{"id":11178,"date":"2025-08-13T12:02:05","date_gmt":"2025-08-13T17:02:05","guid":{"rendered":"https:\/\/www.carahsoft.com\/wordpress\/?p=11178"},"modified":"2025-08-13T12:09:09","modified_gmt":"2025-08-13T17:09:09","slug":"zl-tech-from-noise-to-impact-how-agencies-can-build-real-ai-use-cases-blog-2025","status":"publish","type":"post","link":"https:\/\/www.carahsoft.com\/wordpress\/zl-tech-from-noise-to-impact-how-agencies-can-build-real-ai-use-cases-blog-2025\/","title":{"rendered":"From Noise to Impact: How Agencies Can Build Real AI Use Cases"},"content":{"rendered":"\n

Insights from Federal data, legal and technology leaders on turning AI potential into mission-driven action<\/h2>\n\n\n\n

Everyone\u2019s talking about AI. But in Government, where budgets are tight, oversight is strict and the stakes are high, talk isn\u2019t enough. Agencies need AI use cases that solve real problems, not just generate headlines.<\/p>\n\n\n\n

At a recent panel discussion in D.C. hosted by ZL Tech and Carahsoft, 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.<\/p>\n\n\n\n

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1. Want Real AI? Start at the Top<\/h2>\n\n\n\n

The biggest challenge agencies face? Starting small and remaining siloed.<\/p>\n\n\n\n

\u201cStart at the highest, most strategic level of the organization,\u201d said Matthew Versaggi<\/strong>, a White House Presidential Innovation Fellow for AI. \u201cDon\u2019t begin in your own department, by then it\u2019s too narrow. Instead, ask: what\u2019s the most impactful agency-wide use case we can build toward?\u201d<\/p>\n\n\n\n

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.<\/p>\n\n\n\n

\u201cWithout a structured process, you\u2019re just chasing your tail,\u201d added Kon Leong<\/strong>, CEO of ZL Tech. \u201cStart small, but make sure your experiment is scalable and aligned to long-term strategy.\u201d<\/p>\n\n\n\n

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2. Governance Isn\u2019t a Roadblock. It\u2019s the Roadmap.<\/h2>\n\n\n\n

AI can\u2019t succeed without trust in the data. And trust depends on governance.<\/p>\n\n\n\n

\u201cGovernance is accountability,\u201d said Leong<\/strong>. \u201cIt\u2019s what separates scalable, sustainable innovation from science experiments.\u201d<\/p>\n\n\n\n

Jason Baron<\/strong>, a professor and former senior Government attorney, described governance as a mesh, not a silo: \u201cTrue governance links your CISO, CIO, records officers, FOIA leads, legal teams\u2014all under shared policy and ownership. We used to work in silos. That has to end.\u201d<\/p>\n\n\n\n

And as Matthew<\/strong> pointed out, AI governance isn\u2019t a blocker, it\u2019s an enabler: \u201cAI governance becomes the mechanism for sustaining innovation. If we\u2019re going to compete globally, we have to embrace it.\u201d<\/p>\n\n\n\n

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3. Talk to Your CDO\u2014Yes, You Have One<\/h2>\n\n\n
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One of the most actionable takeaways: if you\u2019re not already talking to your Chief Data Officer, you\u2019re behind.<\/p>\n\n\n\n

\u201cEvery agency has a CDO,\u201d said Jason<\/strong>. \u201cGo find them. Hopefully you like them. Have a conversation.\u201d<\/p>\n\n\n\n

CDOs are uniquely positioned to bridge mission needs with data access and policy. As one attendee noted during the session, \u201cAwareness is the first step. Records and governance leaders are finally getting a seat at the table.\u201d<\/p>\n\n\n\n

It\u2019s no longer enough for legal, records and privacy teams to operate in isolation. Building AI responsibly requires alignment\u2014and that starts with the CDO.<\/p>\n\n\n\n

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4. Unstructured Data Is the Game-Changer<\/h2>\n\n\n\n

Structured data, like spreadsheets and databases, has been the traditional foundation for reporting and analytics. But that\u2019s not where the majority of Government data lives.<\/p>\n\n\n\n

\u201cUnstructured data is radioactive,\u201d said Leong<\/strong>. \u201cThat\u2019s where every crisis lives. And now, it\u2019s center stage in AI.\u201d<\/p>\n\n\n\n

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.<\/p>\n\n\n\n

Jason<\/strong> gave a real-world Federal perspective: \u201cAs a records guy, I\u2019d take out my watch and wait to see how long it took vendors to say \u2018FOIA\u2019 or \u2018FedRAMP.\u2019 If they don\u2019t understand the challenges around Federal unstructured data, they\u2019re not serious.\u201d<\/p>\n\n\n\n

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5. Use the Impact vs. Effort Matrix to Prioritize Wisely<\/h2>\n\n\n\n

With hundreds of possible AI use cases, how can agencies filter out distractions and find the ones worth pursuing?<\/p>\n\n\n\n

Panelists recommended the Impact vs. Effort Matrix<\/strong>\u2014a simple yet powerful tool to map use cases by how much effort they require and how much impact they\u2019ll deliver.<\/p>\n\n\n\n

What Is the Impact vs. Effort Matrix?<\/strong><\/p>\n\n\n\n

This tool helps agencies focus on what\u2019s worth doing, especially when time, talent and resources are limited. Each AI idea gets placed into one of four categories:<\/p>\n\n\n\n