Insights from Federal data, legal and technology leaders on turning AI potential into mission-driven action
Everyone鈥檚 talking about AI. But in Government, where budgets are tight, oversight is strict and the stakes are high, talk isn鈥檛 enough. Agencies need AI use cases that solve real problems, not just generate headlines.
At a recent panel discussion in D.C. hosted by ZL Tech and 探花视频, experts from data, legal and tech roles shared their insights on how Federal agencies can move from experimentation to impact. Their message was clear: success with AI starts with governance, strategy and the right people at the table.
1. Want Real AI? Start at the Top
The biggest challenge agencies face? Starting small and remaining siloed.
鈥淪tart at the highest, most strategic level of the organization,鈥� said Matthew Versaggi, a White House Presidential Innovation Fellow for AI. 鈥淒on鈥檛 begin in your own department, by then it鈥檚 too narrow. Instead, ask: what鈥檚 the most impactful agency-wide use case we can build toward?鈥�
The panelists emphasized that departmental pain points might improve workflows, but agency-wide pain points tied to the mission are where AI can truly move the needle.
鈥淲ithout a structured process, you鈥檙e just chasing your tail,鈥� added Kon Leong, CEO of ZL Tech. 鈥淪tart small, but make sure your experiment is scalable and aligned to long-term strategy.鈥�
2. Governance Isn鈥檛 a Roadblock. It鈥檚 the Roadmap.
AI can鈥檛 succeed without trust in the data. And trust depends on governance.
鈥淕overnance is accountability,鈥� said Leong. 鈥淚t鈥檚 what separates scalable, sustainable innovation from science experiments.鈥�
Jason Baron, a professor and former senior Government attorney, described governance as a mesh, not a silo: 鈥淭rue governance links your CISO, CIO, records officers, FOIA leads, legal teams鈥攁ll under shared policy and ownership. We used to work in silos. That has to end.鈥�
And as Matthew pointed out, AI governance isn鈥檛 a blocker, it鈥檚 an enabler: 鈥淎I governance becomes the mechanism for sustaining innovation. If we鈥檙e going to compete globally, we have to embrace it.鈥�
3. Talk to Your CDO鈥擸es, You Have One
One of the most actionable takeaways: if you鈥檙e not already talking to your Chief Data Officer, you鈥檙e behind.
鈥淓very agency has a CDO,鈥� said Jason. 鈥淕o find them. Hopefully you like them. Have a conversation.鈥�
CDOs are uniquely positioned to bridge mission needs with data access and policy. As one attendee noted during the session, 鈥淎wareness is the first step. Records and governance leaders are finally getting a seat at the table.鈥�
It鈥檚 no longer enough for legal, records and privacy teams to operate in isolation. Building AI responsibly requires alignment鈥攁nd that starts with the CDO.
4. Unstructured Data Is the Game-Changer
Structured data, like spreadsheets and databases, has been the traditional foundation for reporting and analytics. But that鈥檚 not where the majority of Government data lives.
鈥淯nstructured data is radioactive,鈥� said Leong. 鈥淭hat鈥檚 where every crisis lives. And now, it鈥檚 center stage in AI.鈥�
Unstructured data includes everything from emails and PDFs to file shares, chat logs and documents. It makes up more than 80% of enterprise data, yet many agencies lack visibility or control over it.
Jason gave a real-world Federal perspective: 鈥淎s a records guy, I鈥檇 take out my watch and wait to see how long it took vendors to say 鈥楩OIA鈥� or 鈥楩edRAMP.鈥� If they don鈥檛 understand the challenges around Federal unstructured data, they鈥檙e not serious.鈥�
5. Use the Impact vs. Effort Matrix to Prioritize Wisely
With hundreds of possible AI use cases, how can agencies filter out distractions and find the ones worth pursuing?
Panelists recommended the Impact vs. Effort Matrix鈥攁 simple yet powerful tool to map use cases by how much effort they require and how much impact they鈥檒l deliver.
What Is the Impact vs. Effort Matrix?
This tool helps agencies focus on what鈥檚 worth doing, especially when time, talent and resources are limited. Each AI idea gets placed into one of four categories:
- Quick Wins (High Impact, Low Effort): Prioritize these immediately.
- Major Projects (High Impact, High Effort): Worth the investment鈥攑lan carefully.
- Fill-Ins (Low Impact, Low Effort): Do when time permits.
- Thankless Tasks (Low Impact, High Effort): Avoid or minimize these.
鈥淲e see hundreds of AI ideas across agencies,鈥� one panelist said. 鈥淏ut when you apply the matrix, only a handful have real traction. The juice has to be worth the squeeze.鈥�
The matrix helps filter noise and ensure teams are spending time on the projects most likely to scale, succeed and support the mission.
6. Build with Scale in Mind, Even If You Start Small
AI is experimental. Not every idea will pan out. But successful projects need a path to grow from day one.
鈥淒o a small test with an enterprise mindset,鈥� said Matthew. 鈥淪ecurity, governance and scale should be built in from the start.鈥�
Leong agreed: 鈥淕et your data ducks in a row, and everything else will follow. You don鈥檛 want to make long-term bets on projects that were never designed to scale.鈥�
7. Custom or Off-the-Shelf? Choose Based on Complexity
Should agencies build custom platforms or adapt off-the-shelf tools? It depends.
鈥淒on鈥檛 overpay for generic tools,鈥� said Matthew. 鈥淏ut for deep, high-end capabilities, you may need in-house builds鈥攋ust know the tradeoffs.鈥�
The more specialized the use case, the more likely a tailored solution is required. But whether buying or building, the panel emphasized the importance of involving records officers, legal teams and SMEs 别补谤濒测鈥�not just the CIO chasing the next shiny object.
Final Thought: The Data Is There. The Champions Are Too.
The core message of the session? Agencies already have the data鈥攁nd they have the people who care about getting it right.
What鈥檚 missing is coordination, prioritization and a strong governance foundation.
Start with strategy. Talk to your CDO. Use the matrix. Build with intent.
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