By: Paul Perez, SVP & Senior Technology Fellow, Office of the CTO & Dell Federal
Federal agencies can scale AI securely by adopting a workload-first infrastructure strategy that pairs accelerated compute, governed data, and a repeatable deployment model.
Key takeaways
- Federal agencies are expanding AI across mission environments, with 85% of federal chief AI officers saying AI will transform agency operations by 2030 in ways they have not yet imagined.
- Scaling AI requires a workload-first infrastructure path that connects investments to mission value, reduces complexity, and supports secure, repeatable deployment.
- Together, Dell Technologies and NVIDIA provide a platform and reference design that help agencies build a common foundation for priority AI workloads.
Federal leaders have identified where AI can add value, and according to MeriTalk’s 2025 Federal CAIO Outlook, 85% of federal chief AI officers (CAIOs) say AI will transform agency operations by 2030 in ways they have not yet imagined. The focus now is moving those efforts from early pilots into secure, governed production.
Which AI workloads are federal agencies prioritizing?
The focus now is practical: scaling the workloads that can improve mission performance, strengthen operations, and help teams make faster decisions. For federal organizations, those workloads span agentic AI, geospatial intelligence, high-performance computing (HPC), modeling and simulation for science, data engineering, mission intelligence, cybersecurity, and zero trust support.
Each workload carries its own data, performance, security, and governance requirements. Agentic AI requires persistent inference and token budget planning. Geospatial intelligence depends on large imagery, mapping, and location datasets. HPC and modeling environments require accelerated computing and scalable infrastructure. Cybersecurity workloads require trusted data, rapid analysis, and strong control.
A workload-first approach helps agencies identify what each AI effort needs to succeed before they make infrastructure decisions. Once agencies understand the requirements of each workload, they can make infrastructure decisions that support performance, security, governance, and scale.
What infrastructure does federal AI scale require?
Scaling federal AI requires a full-stack infrastructure – accelerated compute, high-speed networking, scalable storage, and enterprise AI software – working together with governed data, security, and compliance.
That foundation must also support AI wherever mission data and users reside. Some workloads may run at the edge. Others may require data center-scale inference or high-performance computing. Hybrid environments will remain essential for agencies balancing agility, data control, latency, and cost.
Repeatability matters, so agencies do not have to build a new architecture for every AI workload. A common foundation can help teams move faster, apply consistent governance, and support multiple AI pathways, from deskside development and edge deployments to data center-scale inference and high-performance workloads.
Repeatability becomes especially important as agencies move AI into production environments where security, governance, and integration requirements become part of day-to-day operations.
Scaling securely
As agencies expand AI, they are also confronting practical implementation challenges. According to MeriTalk’s 2025 Federal CAIO Outlook, top barriers are insufficient funding or resources, lack of internal AI expertise, data quality and accessibility issues, and difficulty integrating with legacy systems.
A repeatable operating model can help address those barriers. It gives teams a more consistent way to select, deploy, monitor, govern, and scale AI models. It also helps align infrastructure decisions with mission requirements instead of treating AI as a series of disconnected point solutions.
The Dell AI Factory with NVIDIA provides agencies a platform for operationalizing AI across Dell infrastructure and services, integrated with NVIDIA accelerated computing, NVIDIA networking, NVIDIA AI Enterprise software, and NVIDIA NIM inference microservices. Paired with the NVIDIA AI Factory for Government reference design, it supports a repeatable approach to scaling agentic AI responsibly, securely, and efficiently. With this foundation in place, agencies can evaluate AI success by the operational and mission results it delivers.
Measuring mission value
For agencies, AI value is measured in practical gains. Did the agency save time? Improve services? Reduce risk? Control costs? Accelerate decisions?
Those questions are especially important as agencies move from isolated AI efforts to production workloads. Infrastructure choices shape how quickly teams can deploy, how securely they can operate, and how predictably they can manage cost.
With the Dell AI Factory with NVIDIA and the NVIDIA AI Factory for Government reference design, agencies can scale priority AI workloads with greater confidence. The result is a secure, repeatable foundation for return on mission – one that helps federal teams turn AI potential into operational progress. Learn more: https://www.delltechnologies.com/assetlink/doc/en-us/meritalk-dell-nvidia-ai-moves-missions-forward-ebook-dl2bqz-original.pdf.