledge of or responsibility for AI strategy at their organisation. Respondents’ organisations had to have 250 employees or more across the following countries: US, UK, France, Germany, India and Australia.

Organisations are from several public and private sectors but there was a strong focus in banking and financial services, insurance, healthcare and life sciences, media and entertainment, the public sector (excluding healthcare) and energy. This summary is based off 212 decision-makers in the public sector.

AI can help reduce costs and overhead within the public sector

AI is continually evolving, creating new tools and capabilities that can help revolutionise the way organisations function. For many in the public sector, using AI will be key in helping overcome some of the wider sector challenges as it can be implemented across a variety of operational areas. Our research found that AI’s main use case in the sector is actually cost reduction via reducing overhead or time spent on non-revenue generating work (61%).

For the public sector, AI is most commonly deployed across IT and security, research and development, and finance use cases).

Moreover, the public sector is one of the most likely sectors to be deploying AI across research and development areas. It’s likely the influx of resources from government plans prioritising these areas such as The National Artificial Intelligence Initiative Act of 2020 in the US, or the Digital Europe Programme in the European Union, is a contributing factor in this growing focus.

On the other side of the spectrum, there’s lower-than-average integration of AI across customer service (53% and 47%) and customer-facing products/solutions (47% and 43%) than other global organisations. However, that doesn’t mean that AI isn’t being used more broadly to improve customer experience within the sector. In fact, more than the global average (public sector: 58% vs global average: 52%) strongly agree that their organisation effectively uses AI-powered agents to streamline operations and improve customer experience. Given AI’s potential to optimise processes, personalise interactions, and reduce response times, it makes sense that the public sector organisations prioritise these areas to improve citizen engagement and service delivery. However, it does raise the question – why is AI not used specifically within customer service areas, when customer experience is clearly an important outcome?

This discrepancy may stem from the unique challenges faced by public sector organisations when implementing AI in direct customer-facing roles. Strict regulations around data privacy, ethical concerns, and the need to maintain public trust can slow the adoption of AI in sensitive customer service interactions. Additionally, legacy systems and resource constraints may limit the integration of advanced AI tools within frontline services. However, as more organisations recognise the benefits of AI powered agents in enhancing responsiveness and efficiency, there is an opportunity to extend these capabilities into dedicated customer service functions, improving both operational performance and citizen satisfaction.