Good afternoon. I lead the AI team for AEM Corporation. We work with several Chief Data Officers and Chief AI Officers on AI adoption.

Thank you to the Data Foundation for arranging this event. I am, our team is, as excited as anyone about the potential for AI.

I have been asked to highlight a challenge today and invite you to reconsider how you use large language models. I hope you will think differently about how you approach building and buying AI systems. If I’m successful, you may also consider policy changes for what we require of our AI systems in the future.

While the concept of early warning systems (EWS) is often associated with disaster management, its fundamental principles of early detection and proactive response hold significant promise for broader application within public administration.

The core idea behind an EWS is to establish an integrated framework for monitoring potential threats, assessing risks, communicating warnings, and preparing for timely action. Traditionally, these systems have focused on tangible hazards, but their underlying structure can be effectively adapted to identify and address less visible yet equally critical challenges such as emerging public concerns and potential service failures within federal agencies.

Traditional methods of handling these reports often involve manual processes that are time-consuming, prone to errors, and limited in their ability to extract comprehensive insights.

These manual approaches struggle to keep pace with the increasing volume and complexity of grant portfolios, hindering effective oversight, informed decision-making, and the identification of systemic trends.

This heightened level of civic participation, while a success for democratic engagement, has presented federal agencies with a significant challenge: the "volume problem."

Agencies now routinely face the task of effectively handling tens of thousands, hundreds of thousands, or even millions of public comments on regulatory and policy matters.

Agencies must commit to continuous improvement and adoption of strategies that clearly lead to positive outcomes. 

While quantitative data provides crucial metrics for measuring performance, qualitative analysis offers a vital, complementary perspective by exploring the nuanced understanding of why and how certain practices achieve success.

While numerical metrics provide essential information, they often fail to capture human experiences, stakeholder perspectives, and underlying causes of complex societal problems.

By analyzing qualitative information, federal agencies establish stronger connections with constituents and stakeholders, fostering better understanding of their needs and concerns.

As William Bruce Cameron famously observed: "Not everything that can be counted counts, and not everything that counts can be counted.

With growing public scrutiny and emphasis on citizen-focused services, understanding the needs expressed through qualitative data has become essential for effective federal agencies.

Unfortunately, quantitative data frequently falls short in addressing the critical "how" and "why" questions that inform our understanding of complex issues.

Agencies are increasingly bringing AI into their analytical workflows, drawn by its promise to handle larger datasets, streamline processes, and speed up research. But while the role of analysts is changing, their contribution to meaningful insights is as important as ever.