Agencies Seek Gains Through Precision Modernization
Agencies today often pursue targeted modernization programs, rather than sweeping changes. Here are four areas in which they’re devoted modernization resources.
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.
Artificial intelligence (AI) is an interesting new technology, easier to implement than it is to regulate. Here are practical steps agencies can take today to begin using AI safely and responsibly.
In government, promoting positive citizen experiences poses unique challenges. Modern case management platforms, however, can help agencies rise to the challenge through greater visibility, control, and traceability of data and processes.
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.