Preparing Government Data for AI

Challenge

A public sector customer wanted to explore artificial intelligence, but its information was spread across multiple repositories and business systems. Teams had useful data, yet they did not always have a clear view of ownership, quality, sensitivity, or the rules governing how information could be used.

The customer needed a realistic path to AI. It did not need a large demonstration that could not move into production. It needed trusted data, clear controls, and a small set of use cases tied to mission needs.

Solution

We worked with stakeholders to identify the questions they wanted AI to help answer. That kept the effort focused on business value rather than technology alone. We then reviewed the data needed for those use cases and assessed quality, access, security, and governance requirements.

Our team helped organize metadata and define a clearer view of trusted data sources. We considered how secure retrieval could allow an AI solution to use approved information without exposing unrelated or sensitive content.

The approach was intentionally phased. The customer could begin with a controlled use case, measure the results, improve the data, and expand only after the foundation was proven.

Outcomes

• Connected AI planning to clear mission and business needs
• Improved understanding of trusted and approved data sources
• Strengthened governance before introducing new AI capabilities
• Reduced risk through a focused and controlled starting point
• Created a repeatable path for evaluating future AI use cases

Executive Takeaway

The best AI conversations often begin with a simple question. What decision or task should become easier? Starting there helps the team select the right data, controls, and technology without overbuilding the solution.