Infrastructure, Cloud, and the Next Phase of AI in Government скачать в хорошем качестве

Infrastructure, Cloud, and the Next Phase of AI in Government

Dave Hinchman, Director of IT and Cybersecurity at the Government Accountability Office, and Wole Moses, Chief AI Officer, US Federal Civilian at Microsoft, discuss how infrastructure decisions determine whether AI can scale inside agencies. Dave explains that the federal government is still in the early stages of AI adoption. While tools and pilots are expanding, AI is not yet integrated broadly across operations, and agencies are still working through how to apply this technology in sustainable, scalable ways. Dave shares GAO lessons from auditing major IT implementations, emphasizing that planning should happen early—ideally before agencies engage vendors—so leaders understand what they are pursuing and what outcomes they expect. He also stresses that agencies must design for flexibility and adaptability, because AI will continue to evolve rapidly and systems built today must remain viable as new capabilities emerge. Wole explains that major cloud providers offer managed AI services that simplify many architecture decisions, including access to AI models, GPUs for training and inferencing, and tools for building AI applications. However, he stresses that agencies still must make key decisions about model selection and benchmarking, governance, observability, and how AI applications will integrate with existing mission systems. He notes that many of the highest-value AI deployments depend on integration with systems like case management and records platforms, meaning architecture planning must include connectivity and workflow integration from the beginning. The conversation also focuses on integration strategies for legacy environments. Wole explains that agencies are building abstraction layers, such as REST APIs, to enable modern connectivity into older systems. He also points to AI-native connectivity approaches, including model context protocols, along with other integration technologies that can help connect systems that were never designed for AI-era workflows. These approaches can help agencies adopt AI while modernizing incrementally rather than waiting for full system replacement. Dave agrees that AI has been adopted more through defined use cases than many past technology waves, pointing to rapid growth in the government’s AI use case inventory as a sign that agencies are identifying mission-relevant problems and applying AI accordingly. At the same time, both leaders emphasize that culture remains a major barrier. Dave explains that agencies need strong executive sponsorship and communication to help employees understand why changes are happening and how AI will affect work. Wole outlines challenges across people, process, and technology, including uneven understanding of AI capabilities, uneven trust and adoption, and a tendency to bolt AI onto existing workflows rather than redesigning processes around new capabilities. The conversation concludes with a forward-looking view of what agencies should be preparing for next. Wole encourages agencies to begin building awareness and readiness for agentic AI, moving from today’s model of AI assistants toward environments where AI agents operate as collaborative teammates—and eventually toward more autonomous scenarios. Both leaders reinforce that long-term success will depend on combining adaptable infrastructure, strong governance, cybersecurity discipline, and workforce training so agencies can scale AI responsibly while maintaining trust and mission performance.