Original broadcast 10/15/25
Presented by ServiceNow
Federal agencies are accelerating their adoption of artificial intelligence to improve efficiency, streamline mission delivery, and empower the workforce. Yet challenges remain around preparing fragmented data, establishing effective governance, and managing risk. Intelligent Government: Smart Strategies to Accelerate AI Innovation, presented by Fed Gov Today and ServiceNow, brings together government and industry leaders to share strategies for making AI investments deliver real outcomes. Moderator Francis Rose guides conversations with senior executives from across government and ServiceNow as they explore how to prepare data, strengthen governance, and responsibly scale AI innovation for mission success.
From Hype to Impact: Making AI Deliver Real Outcomes in Government
Speakers:
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Mike Hurt, Group Vice President, U.S. Public Sector, ServiceNow
Mike Hurt explains how federal agencies can unlock the full value of AI by tightly connecting data and workflows to mission outcomes. He outlines why good governance and high-quality data are essential for success and warns against implementing AI “for AI’s sake.” Hurt also highlights how centralized governance structures, such as AI councils, help agencies scale innovation while maintaining accountability.
Key Takeaways:
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AI effectiveness depends on clean, accessible, well-governed data.
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Agencies should target high-value processes for AI, not just chase technology trends.
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Establishing AI councils and centralized platforms helps agencies manage models and mitigate risk.
Breaking Down Data Silos — and the Culture That Created Them
Speakers:
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Taka Ariga, Former Chief Data & AI Officer, Office of Personnel Management
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Patrick McGarry, Federal Chief Data Officer, ServiceNow
Taka Ariga and Patrick McGarry explore how agencies can overcome cultural and technical barriers to prepare data for AI. Ariga shares his experience leading agile governance at OPM and GAO, emphasizing the importance of trust, user experience, and iterative decision-making. McGarry describes the challenge of data fragmentation across government and underscores that cultural collaboration—not just technical solutions—is the first step toward interoperability and AI readiness.
Key Takeaways:
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Data readiness is as much about cultural change as it is about technical integration.
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Agile governance models allow organizations to innovate at mission speed.
- Agencies should avoid “analysis paralysis” and keep focus on mission-driven outcomes.
How the Defense Logistics Agency Is Turning AI Into a Force Multiplier
Speakers:
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Adarryl Roberts, Chief Information Officer, Defense Logistics Agency
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Jonathan Alboum, Federal Chief Technology Officer, ServiceNow; Former CIO, USDA
Adarryl Roberts details how the Defense Logistics Agency is using AI to improve supply chain readiness, procurement, and workforce effectiveness through its AI Center of Excellence. He stresses the importance of treating AI as a workforce multiplier and ensuring leadership is trained to understand the technology. Jonathan Alboum adds that successful adoption requires aligning AI directly to mission outcomes and democratizing its use across the enterprise, rather than centralizing it solely under IT leadership.
Key Takeaways:
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Start with mission priorities, not technology, to identify valuable AI use cases.
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Workforce training and upskilling are essential to sustain AI adoption.
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Democratizing AI usage across agencies fosters innovation and accelerates outcomes.
Turning Risk Into Reward — Building Trustworthy AI in Government
Speakers:
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Martin Stanley, AI RMF Lead, National Institute of Standards and Technology
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Melissa McGriff, Vice President of Federal, ServiceNow
Martin Stanley outlines the NIST AI Risk Management Framework and explains how agencies can balance innovation with accountability by embedding AI into enterprise risk programs. He stresses that risk management must be contextual to specific use cases and should focus on taking the right amount of risk to achieve mission benefits. Melissa McGriff emphasizes that effective governance is the linchpin of successful AI programs and that collaboration between agencies and industry ensures compliance while enabling agility.
Key Takeaways:
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AI governance should balance innovation with risk management, not eliminate all risk.
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Risk management approaches must adapt to different AI applications and mission contexts.
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Strong governance and continuous monitoring ensure that AI delivers measurable mission outcomes.