Mapping AI Adoption in Business and Government to NIST Standards
Created By: Zack Huhn, Co-Founder, Enterprise Technology Association
AI adoption across business and government is accelerating. As artificial intelligence moves from experimental pilots to enterprise systems and mission-critical infrastructure, leaders are under increasing pressure to ensure these technologies are trustworthy, secure, and aligned with societal and regulatory expectations.
The National Institute of Standards and Technology (NIST) offers a powerful toolkit for organizations navigating this transformation. The NIST AI Risk Management Framework (AI RMF) provides a structured, voluntary guidance framework for mapping AI use cases to risk governance strategies. Let’s break down how organizations can align the various stages of AI adoption with NIST’s frameworks, supporting innovation while upholding public trust.
A Call for Aligned AI Governance
Businesses are deploying AI to increase efficiency, make better decisions, and uncover new value. Governments are applying AI to enhance service delivery, modernize infrastructure, and improve outcomes for citizens. Across both sectors, the challenge is no longer whether to use AI, but how to use it responsibly.
This moment demands more than hype or experimentation. It calls for clear principles, coordinated strategies, and a shared language between builders, buyers, and regulators. That’s where NIST comes in.
The NIST AI Risk Management Framework
The AI RMF, introduced in 2023, is designed to help organizations manage the risks associated with artificial intelligence systems throughout their lifecycle. It is non-binding, voluntary, and applicable across industries.
The framework organizes AI governance into four core functions:
Map: Understand the context in which an AI system is developed and used. This includes identifying intended purposes, stakeholders, and potential risks.
Measure: Analyze and assess those risks, including technical performance, bias, explainability, and robustness.
Manage: Prioritize and implement risk mitigation strategies, allocate resources, and take corrective actions when necessary.
Govern: Establish internal structures, policies, and accountability systems to ensure oversight and alignment with organizational values.
These functions are intended to be iterative and adaptable, supporting both small pilot projects and large-scale enterprise systems.
Aligning AI Adoption Stages with NIST Guidance
AI adoption typically unfolds in stages—beginning with exploration and strategy, progressing through pilot development and implementation, and ultimately maturing into a governed and scalable capability. Each stage introduces new considerations and risks, which can be effectively mapped to specific NIST functions and standards.
During the exploration phase, organizations are focused on identifying potential AI use cases, defining objectives, and assessing the risks of implementation. This is where the “Map” and “Govern” functions of the AI RMF are most valuable. Organizations should establish internal governance mechanisms, clarify roles and responsibilities, and begin contextual analysis of data sources, system boundaries, and ethical implications.
As organizations move into pilot testing and development, they begin building and testing AI models. This phase requires a closer focus on the “Measure” function. It involves evaluating model performance, detecting and mitigating bias, and assessing explainability and robustness. NIST’s guidance on managing bias in AI, along with its systems engineering and cybersecurity standards, become particularly relevant here.
Once AI systems are ready for deployment, leaders must turn to operational risk management. This is the heart of the “Manage” function. Organizations must monitor performance in real-world conditions, respond to emerging risks, implement ongoing privacy safeguards, and align with broader enterprise risk management protocols. At this stage, privacy frameworks and secure software development principles should guide decisions.
In the final phase, where AI becomes embedded and scaled across the enterprise or agency, governance maturity becomes essential. Organizations should adopt formal maturity models, conduct internal audits, and align their activities with regulatory guidance, especially in government contexts. The NIST tiers for AI risk management help organizations assess and improve their posture over time.
AI Governance in the Public Sector
Public agencies face unique challenges—and responsibilities—when deploying AI. Citizens expect transparency, fairness, and accountability in government use of emerging technology. That means public sector leaders must proactively align their AI systems with federal guidelines and principles.
Recent directives, including Executive Order 14110 on AI Safety and Trust, OMB Memorandum M-21-06 on agency AI governance, and the White House’s Blueprint for an AI Bill of Rights, all reinforce the same imperative: public sector AI must be lawful, effective, ethical, and safe.
NIST plays a central role in enabling this transformation. By following the AI RMF and related guidance, government agencies can meet rising expectations and ensure AI systems serve the public interest.
A Practical Example: AI in Local Government
Consider a city government that introduces an AI-powered system to streamline its permit application process. The city begins by mapping the system’s purpose, stakeholders, and impact, establishing clear governance from the start. In the pilot phase, it tests the model on historical data, measuring for accuracy, speed, and bias.
As the system is rolled out, it is monitored for performance and continuously improved based on feedback. Privacy controls are strengthened to protect applicant data. Ultimately, the city formalizes its AI oversight process, conducts audits, and scales the solution to other departments.
By mapping each step of the AI lifecycle to the NIST framework, the city enhances both service delivery and public trus, without compromising ethical, legal, or operational standards.
Strategic Recommendations
For business and government leaders seeking to adopt AI in alignment with NIST standards, the path forward is clear:
Start with governance. Build internal structures, assign accountability, and develop an AI use policy before investing in tools or models.
Map before you build. Define your goals, stakeholders, constraints, and potential risks early in the process.
Measure what matters. Use available tools to assess bias, explainability, fairness, and robustness—especially during development and deployment.
Manage risks proactively. Implement monitoring, incident response, and feedback mechanisms throughout the AI lifecycle.
Build governance maturity over time. Use the NIST tier model to benchmark progress and continuously evolve your oversight strategy.
Require alignment from vendors. Demand that third-party AI providers adhere to NIST-aligned development practices and risk controls.
Trust and Innovation Can Work Together
The adoption of artificial intelligence is a cultural, ethical, and operational transformation. By aligning AI initiatives with the NIST Risk Management Framework and related guidance, organizations can accelerate innovation while maintaining trust, accountability, and control.
The path forward is not about choosing between speed and safety. It’s about designing systems—and strategies—that do both.
About the Author
Zack Huhn is cofounder of the Enterprise Technology Association, a national coalition of leaders advancing the responsible development and deployment of emerging technologies; working across sectors to create shared value through innovation, security, and collaboration.
About the Enterprise Technology Association
The Enterprise Technology Association helps professionals and leaders navigate what’s now and what’s next in technology. Through education, events, partnerships, and a national network of advisors and solution partners, ETA is building the trusted infrastructure for emerging technology adoption across the United States.
