Proper AI governance is critical for organizations preparing to adopt and implement AI solutions into their business models, but it’s not always a straightforward process. Here’s how to get started on your path to an effective AI governance framework.
Widespread adoption and demand for the use of AI in business are showing no signs of slowing down. Amid rising investments in and focus on AI applications, particularly with the rise of generative AI, companies are facing increasing pressure to adopt and implement AI quickly in their business models. But in the race to adopt AI, you can’t overlook the necessity of establishing strong AI governance practices that can help streamline implementation and reduce risks to your organization.
What is AI governance?
Governance is a crucial and often overlooked piece of an organization’s AI strategy. AI governance encapsulates your organization’s established policies and best practices for adopting and responsibly using AI by providing a framework for AI decision-making, operations, and sustainability. These policies extend beyond how you govern your data, looking at how data is managed and used in the context of AI models, the level of decision-making afforded to AI within your organization, and the role of people in your organization to ensure quality and ethical outputs.
Without establishing proper AI governance that addresses how to use AI and secure the data being fed into it, you’re leaving the door open to failed or stalled AI implementations, ineffective AI use, and creating unnecessary cybersecurity risks. Establishing an AI governance framework can help your organization solidify quality controls, ensure AI is being used responsibly and ethically, and promote greater data security and privacy.
Barriers to AI adoption
In our AI-readiness webinar, we found that while 49% of attendees said they currently use AI, only 23% had an AI governance policy in place — meaning that over half of attendees struggle to adopt AI at all, and less than a quarter that do have established policies to govern it. Many factors can act as barriers to AI adoption. According to an IBM Global study, a lack of AI skills and expertise, data complexities, and ethical concerns are among the chief challenges for organizations struggling to adopt AI. The key to overcoming these barriers is identifying gaps in your AI adoption readiness and governance — and developing a clear path forward to address them.
Key steps for establishing an AI governance framework
1. Identify meaningful AI use cases for your organization
Proper governance starts with selecting the right AI tool for your business problem. Many organizations fall into the hype around AI adoption, but a deliberate approach is key. Your leadership team should consider how AI fits into your business and how it should (or shouldn’t) be used. Ask yourself what problem you’re trying to solve — is the AI tool in response to an actual business problem? Responsible AI provides a clear solution to a business need. If you lack a business case for the AI solution you’ve selected, then you’re likely not going to reap its benefits.
Also consider: Will the AI solution provide you with a competitive advantage? Reviewing what competitors and peers are doing in this space can help you define a business advantage for adoption. Failing to align your AI strategy with your business goals can lead you to wasting time and money, without the results to show for it.
2. Build a strong governance foundation for data and decision-making
Consider the types and levels of leadership that are required for decision-making. Forming an AI governance committee can help you establish a formal, uniform strategy and support change management procedures. AI adoption is not solely an “IT responsibility,” rather it requires close collaboration between business and technology leaders across the organization. Also, ensure your staff know their roles in using AI. Reviewing key job roles adjacent to AI governance and data management can help you identify responsibilities.
Data drives AI solutions, thus the crux of building the foundation of your AI governance strategy is good data governance. Effective AI hinges on the quality of your data inputs. If your organization has established policies around data retention, data archiving, and data security, you’ll likely already have the building blocks to implement AI governance.
3. Establish policies for the acceptable use of AI
An effective framework will have controls and mechanisms in place to enforce and encourage your staff to comply with established policies. It’s important to maintain a human-in-the-loop or human-centric approach to AI, whereby the technology is seen as a useful tool and not a replacement for employee work responsibilities. Establishing clear limitations on the use and purpose of AI can help minimize risks such as IP leaks or copyright infringement. When feeding an AI model, avoid inputting more data than necessary. Ensuring the right people have access to the right amount of data can add an extra layer of data protection. Be sure to maintain and update your AI governance policies on a routine basis to ensure they align with the changing needs of your organization and the compliance landscape.
Keep data governance front and center
It can’t be said enough: AI governance and data governance are crucially linked. As you’re developing your AI model through model life cycle management — a governance process for developing and validating your AI model throughout its entire life cycle — consider the type of data permitted and how it’s retrieved. Do you have transparency within your AI model? Likewise, do you have safeguards and policies in place to protect your data inputs? While some AI platform vendors may have protections for customers to shift risk and liability onto them, others don’t. Ensure you can clearly identify the product liability your vendor possesses, and which responsibilities fall under your domain.
Ultimately, data minimization should be a central feature of your data governance strategy. Your organization should have clear and established data governance controls to ensure you’re not feeding your models with more data than necessary and inadvertently causing copyright or IP risks. Establishing strong internal controls can also set you up to maintain compliance with existing privacy laws such as GDPR compliance in the European Union and United Kingdom and the California Consumer Protection Act.
Practice “human-in-the-loop” policies for AI accountability
With the advancement of Agentic AI, carefully consider the level of decision-making you’re permitting AI to perform. It’s essential that you create an accountability structure to screen the quality of outputs from your chosen solution. A human-centered approach to AI use is necessary. Current AI technology can perform a variety of complex tasks with ease and deliver great value — they’re not infallible though. AI solutions can suffer from model bias producing undesired results or even “hallucinations,” whereby the AI will provide false or inaccurate output. While AI can help you make decisions, it lacks the human judgment to fully validate the appropriateness and ethics of the output. Make sure your staff — from the top down — understands their responsibility when using AI. Training your organization on governance procedures is critical to maintaining compliance with organizational policies and building a culture of accountability.
Governance is crucial for AI success
As AI technologies rapidly advance and provide new capabilities, proper governance remains a pivotal factor in driving successful adoption and overcoming challenges with AI. Ultimately, everyone within your organization has a stake in upholding AI and data management policies, and it requires a coordinated effort to build out a governance structure that protects the integrity of your data and organization.
