AI has incredible potential, but with that power comes a lot of responsibility. If you want to unlock the full benefits of AI technology—without running into problems—you need to have solid AI governance in place.
Governance is a key part of any successful AI strategy, and it’s one of the five essential pillars of AI readiness:
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Data
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Governance
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Investment
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Skills
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Strategy
In this article, we’ll focus on the Governance pillar, sharing insights from our research with 600 tech executives and leaders across different industries. Here’s what we discovered about AI governance and how organizations can safely adopt AI while reducing potential risks.
AI Governance: Finding the Balance Between Innovation and Responsibility
Successful AI governance creates a framework that allows organizations to adopt AI in a responsible and ethical way. It sets up processes and policies that help manage risk, build trust, and ensure compliance as your organization uses or develops AI over time.
When organizations have strong AI governance in place, they typically have:
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Clear, organization-wide policies for how AI should be used and developed
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Tools and processes to monitor and reduce AI risks, such as issues with bias, data privacy, or legal liabilities
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Guidelines to make sure they comply with AI laws and regulations
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Open channels for discussing any ethical concerns or questions about AI
The good news? More than half of tech leaders are on board with the idea of AI governance. In fact, 64% of them say their organization has the right infrastructure to stay compliant and adapt to future changes in AI regulations.
Scaling AI with Confidence: How Governance Plays a Key Role in AI Success
Just like cybersecurity protocols are crucial for protecting your business, AI governance is essential to ensure safe and responsible AI use.
Without proper governance, AI can pose serious risks—like damaging your brand’s reputation, causing data breaches, or even leading to physical harm. But good governance isn’t about putting the brakes on innovation—it’s about managing risk while still encouraging new ideas and breakthroughs.
In fact, sometimes the restrictions in place can actually spark creativity. Take the DeepSeek AI model, for example. Due to restrictions on AI chips, the DeepSeek team had to work with less powerful GPUs. These limitations forced them to get creative, and as a result, they built a competitive AI model at a lower cost than companies like OpenAI.
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How to Build an AI Governance Framework for Ethical and Responsible Use
AI governance might seem like a lot to keep up with, especially since the AI landscape is constantly evolving. But don’t worry, here are some key steps to help you improve your governance strategies, minimize risk, and stay on top of the latest best practices.
Start with a Centralized Policy for AI Use and Development
Did you know that 79% of organizations already have a centralized AI usage policy or set of guidelines? Having a clear, consistent framework for AI use is a great foundation for any AI governance strategy. It helps employees understand how, when, where, and why they can use AI in a responsible way.
If you’re not ready to roll out AI across your entire organization or can’t develop a full policy just yet, don’t worry—a simple usage policy is still better than nothing! In fact, many employees are already experimenting with AI on their own. Rather than letting it turn into “shadow IT,” providing clear guidelines can help prevent any major issues down the road.
This is especially important for government agencies and public sector organizations. With the rollback of Biden’s AI executive order and no federal AI guidelines yet, they’ll need to create their own policies to keep their AI projects on track.
Mitigate Risk with Data Governance for AI
In addition to having general usage guidelines, it’s important to set up specific policies to address key AI risks, such as:
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Bias and discrimination
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Copyright infringement and plagiarism
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Data privacy
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Lack of transparency and accountability
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Misinformation
Invest in Dedicated Resources for AI Cybersecurity Risk Management
Surprisingly, only 29% of organizations have dedicated resources in place to manage AI risks. About 40% address these risks on a case-by-case basis, and a third have little to no resources for responding to AI-related threats.
While having formal policies is helpful, you can’t fully eliminate AI risks without being proactive. It’s important to allocate dedicated resources to monitor and address potential AI cybersecurity issues before they turn into a crisis.
Set Up Employee Feedback Channels and Monitor AI Ethics
Effective governance is all about staying on top of things, and that means creating systems to monitor your AI practices and encourage employee feedback.
Consider setting up automated dashboards to keep track of your AI systems, flagging potential risks, and ensuring compliance with policies, ethical practices, and regulations.
It’s also essential to have channels where employees can voice concerns or ask questions about the ethical use of AI. While 94% of organizations have these feedback channels, not all of them are able to respond effectively. In fact, 48% have mechanisms in place but can’t always address the concerns raised. If this is the case in your organization, it’s time to create dedicated teams to manage these channels.
Take Internal Feedback Seriously to Prevent Ethical Issues Later
Ignoring internal feedback can lead to bigger ethical problems down the road. It’s important to actively monitor feedback channels and respond to concerns, so you stay on top of any potential risks. Showing employees that you take their feedback seriously will help build trust and keep everyone on the same page.
Build Your Team’s AI Ethics Skills
Even with the rapid growth of AI, only 1.8% of people learning about AI have taken the time to figure out how to use it responsibly.
When it comes to AI governance, it’s not just about having the right policies in place—organizations need to make sure their team understands and follows them. This starts by educating your workforce on why AI governance matters and how careless AI use can impact the entire organization.
