Government Technology AI·
The pace of development and proliferation of artificial intelligence tools — generative, agentic, physical — can be hard to follow, but IT leaders must do their best to stay apprised of potential innovations and risks.
Read full articleA new law signed June 2 makes computer science a requirement for public schools and creates the Connecticut AI Academy to develop training materials to teach students, teachers and school officials about using AI.
New data suggests AI-driven payment rails are evolving beyond experimentation as users are making more higher-value transfers.
For the past few years, enterprise AI conversations have been dominated by optimism: bigger models, more pilots, faster automation. The prevailing assumption was simple — pick the right AI platform and progress would follow. Reality has been far less forgiving. Most IT leaders have discovered that production AI is significantly harder than early experimentation suggested. The real work begins not when a model performs well in isolation, but when it must operate inside environments that are secure, observable, and operationally durable. Recent research my company conducted with enterprise cloud architects and IT decision-makers confirms what many engineering teams already know instinctively: experimentation is easy. Operationalizing AI reliably, repeatedly, and at scale is the hard part. Once AI begins influencing real workflows, recommending decisions or triggering actions, the model quickly becomes the least interesting part of the system. The pressure shifts to everything around it.
Lavern, which is figuratively a ‘law firm’, has formally launched and now offers 67 specialist legal agents that can perform key tasks. Together they form ...
Credentialing programs in artificial intelligence are multiplying fast, but educators and researchers say their value depends on workplace relevance, hands-on learning opportunities and measurable career outcomes.
When it comes to AI adoption, some institutions lead with executive strategy, others with faculty experimentation, but all are working through governance, curriculum updates and faculty training.
Software companies Oracle and Drivestream developed a simulated environment for administrators and IT leaders from 15 institutions to see how AI agents could augment university operations.
AI safety shifts from the model to the system level. As AI becomes agentic and tool-driven, risk emerges from complex interactions, widening the gap between evaluation and real-world behavior.
