Artificial intelligence is rapidly expanding throughout the federal government. Currently, close to 90% of agencies have either implemented AI or intend to do so down the line. However, simply adopting AI doesn’t mean agencies are truly prepared. The same studies indicate that a lack of skilled personnel remains one of the biggest hurdles when rolling out AI.
This isn’t a shortcoming—it’s actually one of the greatest opportunities available to federal agencies right now.
Compliance was never intended to be the endpoint
Required yearly training sessions, cybersecurity awareness programs, ethics briefings, and acceptable use policies are all essential. They lay the groundwork every agency requires. But that’s precisely what compliance was designed to be: the starting point, not the finish line.
From leading tactical training with government teams to managing federal contracting across multiple public and private sector organizations, what I observed time and again wasn’t a shortage of dedication or ability, but a gap between understanding policy and being able to act under pressure. Those are two entirely different competencies, and only one appears on a training completion report.
In high-stakes scenarios, the top-performing leaders were those who had trained so rigorously that nothing caught them off guard when it truly counted. Likewise, the strongest teams didn’t fall apart when things went sideways. They stayed composed.
These leaders pushed themselves beyond comfort in practice by constantly pushing boundaries, so the real challenge felt routine. Those teams genuinely wanted to work together. They made the most of every moment, had confidence in one another, and were assured in their capacity to deliver when it mattered most.
That same level of rigor is precisely what government agencies must develop right now.
Developing capability demands consistent discipline
Agencies that excel under pressure are all methodical about how tasks get accomplished, not just what the policy states. When teams are stretched thin and expectations are intense, basic practices can erode. Reacting becomes the norm. And when that sets in, uneven performance follows. That was the case before AI emerged and it’s even more critical now.
A 2023 Government Accountability Office audit revealed that 15 out of 23 reviewed agencies had incomplete or inaccurate AI use-case inventories. What may appear to be a technology issue is really a discipline issue. You can’t scale what you can’t monitor, and you can’t monitor what you haven’t standardized.
After years of working within and alongside government organizations, I created a proven framework that distinguishes the agencies that deliver from those that get stuck:
Decision ownership. Every high-pressure situation has one shared failure point: no one knows who’s responsible for the call. AI speeds up decision-making, but if authority is ambiguous, velocity turns into a risk. Owning the decision means clarifying in advance who decides, what they decide, and when they escalate. Once that’s in place, teams stop seeking approval and start acting with intent.
Enforce the standard. A standard without enforcement is merely a recommendation. Agencies can deploy the most advanced AI tools on the market, but if there’s no uniform expectation for how they’re applied and how results are evaluated, outcomes will be inconsistent and advancement will plateau. The Defense Department’s Responsible AI framework does this correctly. It specifies who is accountable for results across the full AI lifecycle, from creation through deployment, so responsibility remains transparent even as AI grows across the organization. That’s enforcing the standard at scale.
Multiply capability. Government leaders are being asked to achieve more with fewer resources. The solution isn’t grinding harder individually, but growing the people around you so the entire team can share the burden. When capability is expanded and spread out, the organization becomes adaptable. When it rests with just one or two individuals, a single unexpected issue can derail everything. AI won’t fix that dynamic. Only deliberate development will.
Practice under pressure. Most training occurs in relaxed, controlled environments. Most real-world situations aren’t. The GAO has taken a positive step with AI training connected to specific use cases, equipping employees to act both efficiently and responsibly. But having access to training is only the beginning. Scenario-based exercises, actual decision points, and simulated stress create the kind of instinctive response that holds up when conditions get tough. Practice is where capability becomes dependable.
Advance the mission. Everything above is meaningless if it doesn’t result in measurable progress. The purpose of capability development is a workforce that performs reliably, adjusts rapidly, and fulfills the mission regardless of what circumstances arise. Most generative AI pilots fall short because they were never fully put into operation, not because of the technology itself.
Track what counts. Completion metrics show you who attended the training, not who can deliver under pressure. Valuable measures are decision speed in critical moments, how promptly teams raise concerns when needed, whether habits shift after training or revert within 30 days, and if leaders uphold standards daily or allow them to fade. These reveal whether your workforce is genuinely ready or merely compliant.
AI won’t be the last transformation that requires something new from the government workforce. The agencies that will keep fulfilling their mission aren’t necessarily the ones that act first. They’re the ones that invest in how their people think, decide, and perform, and build the discipline to maintain it over time.
Compliance brought us to this point. Capability is what carries us ahead.
Ray Resendez is senior vice president of federal solutions at ELB Learning.
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