AI Isn't the Distraction. The Busywork Around It Is.
Government teams are under pressure to deliver faster service, stay compliant, and update legacy systems, all while managing the routine tasks that eat up the day. Reading every document yourself. Manually processing information that should already be organized. Trying to justify a new tool to leadership. Navigating an AI policy that’s still being written. Managing compliance and risk on top of everything else.
None of that is really about AI. It’s about the busywork that keeps teams from doing the work they were actually hired to do. AI matters because, done well, it can lighten some of that load. Done poorly, or oversold, it just becomes one more thing to manage.
AI is a tool, not a magic wand. The agencies getting real value out of it are pairing it with a clear policy, a testing phase, and a person who still checks the work. If you’re trying to figure out whether AI is worth the investment or just worth the noise, start here.
AI as a Tool, Not a Replacement
The agencies seeing real results treat AI as a way to handle repetitive first-pass work: sorting, extracting, flagging. People still handle the exceptions, the judgment calls, and the final quality check. AI supports the work. It doesn’t own it.
Why It Matters: Is AI Overhyped?
Parts of the AI conversation are overhyped, and that’s worth saying out loud. But hype around a technology doesn’t mean the technology itself is useless. Problems show up when an organization treats AI as a complete solution instead of pairing it with good process design, accurate data, and human judgment. Used that way, AI can produce measurably better results than older tools like basic pattern matching, especially on messy, inconsistent documents.
How to Get Leadership Buy-In Without Overselling It
Make the case on efficiency, accuracy, and access, not magic. The strongest pitches to leadership aren’t built on buzzwords. They’re built on three things: efficiency, accuracy, and access to information. Ground the request in a real, specific problem your team deals with today, and be upfront that AI is there to handle repetitive work so staff can focus on higher-value judgment calls, not to replace anyone.
It also helps to look outward. Agencies at every level, cities, counties, state departments, higher education, are working through similar questions right now, often at different speeds. In the state, local, and education government space, peer organizations are frequently willing to share what worked and what didn’t. You don’t have to figure this out from scratch.
Where the Legal Lines Are (And Why That’s a Good Thing)
Automate the busywork, not the accountability. Not everything should be automated, and records managers already know this better than anyone. Take records destruction as an example. Automation can support a lot of the surrounding work: assigning retention schedules, placing documents correctly, managing access rights, applying metadata. But the decision to purge a record, and the accountability that comes with it, still needs to sit with a person, backed by audit logs and clear policy.
This isn’t legal advice, and every agency’s rules are different. If you’re unsure where the line is for your organization, your state archivist and your internal policy are the right places to start, not a vendor’s marketing page.
A Few Questions Worth Asking Before You Start
Before any AI project moves past the pilot stage, it’s worth running through a short list of questions with your team and your leadership. What data is the AI actually touching, and is any of it sensitive or restricted? Who reviews the output before it becomes official? What happens when the tool gets something wrong, and how will you know? Is the vendor’s approach to security and data handling documented clearly enough for your compliance team to sign off on it?
None of these questions are meant to slow a good project down. They’re meant to make sure the project you build is one your agency can actually defend later, whether that’s to an auditor, a records requester, or your own leadership a year from now.
Automation Is a Journey, Not a Launch Day
Nobody automates everything overnight. The agencies with the strongest AI results usually didn’t start with AI at all. They started by automating a manual process years earlier, refining it, and only later layering AI on top of a foundation that already worked. There’s always a next step. That’s not a flaw in the plan. It’s how sustainable automation actually happens.
Washington State Housing Finance Commission is a good example of this kind of gradual, policy-backed approach, building an AI policy first and using it to guide a specific, well-scoped project rather than a sweeping overhaul. You can read the full case study here to see how that played out.
If you’re just getting started, resist the urge to solve everything at once. Pick one process. Prove it works. Expand from there.
Get More Good Days Done With AI That Earns Its Keep
AI doesn’t need to be the headline. It needs to be useful, tested, and backed by a policy your team actually trusts. When it’s grounded that way, it stops being hype and starts being one more way to cut the risk, skip the busywork, and get more good days done.