Agencies Tackle Infrastructure Challenges to Drive AI Adoption

New presidential directives are promoting the adoption of commercial AI technologies, and federal leaders are now shifting from simply managing data to maximizing its utility to unlock new mission capabilities, accelerate automation and strengthen cybersecurity.

Achieving meaningful AI transformation will depend on strategic focus across four key pillars: workforce enablement, citizen engagement, mission process redesign and data fusion.

Aligning with Presidential Directives

Adoption of AI in the public sector has been hampered in the past by barriers relating to legacy technology and data. The government has a data gap when it comes to data access and data readiness. Much of the public sector’s focus on data has been on how to manage and secure data. Now, the focus must shift to how agencies can promote their data for maximal use.

Several new presidential directives allow the government to adapt commercial AI technologies as they exist in the commercial sector, with the goal of promoting American leadership in AI. To realize the true potential of AI in the public sector, agencies will need to focus their efforts to achieve improvements in three key challenge areas:

• Leveraging AI to unlock new efficiencies: New mission capabilities are emerging, thanks to AI. In fields like national security, AI is proving to be a force multiplier. For example, intelligence analysts can deploy AI to sift through vast amounts of surveillance data or open-source information to detect threats faster. The Defense Department and Intelligence Community are exploring classified AI deployments —such as Azure OpenAI Service in secure clouds — so that even highly sensitive operations can leverage cutting-edge models for tasks like imagery analysis, situational awareness and decision support. This accelerates the ability to respond to threats and protects national interests in ways not possible before, effectively innovating within the tight constraints of security protocols.
• Reducing workforce burden with automation: Across agencies, AI-driven business process automation is reducing mundane manual work. Routine tasks like invoice processing, compliance checks, scheduling and data entry can be handed off to intelligent systems. In the federal AI use case inventory, a significant portion — about 46% — of reported AI projects were aimed at mission-support functions, including finance management, HR, cybersecurity and record-keeping. These are areas ripe for automation.
• Bolstering cybersecurity with AI: Many agencies are struggling to counter a constantly growing number of sophisticated cybersecurity attacks, which deplete budgets and strain workforces. The rapid rise of AI is pushing agencies to modernize their IT infrastructure to support these advanced workloads. This modernization not only enables AI but also improves overall efficiency, storage and cybersecurity. On the security front, AI innovations like Microsoft’s Security Copilot demonstrate how generative AI can radically improve cyber defense by analyzing threats and recommending fixes in moments, helping agencies counter escalating cyberattacks more effectively.

True Federal AI Transformation Relies on a Stronger Foundation

Federal transformation requires more than just adopting new tools. It demands a solid foundation that supports long-term, scalable innovation. This means rethinking how the government empowers its workforce, engages with citizens, modernizes mission processes and accelerates technology adoption. Agencies can build an environment where AI isn’t just a novelty, but a core driver of efficiency, trust and mission success by focusing on four transformation pillars:

• Enhancing employee experience by automating repetitive tasks: The workforce can devote their time to focus on high-value work. As government employees become more comfortable with AI, they’ll spend more time managing AI agents that will work alongside them, driving even greater efficiency.
• Improving citizen engagement through personalized experiences and scalable services powered by natural language technologies: Modern federal services enable citizens to manage interactions with government more easily and efficiently, reducing delays, minimizing confusion and strengthening public trust in the agency.
• Infusing AI into traditional processes to drive efficiency: Mission processes can be reshaped by reenvisioning AI’s role and baking AI into traditional processes. Opportunity exists for efficiency by implementing AI-assisted coding. Authoring new code, modernizing old code and delivering more features and functions is easier with AI. Another exciting use case is data fusion for defense and intelligence. With multiple sources of data and an enormous volume of sensor data, being able to quickly fuse and integrate all that data in a way that enables you to pull out the valuable insights you need for decision support is essential. Multiple agents orchestrating over those diverse data sets are producing breakthroughs and efficiencies in intelligence analysis, mission planning and simulation, as well as other key areas.
• Utilizing AI to bend the curve on innovation and infuse technology faster: The current planning and acquisition process takes so long that by the time it is done, the technology has moved far beyond the scope of procurement. While the administration will need to simplify and speed up the acquisition cycle, agencies can adopt already proven AI technologies to allow for transformative technology innovation.

Rather than seeking individual AI products for coding, consumption and customization, the AI Platform provided in Microsoft Cloud empowers all three of these critical functions. This greatly accelerates AI adoption, since you are not asking your workforce to learn separate tools. Splitting your AI adoption across multiple tools relegates the agency back into an integration nightmare, which is no more efficient than the old system.

By leveraging the power of the Microsoft Cloud AI Platform, agencies can establish improvements in all four pillars of AI transformation today, and be positioned to continue their AI evolution in the future.