EY Report Highlights Use of Internal Data Repositories in Training AI Models by Federal IT Leaders

A recent report by Ernst & Young (EY) indicates that most federal IT decision-makers and influencers rely on internal data repositories for training artificial intelligence (AI) models. Specifically, 58% of federal respondents look to these resources as the main source for their AI training data.

Exploring Data Sources for AI

Apart from internal repositories, the report highlighted how agencies diversify their data sources for AI training. Public datasets, third-party providers, and internal data marketplaces are among the options explored to perform various data analysis tasks.

Building and Training AI Tools

The EY report’s findings, which were based on an online survey done in collaboration with Market Connections, showed that federal IT leaders are utilising different methods to build and train their AI tools. More than half (51%) of the respondents confirmed the adoption of such mixed methodologies.

Conclusion

The EY report contributes valuable insights into the AI strategies being adopted within federal IT circles. The heavy reliance on internal data repositories amplifies the essential role of high-quality, accurate, and comprehensive data in harnessing AI’s full potential. This report encourages further exploration of a multifaceted approach to data source diversification and methods for building and training AI tools. More details can be found in the original Executive Gov article.