Preparing Government for AI Adoption Responsibly and Effectively

AI promises great improvements in public services, boosting efficiency and outcomes for citizens. However, to truly use AI, governments need strong data systems and management. Iron Mountain's research in partnership with the Financial Times shows that the public sector is very interested in AI for saving money through automation and better resource use.

Yet, a big problem remains: dealing with disorganized data. A significant 74% of respondents in our study said they weren't good at making this data useful for AI. This is where the real effort starts, and it highlights the crucial need for strong IT and data management to ensure AI succeeds.

Good data systems are the technological foundation for this goal. They include flexible setups to handle large amounts of organized and disorganized data, integrated systems that connect different data sources for a complete view, and automated tools for getting and cleaning data. Importantly, they must also include built-in privacy and security like access controls and encryption.

Alongside this is good data management-the human side that defines the rules, roles, and processes for responsible data handling. This means clear roles for data owners and managers, defined policies for data quality and privacy, and a shared responsibility for data accuracy throughout the organization.

Getting ready for AI has its challenges. Organizations often mistakenly see data management as just a compliance task instead of a strategic tool for creating value. Letting data stay separate, ignoring data quality, and forgetting the human aspect-the need for a data-aware culture and clear roles-are common errors. Furthermore, a lack of leadership support can stop even the best data projects.

The keys to success

To avoid these issues, the public sector must adopt responsible, structured data management as a key part of their AI oversight, focusing on cleaning up unnecessary data, tracking data history for clarity, and using "nutrition labels" for AI models to build confidence.

When navigating the vendor landscape, governments must prioritize problem-solving over product-pushing, working with suppliers as true partners. Transparency is also key: demand that suppliers demonstrate how their AI works, how it was trained, and how it addresses bias, ensuring robust contractual terms for risks and liabilities.

Critically, governments must "fix their data first"-no AI works effectively without high-quality data. Finally, to maintain flexibility and control, avoid vendor lock-in by utilizing open standards in contracts, allowing for adaptability as technology evolves. By focusing on these principles, governments can build trusted, effective partnerships that pave the way for responsible and impactful AI adoption.