Federal AI Governance: How To Get It Right
AI is moving fast—and for many federal agencies, governance is struggling to keep up. That uncertainty slows innovation and makes it harder for organizations to move forward with confidence. But the solution isn’t to pump the brakes—it’s to put the right guardrails in place. The federal government agrees, as demonstrated in recent OMB Memos.
Scaling artificial intelligence (AI) successfully demands governance that does more than check boxes. It must be adaptive, practical, and evolve alongside the technology itself. When done well, AI governance isn’t just about compliance—it’s a catalyst. It accelerates smart deployment, builds stakeholder trust, and ensures that innovation happens responsibly.
Effective AI Governance
Effective AI governance is key to accelerated innovation as it empowers professionals at all levels to align processes, establish clear policies, and foster accountability while reducing unnecessary barriers to AI adoption. The future of AI belongs to those who can govern it wisely, creating frameworks that are right-sized to the risk, built on proven business practices, and flexible enough to meet emerging needs—without slowing down the pace of progress.
Key Principles for AI Governance
Scaling and deploying AI at speed requires governance that protects and provides confidence. While the technology and its usage introduce new and unknown challenges, overly rigid policies can stifle experimentation and limit AI’s impact.
Here are some key principles for AI governance that fuels innovation and drives trust:
- AI risks aren’t equal, so stop treating them that way: AI governance should be tiered. A chatbot generating FAQs and an AI system approving federal grants do not require the same level of oversight. Organizations should apply risk registers to map the likelihood versus severity and govern accordingly.
- Build from what already works: AI governance does not need to start from scratch. Instead of re-inventing the wheel, adapt frameworks like the National Institute for Standards and Technology’s AI Risk Management Framework as the starting point. This includes performing a gap analysis to assess unmet needs, tailoring the identified framework to address the most relevant risks, integrating governance actions into existing processes, and building awareness through training.
- Transparency that benefits teams as much as the organization: Explainability shouldn’t be a compliance checkbox; it should be a tool for continuous learning and improvement. Model documentation and decision logs should help teams refine AI and satisfy regulators.
- Use governance to go faster: The best governance models act as growth levers, streamlining approvals and reducing internal friction, allowing organizations to deploy AI faster while maintaining security, fairness, and accountability.
The Goal of Smart-Scale Governance
The goal should be “smart-scale” or “right-sized” governance that is targeted, efficient, and risk-adjusted, with no wasted effort or overreach. AI governance should protect and help organizations approach innovation with responsibility and purpose. The right approach ensures organizations achieve the benefits of AI quickly while scaling confidently—enabling agencies to focus on what truly matters.
Next Steps for Organizations
To implement effective AI governance, organizations should:
- Assess your governance practices to identify inefficiencies, gaps, and risks.
- Develop an adaptive, right-sized AI governance framework that complies with current OMB guidance and tailors existing frameworks to organizational needs. Engage stakeholders, encourage robust feedback, and establish performance assessments.
- Consider establishing an AI Governance Board or team to continuously monitor and quickly adapt as AI capabilities evolve. This board will comprise stakeholders and can help ensure that internal and external needs are met.
By aligning oversight with strategy and regulation with innovation, organizations can make smarter decisions, faster.
