I have a confession. I love gadgets…and gizmos too. Today’s world offers plenty of them, and if I’m not careful, I can happily get lost exploring what each new tool can do.
But gadgets and gizmos shouldn’t be the focus, especially when it comes to AI in government.
When we talk about AI, it’s easy to fixate on the technology itself: models, platforms, capabilities and features, and what the system can automate, accelerate or analyze.
In practice, though, AI rarely succeeds or fails because of the technology. It succeeds or fails because of how people are brought into it.
Government is, at its core, a human enterprise. People design programs. People deliver services. People make decisions that affect health, safety and livelihoods. AI doesn’t replace that reality, but it does change how people accomplish their work.
That’s the idea behind AI Workforce Enablement: deliberately integrating AI in ways that strengthen the workforce, support the mission, and respect the role of human judgment rather than undermining it.
Starting with the People
Every AI implementation impacts how people do their work. It may reduce the time it takes to review a case, surface patterns that were previously invisible, or summarize information that once required manual effort.
Because AI will change, and in some cases reduce or redefine, roles as well as tasks, agencies have a responsibility to lead that transition in ways that preserve institutional knowledge and mission continuity. But if implemented poorly, it can accelerate the wrong thing, scaling inconsistency, bias or poor decision-making.
That’s the risk of treating AI as “just another tool.” The visible impact may look positive at first, but the hidden impacts emerge later: confusion about roles, inconsistent decisions or a growing distrust of system recommendations.
AI workforce enablement starts with practical questions about the work itself: how day-to-day tasks will change, which decisions become easier or more complex, where new judgment is required, or where human expertise must remain essential. When agencies address these questions early, AI adoption is smoother, safer and more sustainable.
Human-in-the-Loop Is About Responsibility, Not Just Oversight
Human-in-the-loop (HITL) is foundational in government AI.
It ensures that people remain responsible for decisions, review outcomes and intervene when systems produce unexpected or inappropriate results. In high-stakes environments like benefits administration, healthcare review and security screening, that responsibility cannot be automated away.
HITL is about shared responsibility throughout the lifecycle. It requires involving people from start to finish:
- During design, when goals, constraints and acceptable tradeoffs are defined
- During implementation, when outputs are tested against real-world scenarios
- During operations, when patterns, edge cases and exceptions are identified
When people are included at each stage, two things happen at once: the AI improves more quickly, and trust builds naturally. People develop a better understanding of how it behaves and where they fit into it.
AI Literacy: Confidence Is the Real Accelerator
Resistance to AI is often misunderstood.
In my experience, most hesitation isn’t ideological or emotional…it’s practical. People hesitate when they don’t understand how something fits into their role. AI literacy addresses that gap by helping people understand how to use AI responsibly and effectively in their work. It focuses on showing them what the system does and does not do and how it fits into their existing workflows, and translating technical AI language into concepts and terms that are familiar and meaningful to the people using it. Until people understand the terminology of AI, it will always feel like magic that is inaccessible to them.
When people understand AI at this level, confidence replaces guesswork, decisions become more consistent, and AI becomes more effective because the people using it are better equipped to apply it. Targeted upskilling in the areas that matter is what turns AI from a bolt-on tool into a trusted part of everyday work.
Using AI to Accelerate How Work Evolves
The most effective AI initiatives start with a willingness to revisit how work happens.
Every agency already has opportunities to improve how decisions are made, how information flows and how effort is focused. Those changes are often hard to make on their own. AI can accelerate that evolution if it’s introduced intentionally and in service of the change itself.
When agencies approach AI adoption from a people change management standpoint, the goal isn’t to “manage the disruption” caused by AI. The goal is to lead a thoughtful evolution of work, with AI serving as an enabler that makes those changes more practical and impactful. In this model, AI helps make better practices easier to sustain and reduces noise by surfacing what matters most.
Approached this way, AI adoption feels less like a technology rollout and more like progress toward a clearer, more effective way of working – one that the workforce can understand, trust and participate in.
That’s the difference between implementing AI versus enabling the workforce to use it.
Keeping the Focus Where It Belongs
Successful AI adoption isn’t about maximizing the technology. It’s about maximizing what people can do with it. AI Workforce Enablement keeps that focus where it belongs. It ensures AI strengthens the workforce, supports sound judgment and stays aligned with each agency’s mission.
Three Practical Takeaways
- Start with people, not platforms.
Understand how work and roles will change before introducing AI. - Invest in confidence, not just capability.
AI literacy and targeted upskilling are what make systems usable and trusted. - Design for real workflows.
Human-centered design and intentional people change management determines whether AI sticks.
When AI is introduced with intention, clarity and respect for the workforce, it doesn’t become just another set of gadgets and gizmos – it becomes progress. It enables better decisions. And it helps people do their jobs more effectively while keeping the mission human.
