Original Broadcast Date: 03/29/2026
Presented by HII Mission Technologies
Federal agencies are navigating a fundamental shift in how they think about information, and according to Sterling Thomas, chief scientist at the Government Accountability Office, that shift centers on one key idea: data and records are now mission-critical assets.
Thomas explains that agencies are moving beyond traditional approaches that focused primarily on executing missions through actions alone. Today, many missions are deeply dependent on data—how it is collected, managed, analyzed, and ultimately used to improve outcomes for the public. Information is no longer just a byproduct of operations; it is a driver of better, faster, and more informed decision-making.
This evolution is closely tied to the rise of advanced technologies like analytics, automation, and artificial intelligence. While AI often captures the spotlight, Thomas is quick to point out that it is just one part of a broader ecosystem of tools that agencies can use to enhance their work. The real opportunity lies in how these tools are applied.
Agencies that are succeeding with AI and data analytics take a thoughtful, deliberate approach. They begin by clearly defining the problem they are trying to solve—whether it is improving efficiency, reducing workload, or delivering better services to the public. Only then do they select the tools that best align with that mission need.
This “problem-first” mindset is what separates successful implementations from less effective ones. Thomas notes that some agencies fall into the trap of adopting AI simply because it is new or widely discussed. When that happens, they may deploy solutions that do not fully address their needs, leading to wasted time and resources. While there is value in experimentation, a more strategic approach tends to produce better results.
Another important dimension of this transformation is the impact on the workforce. When agencies use data and AI effectively, they can automate routine tasks and free employees to focus on higher-value work. This shift not only improves efficiency but also enhances the overall effectiveness of government operations.
At the same time, managing the growing volume and complexity of data presents significant challenges. Agencies are no longer dealing with a single source of information. Instead, data flows in from emails, collaboration platforms, and a wide range of digital systems. What was once a stack of paper is now a sprawling digital ecosystem.
To navigate this complexity, Thomas emphasizes the importance of strong data governance. Successful agencies take a systematic approach to managing their information, treating all inputs as digital records that must be properly stored, categorized, and maintained. This includes establishing clear rules for how long records are kept, how they are labeled, and how they can be used in future analysis.
One of the most critical elements of this governance model is tagging and classification. By properly labeling data, agencies can make it accessible to analytics tools that can extract insights across large datasets. For example, records associated with a specific project or congressional request can be tagged in a way that allows systems to identify and analyze them collectively.
This capability represents a significant departure from earlier approaches, where data was often siloed and difficult to integrate. Today, agencies are working to break down those silos, enabling a more holistic view of their information. That, in turn, supports more advanced analytics and
However, this process is not without its challenges. The sheer volume of data makes it impossible to manage everything manually. That is where automation comes into play. Technologies such as natural language processing can help identify relevant records, apply appropriate tags, and organize information at scale.
Even with these tools, Thomas stresses the importance of human oversight. Automated systems can make mistakes, and it is essential to have people in the loop to validate results and refine processes over time. This combination of automation and human judgment is key to building reliable and effective data systems.
Looking ahead, Thomas sees continued improvements in the usability of AI and analytics tools as a major enabler for agencies. Early versions of these technologies often required highly specialized expertise, limiting their adoption. Today, more user-friendly interfaces are making it easier for a broader range of employees to implement and benefit from these tools.
He also highlights the emergence of more specialized AI capabilities, sometimes referred to as AI agents. These tools are designed to focus on specific tasks or domains, offering both precision and flexibility. Instead of relying on one-size-fits-all solutions, agencies can deploy targeted tools that address particular challenges.
Ultimately, Thomas’s perspective underscores a broader truth: technology alone is not the answer. The real value comes from how agencies integrate data, tools, and governance into a cohesive strategy that supports their mission.
By taking a thoughtful approach, investing in strong governance practices, and leveraging the right technologies, agencies can unlock the full potential of their data. In doing so, they are not just improving internal processes—they are enhancing their ability to serve the American public more effectively.
As agencies continue to evolve, the role of data will only grow in importance. And those that manage it well will be best positioned to meet the demands of a rapidly changing world.