It’s easy to dismiss government IT infrastructure as a sprawling mess of legacy servers, tangled middleware, and overburdened staff forever patching holes. And easier still to see federal IT managers as beleaguered bureaucrats drowning in tech revolutions they can't quite grasp.
But think about it another way. The reality revolves around massive budget cuts, staff implosions, and relentless pressure. Viewed through this lens, federal agencies aren't adopting new technology by choice, but by sheer necessity.
Randy Hayes, Vice President of Public Sector at VAST Data Federal, sees this reality clearly. The federal tech imperative is sharper, more brutal, and more urgent than ever.
“The federal government doesn’t have the luxury to experiment,” Hayes argues. There’s zero margin for trial-and-error cycles or iterative guesswork, not when mistakes unfold publicly, scrutinized by taxpayers and oversight committees with little tolerance for missteps.
This is the core federal dilemma is how to deploy agentic AI but for Hayes, the answer is simple (at least on the surface). Every decision must be guided by proven expertise.
Here’s the problem as he sees it. A thousand tech companies now wear “AI” as a badge of honor, yet precious few possess the operational depth needed to justify the claim.“
Ask most of these companies how many AI pipelines they’ve built, and for whom. The number will be very low,” he challenges, noting that while the landscape is littered with pretenders quick to brand themselves AI specialists without nothing to show for it, agencies can't afford the trial-by-hype approach.
This leaves federal IT in an impossible bind, caught between urgency and capability, the imperative of innovation against the backdrop of diminished resources.
Staffing is a key choke point. Layoffs have decimated IT teams, leaving a skeleton crew of sys admins and managers forced to shoulder the workloads once handled by entire departments. “People who remain are doing the job of three or four colleagues,” he says.
But the problem goes beyond just staffing. Federal IT infrastructure is often a patchwork of multiple vendors, multiple storage tiers, tangled webs of analytic tools and middleware, all of which conspire to create operational chaos. “
The complexity is tremendous,” Hayes says. “Agencies manage layers of redundant technology, each with its own vendor contract and lifecycle. It becomes impossible to sustain.”
It’s natural to make the argument for a simple solution. The wholesale consolidation of unwieldy, fragmented infrastructure into a single, efficient architecture.
This goes beyond simply collapsing storage tiers (although that alone is significant). It means integrating analytics platforms, streamlining data movement and security operations, and eliminating countless redundant contracts.
“The question we get from agencies right now is, ‘how many things can we get rid of?’” Hayes says.
The answer, invariably, is a lot.
Agencies can no longer sustain inefficiency, redundancy, or the needless complexity of their existing architectures. “We’ve been selling efficiency since day one,” Hayes emphasizes, “but now it’s no longer optional. It’s forced by the reality agencies face.”
The notion of “doing more with less” is now a federal mantra, but the cliché masks deeper urgency. This is especially clear in frontline federal operations like TSA screening, border management at CBP, or patient processing in Medicare and Medicaid offices, all areas ripe for automation through agentic AI. These are repetitive, rule-based processes ideal for automation, processes commercial enterprises have long since mastered. But agencies have lagged behind, not through inertia, but through resource constraints, expertise gaps, and fear of costly experimentation that yields little.
Agencies must abandon layers of redundant complexity and replace them with unified architectures built explicitly for real-world AI workloads. It’s an aggressive simplification strategy, reducing infrastructure clutter to an elegant minimum.
“We become the single throat to choke,” Hayes says without irony, meaning VAST Data assumes both responsibility and accountability for an agency’s entire data-driven architecture, from storage through analytics to full-stack AI pipelines.
Yet, even beyond infrastructure consolidation lies a deeper issue. Genuine AI expertise remains rare, expensive, and difficult to access. Agencies risk entrusting critical infrastructure transformations to superficial vendors lacking real-world validation. On this point, Hayes is unforgiving. Agencies must verify credentials rigorously.
“We’ve built thousands of AI pipelines for thousands of organizations,” he says, pointing to tangible, demonstrable deployments. “Real experience matters because federal agencies simply don’t have the freedom to fail.”
Agencies aren’t venture capital firms gambling on technology startups, they’re public entities accountable to taxpayers who demand responsible stewardship of public resources.
Experimentation, in this environment, is a political minefield; pragmatic certainty becomes the necessary path. Infrastructure decisions must solve problems immediately, on first deployment, without prolonged, costly refinement.
“This isn’t iterative. Agencies need solutions that work on day one.”
Further, he argues agencies facing relentless budget cuts and diminishing human resources have to pivot toward proven AI infrastructure that directly supports their mission-critical work.
Automation, integration, and streamlined architectures form the three pillars supporting this new federal IT reality.
Hayes’s vision for the federal sector, stark but optimistic, hinges on agencies’ willingness to embrace this new pragmatism.
Resource constraints and relentless scrutiny have forced federal agencies into a position where adopting truly expert-driven, consolidated AI infrastructure is their only viable path forward.
“You’re going to buy infrastructure anyway. Why not choose something designed specifically for the complexities of real-world AI workloads,” Hayes says.