A Roadmap for Responsible AI in Government
The public sector doesn’t need to chase AI—it needs to wield its power.
AI is already embedded in platforms used for procurement, resident service requests, and internal workflows. And yet, not all agencies have a clear view of how these capabilities are being introduced or a plan to manage the risks they bring.
This final blog in BRONNER’s AI series offers a practical roadmap for responsible adoption. If an agency is using AI—or it thinks AI may be in use—it’s time to ask sharper questions and build smarter guardrails.
Step One: Know What You’re Using
AI often enters through the backdoor: bundled into cloud platforms, productivity tools, or enterprise systems. Start by mapping where AI is already present across systems for finance, HR, housing, or customer service. Understand what the technology does, who it affects, and whether oversight is in place.
Step Two: Adopt a Risk Framework
Public agencies don’t need to reinvent the wheel. The NIST AI Risk Management Framework provides a strong foundation for cross-department alignment and risk identification. Key issues to watch for:
Bias in data or outcomes
Opaque decision-making logic
Gaps in human accountability
Cyber risks from connected systems
Overpromising vendors or black-box tools
Bringing IT, legal, operations, and leadership together around shared definitions and frameworks is a critical early move.
Step Three: Tighten Data Governance
AI relies on data—and that means data quality, privacy, and ownership matter more than ever. Review who owns the data that AI systems use, how PII is protected, and whether legacy processes need to be modernized before AI is layered on top.
AI doesn’t fix bad processes. It scales them.
Step Four: Pressure-Test Your Vendors
Ask pointed questions when evaluating or renewing software tools:
Where does your training data come from?
Can we audit or override system outputs?
What guardrails exist to detect or prevent bias?
Who is liable for errors or harm?
AI vendors may pitch turnkey solutions. Public agencies need to be the adults in the room, especially when resident outcomes or civil rights are at stake.
Step Five: Pilot Low-Risk, High-Learning Use Cases
Start where the stakes are lower, and the gains are clear. Examples include:
Summarizing grant opportunities and drafting applications
Flagging procurement anomalies
Classifying constituent service requests
These pilots allow agencies to refine oversight practices and build confidence before expanding to more sensitive applications.
From Strategy to Stewardship
AI in government doesn’t need to start with bold bets. Thoughtful application of AI starts with curiosity, clarity, and cross-functional leadership. The agencies that succeed will not be the ones that deploy AI fastest—they will be the ones that align its use to mission and govern its deployment across operational uses.
Public trust, fiscal stewardship, and service equity are all at stake. Building a foundation now ensures that as AI evolves, public agencies are empowered to evolve with it on their own terms. BRONNER is here to help.