AI Governance isn’t Optional—But it Doesn’t Have to Be a Headache

Picture this: you’re a data architect whose mission is to set up a new AI tool that helps your company understand customer behavior better. The company’s excited about the potential to increase sales. But there’s a catch: you’re faced with a maze of rules, ethical concerns, and the risk of biased outputs. A single misstep could derail the whole project or even damage customer trust.  

It’s an all too familiar situation for many data architects today: how do you adopt AI quickly while ensuring you’re doing it responsibly?

Many organizations are rushing into the world of artificial intelligence (AI) without a proper AI governance plan. This can leave them vulnerable to serious risks such as data breaches and severe regulatory fines.  

Take, for example, the case of the remote tutoring company iTutor Group, which had to pay out a staggering $365,000 settlement due to ungoverned AI practices that lead to unlawful age discrimination.

But the good news? When done right, AI governance can actually speed up the adoption of AI, rather than slowing it down. It builds trust, reduces risk, and helps organizations scale their AI efforts with confidence.  

In this article, we’ll provide you with a practical guide to AI governance. We’ll cover the key components, the steps to bring AI to life in your everyday work, and share a real-world use case.  

What is an AI governance framework?

With AI adoption on the rise, with 72 percent of organizations using it in at least one business function, it’s a great time to ask what does an AI governance framework really mean?

An AI governance framework is a way to manage AI systems throughout their lifecycle. It’s the set of processes, standards, and guardrails that ensure AI systems and tools operate safely and ethically.

As AI has moved well beyond testing, it’s become an integral part of our daily business operations, driving real business impact. But without a proper governance framework in place, AI can lead to challenges like fragmented learning, legal issues, and even risks to your brand’s reputation.  

As these risks grow and challenges become more pressing, so does the demand for effective solutions. In fact, the global AI governance market is expected to reach $26.91 billion by 2035.This growth underscores how formal oversight and frameworks are becoming essential for responsible AI adoption, especially with strict regulations like the EU’s AI Act coming into effect.

What are we really trying to achieve with AI governance?

Why should organizations like yours implement AI governance in the first place? Because it’s about building trust as you scale AI across your organization. Your AI governance becomes your safety net to help you move faster, not slower.

The whole point is to help organizations tap into AI’s potential without compromising their integrity.  

Here’s what a solid governance framework will accomplish:

Keep AI ethical and fair

Governance guides AI development toward fairness, safety, and respect for human rights. This means going beyond technical performance. Organizations must ask: Who is this model serving? Who might it be leaving out? Ethical governance promotes transparency, accountability, and thorough audits of training data to prevent the reinforcement of bias.

Take Microsoft’s six responsible AI principles, which they created after early missteps (Tay), built on the NIST AI Risk Management Framework. These principles now guide how they design, test, and launch AI across every team and product.

Stop AI risks before they stop you

Governance frameworks are designed to identify and address critical issues, such as biased outputs, compliance issues, and security vulnerabilities. Effective risk management is also crucial for achieving a positive return on investment (ROI) and helps you avoid “AI pilot purgatory.” This is the endless cycle where AI pilot programs never actually become real solutions.  

Stay on the right side of regulations

Compliance is not optional. Local and international regulations are getting stricter, and non-compliance can result in substantial financial penalties. Under the EU AI Act, you could face fines of €40 million or 7 percent of your turnover.  

Your governance framework becomes your compliance roadmap, ensuring your AI deployments stay legal from day one.

Scale AI the right way

Good governance provides the structure to grow your AI initiatives with confidence. When stakeholders see that you are committed to ethical AI practices, trust follows. And trust? It helps you move fast, adapting AI without breaking things.

Integrating security and transparency into your AI from the start protects against risks and ensures a more robust system. This approach supports a reliable framework for sustainable AI growth.

What does it take to govern AI responsibly?

But how do you build responsible AI into the foundation of your organization? There are some key components that have to fit together cohesively. Each one helps you evaluate AI systems for how well they comply with regulatory standards and align with your company goals.

Here’s how we make AI governance work in practice:

Explainability and transparency

Trust starts with understanding. When AI systems operate as black boxes, stakeholders lose confidence quickly. Your governance framework should prioritize transparency at every level.  

To put this into practice, focus on the following elements:

• Model interpretability means building AI systems that people can actually understand. Decision-making processes should be traceable, from initial input data to final conclusions. This clarity helps when you need to explain outcomes to users or regulators.
• Audit trails are about keeping detailed records covering data sources, model training processes, and decision logs. These records support both AI accountability and compliance requirements.
• Open communication involves engaging stakeholders throughout the AI lifecycle brings valuable perspectives. Different viewpoints help identify potential bias and societal impacts that might otherwise go unnoticed. Clear communication about AI goals and expected impacts reduces resistance and builds broader acceptance.

Accountability and consistency

Effective governance ensures your AI systems get managed properly, with consistent results you can count on and clear accountability when things go wrong.  

• Defined roles and responsibilities assign a specific person or team to oversee AI systems. This helps prevent confusion during AI development, deployment, and ongoing monitoring. Clear ownership structures ensure issues get addressed promptly.
• Create governance policies that provide the roadmap for handling problems. These guidelines should outline procedures for resolving issues and implementing corrections when AI systems do not perform as expected.
• Standardize your processes to create uniform approaches for data management, model training, and output evaluation. This standardization makes scaling easier, reduces errors, and creates consistency across your organization.
• Version control everything, which allows you to track changes and enable reproducibility. Maintain thorough records of model versions and modifications to recreate results or understand what changes were made between iterations.

Safety considerations

AI systems must prioritize user safety and well-being. It’s about building technology that genuinely serves people’s interests beyond legal compliance.

• Risk assessment helps in identifying potential problems before they materialize. Evaluate possible risks and develop a strategy to lower them for each scenario.
• Continuous monitoring will keep you ahead of issues. Regular performance reviews help you detect safety problems as soon as they emerge, enabling prompt intervention.

Security measures

Protecting AI systems from malicious activities maintains both trust and operational integrity. Security cannot be an afterthought.

• Strong infrastructure prevents unauthorized access and protects against data leaks. Build security into your systems from the foundation rather than adding it later as patches.
• Data protection protocols ensure information your AI uses remains secure throughout its lifecycle. Whether data is being stored, processed, or transmitted, proper security measures must be in place.

Fairness and inclusiveness

AI systems should serve all users equitably, not just select groups. Bias in AI can create unfair outcomes that harm both personal relationships and business outcomes.

• Bias detection: Identify and address biased patterns in both your data and algorithms.  Biased outputs lead to unfair outcomes, which in turn create unhappy customers.
• Inclusive design: Design principles ensure AI solutions work for diverse user groups. Consider diverse needs and perspectives during development to create more effective systems that fit everyone.

Now let’s take a look at some of the common challenges when putting AI governance into practice.

What makes it hard to get AI governance right

We know putting AI governance into practice may sound straightforward; we also know that the reality can be quite challenging. Many businesses face hurdles in three key areas: how their teams work together, the technology they have in place, and how they allocate their resources.  

Below are some of the challenges.

Organizational resistance and change management

The challenging part is not writing policies but getting your team to follow them. Employees resist new workflows when they are already comfortable with their current processes.

Change is hard and most organizations feel it. Around 70 percent of change programs fail, largely because employee’s aren’t on board and leadership doesn’t support fully support the process. This resistance creates “shadow AI,” where staff bypass IT approval and use unauthorized AI tools to get their work done faster.

Shadow AI creates serious problems. Data privacy becomes compromised. Security vulnerabilities multiply. Compliance goes out the window, and organizational trust erodes.

• ‍Solution: Involve stakeholders early in the governance design process. Provide training that shows people how governance benefits their daily work, not just company objectives. Establish clear protocols to identify and manage unauthorized AI use while aligning these processes with ethical AI practices.

Technical integration headaches

Most organizations face a fundamental mismatch: they are trying to implement modern AI governance on legacy infrastructure that was not designed for these demands.

Your current systems probably lack the processing power, storage capacity, and scalability required for current AI workloads. Integrating various AI applications with governance frameworks requires significant technical coordination and cross-team collaboration.

• ‍Solution: Conduct a thorough infrastructure assessment first. Use middleware or APIs to bridge compatibility gaps between legacy systems and new AI tools. Tools like Domo can streamline this compatibility headache and support a solid AI risk management framework.

Resource constraints and expertise gaps

Building AI governance in-house is expensive and complex. It diverts budget and personnel from core business objectives while requiring specialized knowledge that many organizations lack.

The challenge is twofold: insufficient funding and a shortage of staff who understand both AI technology and governance requirements. This gap in expertise results in slower implementation and higher costs than expected.

• ‍Solution: Invest in AI literacy training for existing team members. Consider platforms with built-in governance features rather than building everything from scratch. Sometimes, purchasing existing solutions makes more sense than developing custom frameworks, especially when speed of implementation is a priority.

Choosing the right AI governance framework for your organization

Not every organization needs the same AI governance approach. The right framework depends on what your organization aims to achieve, its infrastructure, and the level of AI maturity.

Here’s how to choose the right one and tailor it to your organization’s needs:

Assess your current state

Before selecting a framework, evaluate your organization’s current state. This evaluation identifies governance gaps and sets realistic implementation priorities.

• ‍Data architecture: Are your data sources secure, compliant, and well-documented?
• ‍Governance maturity: Do you already have internal policies or committees in place for data and AI oversight?
• ‍AI use cases: What types of AI models are in use, experimental, production, or mission-critical?

Understand your business and industry context

Governance requirements vary across sectors. Consider:  

• Risk tolerance: High-stakes industries like finance or healthcare require stricter controls.
• Innovation goals: Some organizations prioritize speed to market, while others place greater value on risk mitigation.

Evaluate framework options

Several widely respected AI governance frameworks offer structure and best practices:

• NIST AI Risk Management Framework (RMF): A flexible U.S. government-developed model focused on trustworthiness, risk identification, and lifecycle management.
• IEEE AI Ethics Standards: Technical standards and guidelines for ethically aligned AI design.
• OECD AI Principles: High-level global recommendations on AI that promote fairness, transparency, and robustness.

Define your selection criteria

When choosing or customizing a framework, prioritize:

• Scalability and flexibility: Can the framework grow with your AI program?
• Integration with existing systems: Does it work with your current data, security, and IT policies?
• ROI and resource demands: Is it feasible considering your team’s capacity and available tools?

With a framework chosen, let’s explore practical steps to operationalize AI governance effectively.

Practical steps to putting AI governance into practice

Creating a governance framework is one thing, but getting it to function effectively is where many organizations face challenges. The gap between theory and implementation hinders even experienced teams.

Here are the practical steps that make governance work without overwhelming your team.

• Define policies and roles: Begin by documenting your non-negotiables, including the use of ethical AI, compliance standards, and procedures for addressing errors and incidents. Assign people to oversee the implementation by giving them both authority and responsibility. These governance leads become your early warning system when problems emerge.
• Build an AI model inventory: Create a detailed catalog that documents every AI model in your organization. What does it do? What are its associated risks? Who uses it? Where does the data come from? This inventory is your governance command center, where policies are applied consistently instead of haphazardly.
• Implement risk management: Use established frameworks like NIST AI RMF, but adapt them to your specific context. Look for organizational risks (regulatory compliance, reputation damage) and model-specific risks (bias, security holes, data poisoning). The risk management helps you move fast while staying safe.
• Strengthen data governance: Invest in data quality before you invest in advanced AI tools. Ensure your training data sets represent the populations your AI will serve. Verify compliance with privacy regulations like GDPR, CCPA, HIPAA, and ISO standards, including ISO 42001, to protect users’ information and rights.
• Conduct model verification and validation: Design testing protocols that include adversarial attacks, fairness assessments, and supply chain security reviews. Test your system architecture, not just individual models. Verify that your AI behaves appropriately when it encounters edge cases or unexpected inputs. Thorough validation upfront prevents expensive problems later.
• Leverage tools: Look for solutions like Domo AI that centralize governance functions while integrating seamlessly with your existing tech stack. Prioritize tools that automate routine compliance tasks, provide real-time monitoring, and offer clear visibility into model performance.
• Monitor continuously: Implement monitoring that tracks model performance, bias emergence, and compliance drift in real-time. Build dashboards that quickly surface problems, allowing you to address them before they impact users.  

What good governance looks like in practice

AI governance is a necessity, as shown by leading organizations across sectors. The use cases below demonstrate how companies have effectively implemented AI governance frameworks in practice.

Microsoft’s responsible AI program

The 2016 Tay bot incident quickly became problematic for Microsoft after it interacted with users online. Microsoft used this as an opportunity to improve its approach to responsible AI.

Their response created a comprehensive governance program:

• Dedicated oversight structures: Microsoft established the Office of Responsible AI (ORA) and the AETHER committee (AI, Ethics, and Effects in Engineering and Research) to develop policies that guide AI development across the entire organization.
• Rigorous review processes: Every AI project goes through ethics reviews and impact assessments before deployment. This proactive approach helps in identifying potential issues early, when they are easier and cheaper to address.
• Developer training programs: Microsoft invests in educating its engineers and researchers on responsible AI principles. They provide both the knowledge and practical tools needed to build ethical AI systems from the start.

IBM’s AI Fairness 360 toolkit

IBM recognized that AI bias impacts entire industries, not just individual companies. Instead of creating solutions solely for internal use, they chose to address this challenge through open collaboration.

• Practical bias detection tools: The AI Fairness 360 toolkit provides developers with algorithms, metrics, and tutorials for identifying and addressing bias in AI models.
• Community-driven improvement: IBM enabled faster innovation through community contributions by open-sourcing its fairness toolkit. This approach promotes a commitment to addressing AI bias across the industry.
• Integrated governance approach: The toolkit allows teams to build fairness checks directly into their development workflows. This prevents bias issues rather than trying to fix them after deployment.

Mastercard’s fraud-prevention framework

Mastercard processes billions of transactions globally, making AI governance essential for both security and customer trust. Their fraud detection systems handle sensitive financial data, which requires governance that goes beyond basic compliance.

• Transparent decision-making: Mastercard prioritizes explainability despite the complexity of its fraud detection models. When a transaction is flagged, they can provide clear reasoning, which is crucial for both customer satisfaction and regulatory requirements.
• Advanced privacy protection: Their approach to data privacy exceeds standard requirements. They use advanced techniques like data anonymization and secure processing to protect sensitive information throughout the AI lifecycle.
• Human oversight integration: While AI identifies potentially fraudulent activity, human experts review flagged transactions and make final decisions. This combination uses AI efficiency while maintaining human judgment for complex cases.

Bringing it all together: AI governance as your advantage

We’re entering a new era where AI’s promise comes with real accountability. The organizations that thrive will be those who see governance not as a hurdle, but as a catalyst for trust, agility, and growth.

Building effective AI governance doesn’t have to slow you down. With clear frameworks, practical steps, and a focus on transparency and fairness, you can unlock AI’s value while protecting your business and customers.

As you look to scale AI within your organization, thoughtful governance is what turns innovation into impact. If you’re ready to go deeper, watch the Agentic AI Summit replay or check out our Security and Privacy FAQ for more details about how Domo thinks about AI governance.

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