AI is reshaping industries, but it’s also drawing unprecedented attention from policymakers. U.S. federal agencies introduced 59 AI-related regulations in 2024, and legislative mentions of AI have increased by 21% across 75 countries since 2023.1 Keeping up with policy changes will require organizations to adopt frameworks that ensure AI is both effective and compliant.
AI governance provides this structure. It outlines clear policies, roles, and controls to guide how a business will develop and use AI. Without it, businesses risk falling behind regulatory expectations or adopting tools that erode customer trust.
At TMC, we help organizations build AI governance frameworks that balance innovation with responsibility. Read on to learn what AI governance means, why it matters, and which best practices will help your organization adopt AI responsibly.
What Is AI Governance?
AI governance refers to the processes and controls that guide how an organization adopts AI. It ensures AI is used ethically while providing a consistent framework for data handling, risk management, and accountability.
Effective AI governance isn’t just about limiting risk; it also builds trust by establishing clear expectations for how your business will use AI tools. These expectations are grounded in a set of principles that provide the foundation for any governance model, which we’ll explore below.
AI Governance Principles
While your governance model should be tailored to your organization’s operations and goals, there are a few common principles you can use to form the foundation.
- Transparency: AI decisions and data usage must be explainable and auditable.
- Accountability: Clear roles should be assigned to ensure oversight across all AI projects.
- Security: Security measures like access controls and risk assessments help protect against misuse.
- Fairness: Governance policies need to address potential bias in data and AI models.
- Compliance: Frameworks should align with regulatory requirements and industry standards.
At TMC, we integrate these principles into our AI transformation consulting to help organizations establish an ethical foundation before scaling AI initiatives. We focus on your people, processes, and technology to ensure your adoption is responsible and sustainable.
Why Is AI Governance Important?
AI opens doors to new opportunities, but it also creates risks if not managed carefully. IBM’s 2025 Cost of a Data Breach Report found that 97% of organizations have experienced an AI-related security incident, but 63% still don’t have formal governance policies in place to guide how AI is managed.2 That gap leaves businesses exposed to potential compliance violations, data breaches, and even inconsistent practices across teams.
Currently, laws like GDPR and HIPAA set strict standards for how businesses can handle data – and new AI-specific regulations are emerging all the time. Falling short of these standards can mean not just paying fines, but also suffering reputational damage if customers lose trust in how you’re handling their data.
Strong governance gives organizations a way to get ahead of these challenges. It ensures sensitive data is protected, compliance requirements are met, and AI models are monitored for issues like bias or accuracy.
What AI Governance Challenges Do Businesses Face?
The benefits are clear, but building an effective governance framework isn’t always simple. You may face challenges like:
Data Readiness Gaps
Many organizations have unstructured, unclassified, or siloed data that limits their ability to safely deploy AI.
Lack of Standards
As AI regulations evolve, businesses may lack a clear reference point for developing frameworks.
Resource Constraints
Internal IT and compliance teams may not have the bandwidth to manage AI new governance processes alongside their existing responsibilities.
Shadow AI
A 2025 survey by Varonis revealed that 98% of employees use unsanctioned applications.3 These AI tools operate without IT or compliance oversight, which can create security and data privacy risks.
5 AI Governance Best Practices
Ready to build an AI governance framework for your business? Here are a few best practices we recommend keeping in mind:
1. Establish Policies and Roles
First, you need to define acceptable use policies for AI and set up a governance committee to ensure oversight. Assigning clear responsibilities at the start of your AI initiatives can help you avoid fragmented decision-making and create accountability across business units.
2. Conduct Risk Assessments
Determine where AI can help improve your operations most, while assessing risks like model bias and poor data quality. These assessments will provide a foundation for prioritizing your AI projects and allocating resources effectively.
3. Ensure Data Readiness
Data is central to AI, which means organizations need processes for data classification, access, and protection to ensure its integrity and reduce the risk of exposure or misuse.
4. Build Private and Secure AI Environments
Secure enclaves or private large language models (LLMs) can help you reduce risk while scaling AI across your operations. Conduct security assessments, penetration testing, and ROI analysis to make sure your AI projects stay safe and financially sound.
5. Continuously Monitor and Adapt
AI governance shouldn’t be static. Make sure to perform reviews and audits regularly to keep your frameworks aligned with changing regulations, technologies, and organizational goals.
Simplify AI Governance With TMC
At TMC, we combine our decades of governance expertise with transformation strategy to guide clients through our proprietary 12-Step AI Transformation Roadmap. Built on the People, Process, and Technology methodology, our roadmap ensures your AI adoption:
- Aligns your people and process from the start with accountability, transparency, and trust built into every AI initiative.
- Closes compliance gaps and creates a trusted foundation that accelerates safe AI enablement across your organization.
- Provides ongoing value by setting measurable KPIs tied to your business goals and tracking AI performance against them.
As vendor-neutral consultants, our only agenda is enabling responsible innovation – not selling technology. Our experts work with you to deliver the right governance and transformation strategies for your environment, so you can rest easy knowing your AI initiatives will deliver measurable outcomes while protecting your brand and data.
Ready to build an AI governance framework that lets you innovate responsibly? Reach out to TMC today to get started.
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