Beyond GenAI: Training Models for Precision Government Use Cases

Across government agencies we’re seeing it deliver mission critical ROI on use cases like grant writing, citizen services, and employee training. In fact, according to Boston Consulting Group, GenAI’s effect on government productivity and service quality has the value potential of $1.75 trillion annually by 2033.

But while GenAI is adept at quickly summarizing large quantities of existing content, many government use cases require a higher level of precision. Think digitizing tax returns for fraud, automating social services processes, or ensuring contractors are adhering to unique security standards – cases that can’t afford “good enough” in terms of accuracy. For these highly precise circumstances, agencies need very specific AI models, trained over an extended period, that can be counted on to deliver every time.

GenAI’s effectiveness depends on the quality of the information it processes. The challenge for the government is that to reach this level of proficiency and trust, these models need to learn from massive amounts of high-quality digital data. It is critical for agencies to start building the digital foundation needed to train these future AI models. Information needs to be organized and provided to GenAI in a structured format in order to yield effective results.

For missions that require precision, agencies must think beyond GenAI and start training predictive models today. By implementing three foundational steps, governments can begin to harness AI to enhance efficiency and service quality, ultimately revolutionizing how public services are delivered.

1. Digitize Your Data Foundation

Governments possess a wealth of data. However, much of this data is unfortunately stored in formats that are not accessible to AI systems. The White House reported only 2 percent of federal government forms in 2023 have been digitized (offered as a dynamic online form, not just a fillable online PDF). This means that most of the valuable data from public interactions and government records is often lost in paper formats or outdated systems, creating significant barriers to efficient data utilization.

To overcome this barrier, governments should conduct an audit of all available data sources, including paper records, microfiche, tapes and existing digital files. From there, records managers can seek automated digitization tools and techniques to convert physical records into digital formats. Through this process, governments can ensure their digital data is standardized and organized in a way that makes it accessible and usable for AI training.

2. Clean Your Data

Agencies need to structure data in a way that makes sense to AI models. Recent estimates suggest that approximately 80-90 percent of all data generated by organizations is unstructured, including data types such as emails, documents, social media posts, images, and videos. Data must be organized in a way for AI models to ingest and learn from it. This requires cleansing initiatives to remove duplicates, errors, and inconsistencies as well as structure the data to be as uniform as possible.

Agencies should establish robust data governance practices to continuously maintain accuracy for existing data and new data being created. This ensures that agency data management adheres to standardized protocols and becomes “AI ready”.

3. Train Your Models

With a clean data foundation in place, agencies can begin to train their models. Utilizing pilot programs is an efficient way for government agencies to test use cases in a low-risk manner that could yield high-reward. For example, the Federal Emergency Management Agency (FEMA) is experimenting with predictive AI to assist in developing hazard mitigation plans. Using these new insights from AI, FEMA is able to help communities plan for and develop strategies to build resilience and minimize risks. By investing into pilot programs, government agencies can assess numerous AI scenarios at the same time, without a large financial / resource commitment. Initiatives with higher accuracy can be prioritized for expansion over other less successful projects. The models with the best precision can be leveraged in similar situations (i.e., citizen transactions, health records) within the same agency or across the Federal government.

As model use expands beyond their pilots, they will be continually refined / trained using new high-quality data, helping to improve their accuracy and reliability. As algorithm precision increases, government leaders can apply the AI models to more complicated, high-impact applications, unlocking the benefits of GenAI to greater citizen interactions, inquiries, and processes.