Executives are no longer asking “Should we use AI?”

They’re asking:

• “How do we control this?”
• “What’s going to keep us out of trouble with regulators and the board?”
• “How do we get value from AI without letting it run wild across the business?”

Two names come up fast in those conversations:

• NIST AI Risk Management Framework (AI RMF)
• ISO/IEC 42001:2023 – AI Management System (AIMS)

They sound similar. They are not the same.

This guide breaks down the difference in plain English and shows how you can use both to standardize, de-risk, and govern AI across your organization—without building a monster bureaucracy.

Why AI Governance Standards Matter to Leadership

AI is now in your:

• Office suite (Copilot, Google Workspace add-ons)
• CRM and ERP
• Vendor platforms and SaaS stack
• Shadow tools your teams are quietly using

With that comes risk:

• Biased or unsafe outputs
• Privacy and security incidents
• Regulatory non-compliance
• Bad decisions made on opaque models
• Brand and board exposure

NIST AI RMF and ISO/IEC 42001 exist to put structure around that chaos:

• They give you a shared language for AI risk.
• They give your teams guardrails instead of random experiments.
• They give your auditors and regulators a story they can trust.

What Is the NIST AI Risk Management Framework (AI RMF)?

The NIST AI RMF is a voluntary guidance published by the U.S. National Institute of Standards and Technology in 2023. It’s designed to help organizations identify, assess, and manage AI risks and build “trustworthy AI.” NIST+1

Key points executives should know:

• It’s non-regulatory and voluntary, but widely referenced by industry and regulators.
• It defines 7 characteristics of trustworthy AI: valid and reliable, safe, secure and resilient, accountable and transparent, explainable and interpretable, privacy-enhanced, and fair with harmful bias managed. NIST Publications+1
• It’s built around 4 core functions:
  • GOVERN – culture, roles, policies, oversight
  • MAP – understand context, stakeholders, and risk surface
  • MEASURE – evaluate risk, performance, and trustworthy AI characteristics
  • MANAGE – treat and monitor risk over time NIST Publications+1

Think of NIST AI RMF as:

A risk and governance playbook.
It tells you what good looks like for AI risk management and how to structure decision-making.

It does not give you a certificate. There’s no “NIST AI RMF Certified” stamp. It’s about principles, processes, and practices, not an external audit.

What Is ISO/IEC 42001:2023?

ISO/IEC 42001 is the world’s first certifiable AI Management System standard (AIMS), published in December 2023. ISO+1

At a high level:

• It defines requirements for an AI Management System: the policies, objectives, and processes needed to govern AI across its lifecycle (design, development, deployment, monitoring, retirement). ISO+1
• It follows the classic ISO Plan–Do–Check–Act model, similar to ISO 9001 (quality) and ISO 27001 (information security). Microsoft Learn+1
• It covers:
  • AI risk management and impact assessment
  • Lifecycle management of AI systems
  • Roles, responsibilities, and competence
  • Supplier and third-party oversight
  • Continuous improvement of your AI program KPMG+1

The critical difference:

ISO/IEC 42001 is a management system standard you can be audited and certified against. BSI

It’s designed for any organization that develops, deploys, or uses AI systems, and helps you demonstrate to customers, regulators, and partners that your AI operations are governed in a structured, repeatable, and auditable way. Microsoft Learn+1

NIST AI RMF vs ISO/IEC 42001 at a Glance

The key takeaway:

• NIST AI RMF gives you language and structure for risk.
• ISO/IEC 42001 gives you requirements and evidence for governance.

They’re complementary, not competing.

How They Work Together in a Real Organization

A pragmatic way to think about it:

1. NIST AI RMF = “How we think about AI risk.”
   • Gives leadership and risk teams a common framework to categorize, measure, and manage AI risk across portfolios. NIST Publications+1
2. ISO/IEC 42001 = “How we systematize and prove it.”
   • Forces you to embed that risk thinking into policies, roles, procedures, training, control testing, and continuous improvement – all auditable. A-LIGN+1
3. Other standards = “Domain specifics.”
   • ISO 27001, SOC 2, HIPAA, PCI, CMS marketing rules, etc. continue to handle domain-specific security, privacy, and industry obligations. ISO 42001 gives the AI layer that sits on top and plugs into them. A-LIGN+1

For executives, that translates into:

• A coherent governance story for the board and regulators.
• A standardized way to evaluate AI vendors and internal use cases.
• A way to keep “quick experiments” from becoming unmanaged production risk.

Which Should You Start With?

Short answer: it depends on your current maturity and pressure from regulators/customers.

1. “We’re piloting AI and need to avoid unforced errors.”

• Start with NIST AI RMF.
• Use it to:
  • Define what “trustworthy AI” means for your organization.
  • Stand up basic Govern → Map → Measure → Manage processes for high-impact use cases (e.g., customer-facing chat, underwriting, hiring, clinical decision support).
• Once those patterns are working, move selectively toward ISO 42001 for the most critical areas.

2. “We sell into enterprises or regulated sectors and need proof.”

• You will eventually want ISO/IEC 42001.
• NIST AI RMF will help structure your risk program, but CIOs, CISOs, and procurement teams are beginning to look for certifiable evidence that AI is governed in a repeatable, auditable way. EY+1
• A roadmap approach:
  • Align to NIST AI RMF to get practices in place.
  • Build your AI Management System to ISO 42001 and run internal audits.
  • Pursue certification when you’re ready.

3. “We already have ISO 27001 / SOC 2 and a risk team.”

• You’re in a strong position to layer NIST AI RMF and ISO 42001 onto existing structures.
• Much of what ISO 42001 expects (risk registers, corrective actions, management review, internal audit) already exists in your security or quality programs; you extend it to cover AI lifecycle and models. A-LIGN+1

A Practical Roadmap for Executives

Here’s a simple sequence you can hand to your team.

Step 1: Take Inventory and Label Risk

• Build a register of AI systems: internal tools, vendor AI, embedded features (Copilot, CRM/ERP AI add-ons, etc.).
• For each, identify:
  • Business owner
  • Data used (including sensitive/regulated)
  • Impacted stakeholders
  • Risk level (low / medium / high) based on NIST AI RMF guidance. NIST Publications+1

Step 2: Stand Up Lightweight NIST AI RMF Practices

For your high-risk AI systems:

• GOVERN – define an AI governance committee, decision rights, and escalation paths.
• MAP – document purpose, context, and potential harms.
• MEASURE – define what “good” looks like: fairness metrics, accuracy, robustness, incident thresholds.
• MANAGE – decide on mitigations, human-in-the-loop requirements, monitoring cadence, and incident response. AuditBoard+1

Don’t overcomplicate this at first. Aim for standardization, not perfection.

Step 3: Build an AI Management System Aligned to ISO/IEC 42001

Once the basics work:

• Translate your current practices into policies, procedures, and records that map to ISO 42001 requirements:
  • Context of the organization and interested parties
  • Leadership commitments and AI policy
  • Roles, competencies, and training
  • AI risk assessment and treatment
  • Lifecycle controls (design, data, validation, deployment, monitoring, retirement)
  • Supplier and third-party controls Microsoft Learn+1
• Integrate with existing ISMS/QMS where possible instead of creating parallel structures.

Step 4: Make It Measurable

Executives care about payback period and measurable ROI, not just documentation.

Add metrics like:

• Reduction in AI-related incidents or escalations.
• Time recovered by replacing ad-hoc manual review with standardized controls.
• Time to respond to regulatory or customer AI questionnaires.
• % of AI systems fully covered by governance vs. unmanaged.

Your AI governance program should recover time for your teams, not consume it.

Step 5: Run Internal Audits and Decide on Certification

• Conduct internal readiness assessments against ISO/IEC 42001.
• Close gaps and streamline processes where friction is high.
• Decide whether to pursue external certification based on:
  • Customer demand
  • Regulatory expectations
  • Strategic positioning versus competitors EY+1

Executive Checklist: 10 Questions to Ask Your Team

You can use these straight in your next leadership meeting:

1. Do we have a single inventory of AI systems across the business?
2. For each high-impact AI system, can we clearly explain purpose, data, risk, and owner?
3. Who is accountable for AI risk decisions today? Is it written down?
4. Are we using the NIST AI RMF (or similar) to consistently Map, Measure, and Manage AI risks?
5. Do we have documented policies and procedures for AI lifecycle management (not just one-off guidelines)?
6. How do we evaluate AI vendors and embedded AI features against our governance requirements?
7. Can we demonstrate to a regulator, auditor, or large customer that our AI use is governed and monitored?
8. Where do employees go with AI incidents or concerns? Is that process clear?
9. Are we tracking time saved and risk reduced from AI governance, or is this just cost center overhead?
10. Do we have a roadmap toward an AI Management System aligned with ISO/IEC 42001 and our existing ISO 27001 / SOC 2 programs?

If your team can’t answer these confidently, you have work to do—and that’s exactly where outside help pays off.

How Heed AI Solutions Can Help

Most teams don’t have spare cycles to design all of this from scratch. They’re already stretched.

That’s where we come in.

At Heed AI Solutions, we focus on governance-first AI implementations that are:

• Aligned with NIST AI RMF for risk language and decision structure.
• Built to be ISO/IEC 42001-ready so you’re audit-ready instead of playing catch-up later.
• Designed to recover 10–25 hours per week through intelligent automation while staying inside the lines.

Typical engagements:

1. AI Governance & Adoption Blueprint
   • Rapid assessment of your current AI use, risks, and controls.
   • Alignment to NIST AI RMF.
   • Clear roadmap for ISO 42001-aligned governance across your key workflows.
2. Implementation Sprint (Pilot → Controls in Production)
   • Stand up governance for 1–3 high-impact AI systems (e.g., Copilot, underwriting agents, marketing agents).
   • Put in the policies, workflows, and logging your auditors will ask for.
3. Managed AI Governance & Time Recovery
   • Ongoing support to keep your AI stack governed, monitored, and tuned.
   • Regular reporting your CFO, COO, and CISO can actually read.

Next Step

If you’re an executive looking at AI and thinking “We need the upside without losing control”, this is the moment to standardize your approach.

Book a short AI Governance Strategy Call and we’ll walk through:

• Where you are today
• How NIST AI RMF and ISO/IEC 42001 fit your context
• What a 90-day, low-disruption path to an audit-ready, governed AI program looks like for your organization

You don’t need a giant AI program to be safe.
You need clear standards, a practical roadmap, and someone who’s done this before.