Is Your Business Using AI Without Proper Governance? 5 Risks US Companies Can't Ignore

Artificial intelligence is becoming part of everyday business operations. Employees use AI tools to draft communications, analyse information, automate processes and support decisions.

But as AI adoption accelerates, an important question is often overlooked:

Who is responsible for making sure AI is being used safely, appropriately and in line with business requirements?

For many US businesses, AI adoption has moved faster than the development of governance policies, risk assessments and accountability structures.

The result is a growing gap between what AI can do and how effectively organisations manage it.

AI governance does not need to be unnecessarily complicated. However, businesses need to understand where AI is being used, what risks it introduces and who is responsible for managing those risks.

Here are five areas every organisation should examine.

1. Employees Are Using AI Without Company Oversight

AI tools are increasingly accessible. Employees may use ChatGPT, Microsoft Copilot or other AI applications to complete everyday tasks.

Some of this activity may occur without formal approval or visibility from management.

This is commonly referred to as shadow AI.

The concern is not simply that employees are using AI. It is that organisations may not know which tools are being used, what information is being entered into them or how AI-generated outputs influence business decisions.

Why this matters

Uncontrolled AI use can introduce risks involving:

• Confidential company information
• Customer and employee data
• Inaccurate AI-generated content
• Unapproved third-party services
• Inconsistent business decisions

What businesses should do

Start by identifying the AI tools currently used across the organisation.

Create an AI inventory that records the purpose of each system, its owner, the information it processes and the potential risks associated with its use.

This provides a foundation for informed oversight rather than attempting to govern technology that the business cannot see.

2. There Is No Clear AI Governance Policy

Many organisations have cybersecurity, privacy and information security policies.

However, these policies may not adequately address the specific risks associated with artificial intelligence.

An AI governance policy establishes expectations for how AI may be selected, approved, used and monitored.

Without clear guidance, employees may make their own decisions about acceptable AI use.

Why this matters

An absence of documented rules can lead to inconsistent practices, uncertainty about responsibilities and inappropriate handling of sensitive information.

What businesses should do

Develop an AI governance policy that addresses:

• Acceptable and prohibited AI uses
• Approval of new AI tools
• Handling of confidential and personal information
• Human review of important AI-assisted decisions
• Reporting of AI incidents and concerns
• Responsibilities for monitoring and oversight

The policy should reflect the organisation's actual activities and risk exposure rather than simply adopting a generic template.

3. AI Risks Have Not Been Properly Assessed

Traditional technology risk assessments do not always capture the full range of issues associated with AI.

AI systems can introduce risks relating to accuracy, bias, privacy, security, transparency and reliance on automated outputs.

The nature and significance of these risks depend on how the technology is used.

An AI tool helping draft internal meeting notes presents a different risk profile from an AI system supporting recruitment, lending or healthcare decisions.

Why this matters

Without a structured assessment, businesses may overlook high-impact AI uses or spend resources addressing lower-priority concerns.

What businesses should do

Introduce an AI risk assessment process that considers:

• The business purpose of the AI system
• The types of information involved
• Potential effects on customers, employees and other stakeholders
• The importance of human oversight
• Existing safeguards and remaining risks
• Actions required to reduce unacceptable risks

The NIST AI Risk Management Framework (AI RMF) provides a useful voluntary framework for organising AI risk management activities.

The goal is not to eliminate every possible risk. It is to identify, evaluate and manage risks in a way that supports responsible business decisions.

4. Nobody Has Clear Accountability for AI Governance

Who approves the use of a new AI system?

Who evaluates its risks?

Who monitors performance?

And who responds if something goes wrong?

In many organisations, responsibility for AI is spread across IT, legal, compliance, risk management and individual business teams.

Cross-functional involvement is valuable, but unclear ownership can create gaps in oversight.

Why this matters

When responsibilities are not formally assigned, important decisions may be delayed, duplicated or overlooked.

What businesses should do

Establish a practical AI governance structure with defined responsibilities.

Depending on the size of the organisation, this may include:

• Senior management oversight
• A designated AI governance lead
• Business owners for individual AI systems
• Risk, compliance and information security involvement
• Clear escalation and incident-reporting procedures

Smaller businesses do not necessarily need a dedicated AI governance department.

They do need clear decision-making authority and accountability proportionate to their AI activities.

5. US AI Governance Requirements Are Evolving

The United States does not have one single, comprehensive AI governance law that applies uniformly to every business and every AI use.

Instead, organisations may face a combination of federal laws, state requirements, sector-specific obligations and regulatory expectations.

Which requirements matter depends on factors such as location, industry, the type of AI being used and its impact on individuals.

Why this matters

Businesses that assume AI is unregulated may overlook existing legal obligations.

Equally, businesses that assume every AI-related proposal or framework is mandatory may spend resources on requirements that do not apply to them.

What businesses should do

Begin with an applicability assessment.

Identify where the organisation operates, which AI systems it uses and which existing or emerging requirements may be relevant.

Recognised frameworks can also help businesses develop consistent governance practices.

Two particularly useful references are:

• NIST AI RMF: A voluntary framework designed to help organisations manage risks associated with AI.
• ISO/IEC 42001: An international standard specifying requirements for establishing, implementing, maintaining and continually improving an AI management system.

These frameworks can support structured governance, but using them does not automatically establish compliance with every applicable US law.

What Does Effective AI Governance Look Like?

Effective AI governance is not about producing policies that nobody reads.

It is about establishing a practical management approach that helps the organisation understand its AI use, make informed decisions and respond when risks arise.

A useful starting point includes five essential activities:

1. Identify the AI systems and tools currently in use.
2. Assess the risks associated with those systems.
3. Establish clear policies and accountability.
4. Determine which legal and regulatory requirements apply.
5. Develop a prioritised implementation roadmap.

Businesses do not need to solve every governance issue immediately.

They do need to know where their most important gaps are and what to address first.

How GRC Ahead Can Help

Are you confident your business understands and manages its AI risks?

GRC Ahead provides practical AI governance consulting for organisations seeking greater clarity, stronger oversight and a structured approach to responsible AI adoption.

Our AI Governance Readiness Assessment helps businesses:

• Identify existing AI governance gaps
• Review policies, responsibilities and oversight arrangements
• Understand relevant risk and compliance considerations
• Prioritise areas requiring attention
• Develop practical recommendations and next steps

Our approach draws on recognised frameworks, including NIST AI RMF and ISO/IEC 42001, while considering the organisation's business activities and applicable US governance requirements.

Don't wait for an AI incident to discover where your governance gaps are.

Enquire about AI Governance Consulting →

GRC Ahead — Navigate AI Governance with Confidence.

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Disclaimer: This article provides general information about AI governance and risk management. It does not constitute legal advice. Organisations should obtain appropriate professional advice when determining their specific regulatory obligations.