# Why Government AI Teams Need Digital Natives Now

Parker Elliott · December 26, 2025

> Why Government AI Teams Need Digital Natives Now. Unlocking Agility and Rapid AI Adoption Look, if we're talking about getting AI working for the gover...

## Unlocking Agility and Rapid AI Adoption

Look, if we're talking about getting AI working for the government *now*, not next year, we have to stop treating it like a software update you install on Tuesday. It’s not about just adding a few smart tools; it’s about fundamentally changing how the work actually gets done—that "data & AI-native" process transformation Fujitsu was talking about. Think about it this way: we've seen over a thousand customer transformations, and the common thread wasn't the AI itself, but how quickly those companies could mold their existing processes around the new tech; that's the agility factor. You know that moment when you finally see a new technology click, and suddenly everything moves twice as fast? That's what we need to engineer, and honestly, that speed is tied directly to giving people what McKinsey calls "superagency"—letting the folks doing the job use AI to bypass layers of bureaucracy. If the workforce is shifting the way Deloitte says it is, we simply can’t afford the decade-long ramp-up times of the past; we need the "Frontier Firm" mindset where deployment cycles are measured in weeks, not quarters. I really think that if the IT foundation isn't already lean, we're just setting ourselves up to fail before we even start plugging in the latest large language models.

## Modernizing Infrastructure wi

Look, if we’re serious about actually running AI workloads in government and not just talking about proofs-of-concept, we’ve got to talk about the basement—the infrastructure itself. It’s not just about buying some new servers, right? We’re talking about making things "cloud-native," which really just means building things so they can stretch and shrink like elastic, instead of setting concrete foundations that take five years to pour. If you want that true AI transformation that lets people bypass old hoops, the backbone needs to be inherently scalable and super resilient, otherwise, that fancy new LLM just crashes when too many people try to use it at once. And that’s where I get kind of worked up: if the infrastructure investment isn't made now to be elastic, we’re just building a fancy new AI engine on top of a very old, very slow road. Think about it this way: you wouldn't put a Ferrari engine in a Model T chassis and expect to win races, would you? We need that intelligent backbone ready *before* we start deploying the real firepower, ensuring we have the agility to handle whatever comes next, because honestly, the next big AI thing is probably right around the corner.

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