Adopting AI will enhance operational efficiency and public service. But it will require significant policy changes across U.S. federal agencies to ensure it’s responsibly and ethically implemented.
Outdated systems can stifle innovation and frustrate users. But with machine learning, organizations can streamline their operations and implement a more user-centric approach.
The release of AI foundation models allows organizations to take advantage of broader community investments in AI. But there’s a catch: Agencies need to tweak and train these models to meet their specific objectives, maintain privacy and ensure regulatory compliance.
There are four types of communication platform: Rented, owned, paid, and earned. Learn how to leverage each.
Right now, quantum computing technology is only somewhat helpful. But it has great potential. Here are some possible use cases, as well as a discussion of how close they are to reality.
Organizations depend on a wide array of tools to support CX, and more tools create more attack vectors. A cloud-based resiliency platform can ensure rapid recovery after an event. Here’s how that works.
You can’t have an AI strategy without a data strategy, says Matthew Rose of Snowflake. To be ready for AI, you need an ecosystem for data.
How should federal agencies plan for AI technology? To fully harness the power of AI, federal agencies should create a clear AI roadmap outlining their goals, timelines, and key milestones. This roadmap should be flexible, allowing for adjustments as new technologies emerge and federal priorities shift.
Change is hard, and nothing changes faster than government work. In the reality of government service, we have a workforce that frequently finds itself navigating seasons of change created by mandates or political shifts. Here are some ways to make it work.
AI-powered knowledge graphs can enhance interagency collaboration by mapping and visualizing relationships between diverse data sources.