Original broadcast 10/15/25
Artificial intelligence has become a cornerstone of government modernization. But for many agencies, realizing its full value still depends on one critical factor — data. As agencies experiment with generative AI and advanced automation, the question isn’t whether they can deploy AI, but whether their data and workflows are prepared to support it.
That’s where Mike Hurt, Group Vice President for U.S. Public Sector at ServiceNow, says the real work begins. “AI is only as good as the data you feed it,” he explains. “If the data is siloed, outdated, or poorly governed, you’ll never get the results you’re hoping for.”
According to Hurt, too many agencies still treat AI as a stand-alone technology project rather than as part of a broader mission strategy. The agencies that are getting it right, he says, are connecting their data, automation, and workflow management tools into an integrated ecosystem that drives measurable outcomes.
“Workflow is what turns predictive insights into operational results,” Hurt says. “When agencies can unify data across thousands of systems and apply automation strategically, that’s when AI becomes a real accelerator for mission delivery.”
Hurt points to the example of a customer that managed more than 3,000 applications across its enterprise — each one creating isolated data stores. Rather than moving everything into massive data lakes, which can add complexity and cost, agencies can use modern platforms like ServiceNow’s Data Fabric to connect disparate systems without duplicating information. This “zero-copy” approach allows data to be accessed and analyzed seamlessly, creating what Hurt calls “AI-ready data.”
But building the technical foundation is only part of the solution. Governance, Hurt says, is the multiplier that turns AI experiments into sustained success. “We’re seeing agencies that establish AI councils or similar governance bodies have a lot more success,” he notes. “They’re setting standards, sharing lessons, and ensuring that AI projects align with organizational priorities.”
That alignment also helps agencies avoid one of the most common pitfalls in AI adoption — deploying tools simply because they’re new. “AI for AI’s sake” can create more complexity than it solves. Hurt advises agencies to start small, use process mining to identify where automation will deliver the most value, and then scale those solutions.
“It’s about focusing your efforts where they matter most,” he says. “When agencies choose high-impact processes and apply AI with a clear mission purpose, they see measurable gains in efficiency, service delivery, and ROI.”
Ultimately, Hurt sees AI, data, and workflow not as separate initiatives but as interdependent drivers of digital transformation. “When they’re connected,” he says, “agencies can move from reactive operations to proactive mission execution. That’s the real promise of intelligent government.”
Key Takeaways
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AI success depends on high-quality, connected, and governed data.
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Establishing AI governance structures helps agencies scale responsibly.
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Start with high-value processes and clear mission outcomes before deploying AI.
This interview was part of Intelligent Government: Smart Strategies to Accelerate AI Innovation presented by ServiceNow. Click here to watch the entire program.