Effective Communication Strategies for Federal Tech Leaders
Effectively communicating complex tech topics requires more than just depth of knowledge; it necessitates clarity, empathy, and adaptability in methods of communication.
Effectively communicating complex tech topics requires more than just depth of knowledge; it necessitates clarity, empathy, and adaptability in methods of communication.
As agencies modernize, they need to find, correlate and act on real-time data wherever it is and in whatever format, and to deliver searches in a new way. A cloud-based solution for government can help.
Agencies today often pursue targeted modernization programs, rather than sweeping changes. Here are four areas in which they’re devoted modernization resources.
Douglas County, Nebraska, used artificial intelligence (AI) and geographic information systems (GIS) to save time and money in its inventory of ADA curb ramps.
Poor data management can undermine an agency’s AI innovations. But by following three core AI principles, embracing flexibility, establishing a comprehensive AI strategy and adopting a forward-thinking data management solution, agencies can realize AI’s potential.
Agencies can improve their cyber defenses by integrating artificial intelligence (AI) into their cybersecurity strategies. Here are specific benefits of an AI-powered approach.
Agencies can optimize the performance of their IT systems and applications by taking a comprehensive approach to collecting and analyzing data. Artificial intelligence, and a unified data platform, can help agencies maximize those observability efforts.
Mismanaged data can lead to poor decision-making, loss of trust, increased risk and other fallout, and artificial intelligence has made data use more complicated. Fast, secure, energy-efficient data storage, however, helps agencies manage what they have.
In the journey toward building more equitable and inclusive AI systems, diversity is not just a buzzword — it’s a fundamental principle that must be embraced at every stage of development and implementation.
Recent advances have made it deceptively easy to build artificial intelligence (AI)-driven applications. But that doesn’t mean that testing AI-based software should be quick as well. Testing should be thorough, and performed by independent teams.