Across the federal domain, organizations are at varying levels of automation adoption. Simple automation is showing more maturity with 48% indicating they have completed some simple automation. This is slightly higher than 2022 (45%). Robotic process automation (RPA) initiatives completed increased by 17% compared to 2022.
Rapid advances in AI require the development of a repeatable, ongoing and adaptive AI strategy.
All that said, federal agencies are highly focused on investigating, creating proofs of concept and starting to implement automation at nearly all levels. In particular, 65% are investigating and researching Artificial Intelligence (AI), while 5% are conducting AI proofs of concept. This, coupled with the macro trend of AI, indicates that agencies are focused on learning more with an eye toward future AI projects.
CGI firmly believes that AI is highly relevant and applicable to the future of government operations. However, rapid advances in AI require the development of a repeatable, ongoing and adaptive AI strategy. Agencies must continue to explore AI innovation aligned to mission priorities, as the value it brings to improve government operations is still unrealized.
At the same time, leaders recognize the need to advance AI responsibly, which means AI development must apply rigor and risk management to ensure solutions are accurate, inclusive, transparent and safe. Costs associated with AI programs must also be considered, with leaders focusing on the best use cases for AI that will derive the greatest mission value.
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Improving federal AI with self-supervised learning
Self-supervised learning seeks to make the way machines learn closer to the way that humans do. It promises to deliver benefits of supervised learning, while reducing the time and labor expense of manually labeling massive quantities of data.