Good governance holds the key to successful AI innovation

Organizations often balk at governance as an obstacle to innovation. But in the fast-moving world of artificial intelligence (AI), a proper governance strategy is crucial to driving momentum, including building trust in the technology and delivering use cases at scale.

Building trust in AI, in particular, is a major hurdle for AI adoption and successful business outcomes. Employees are concerned about AI’s impact on their job, and the risk management team worries about safe and accurate use of AI. At the same time, customers are hesitant about how their personal data is being leveraged. Robust governance strategies help address these trust issues while laying the groundwork for standardized processes and frameworks that support AI use at scale. Governance is also essential to compliance — an imperative for companies in highly regulated industries such as financial services and healthcare.

“Done right, governance isn’t putting on the brakes as it’s often preconceived,” says Camilla Austerberry, director at KPMG and co-lead of the Trusted AI capability, which helps organizations accelerate AI adoption and safe scaling through the implementation of effective governance and controls across the AI life cycle. “Governance can actually be a launchpad, clearing the path for faster, safer, and more scalable innovation.”

Best practices for robust AI governance

Despite its role as a crucial AI enabler, most enterprises struggle with governance, in part because of the fast-moving technology and regulatory climate as well as an out-of-sync organizational culture. According to Foundry’s AI Priorities Study 2025, governance, along with IT integration and security, ranks among the top hurdles for AI implementations, cited by 47% of the responding organizations.

To be strategic about AI governance, experts recommend the following:

Focus on the basics. Because AI technologies and regulations are evolving so quickly, many organizations are overwhelmed by how to build a formal governance strategy. It’s important to create consensus on how AI strategy aligns with business strategy while establishing the proper structure and ownership of AI governance. “My advice is to be proportionate,” Austerberry says. “As the use of AI evolves, so will your governance, but you have to start somewhere. You don’t have to have it all baked in from the start.”

Include employees in the process. It’s important to give people easy access to the technology and encourage widespread use and experimentation. Companywide initiatives that gamify AI encourage adoption and promote feedback for AI governance frameworks. Establishing ambassador or champion programs is another way to engage employees by way of trusted peers, and an AI center of excellence can play a role in developing a foundational understanding of AI’s potential as well as the risks.

“Programs that are successful within organizations go that extra mile of human touch,” says Steven Tiell, global head of AI Governance Advisory at SAS Institute. “The more stakeholders you include in that conversation early, the better.”

Emphasize governance’s relationship to compliance. Effective governance means less friction, especially when it comes to regulators and risk auditors slowing down AI implementation. Given the varied global regulatory climate, organizations should take a forward stance and think beyond compliance to establish governance with lasting legs. “You don’t want to have to change business strategy or markets when a government changes regulations or adds new ones,” says Tiell. “You want to be prepared for whatever comes your way.”

To learn more, watch this webinar.