Organizations know AI is important. They’re investing in it, encouraging teams to use it and looking for ways to scale its value. But many organizations are still struggling to turn that priority into governed, scalable action.
Leaders are left scrambling to feel confident about whether AI is being used responsibly, creating a widening gap between what leaders want from AI – innovation, efficiency and competitive advantage – and what they can confidently manage in practice.
Only 24% of AI projects have adequate security controls in place, while 82% of executives say trustworthy AI is essential. This emphasizes the disconnect between intent and execution. AI adoption is moving faster than governance maturity, and organizations aren’t sure how AI is being used or if employees are stewards of trustworthy AI.
Why AI governance gaps happen
AI governance challenges often happen because AI adoption spreads faster than oversight models can mature. Teams begin using models, agents and third-party tools across departments, while governance processes remain distributed across legal, compliance, IT, security, data science and business teams.
Visibility is fragmented across teams and systems, causing potential duplication of work and resources as departments innovate in siloes. Different teams may be working on similar AI use cases, solving similar business problems or testing similar tools without knowing what others are doing. This can lead to duplicated effort, inconsistent decisions and missed opportunities to reuse approved approaches.
Shadow AI adds another layer of risk. When employees use unsanctioned AI tools or apply AI in ways that have not been reviewed, organizations may lose control over data exposure, policy alignment and accountability.
This lack of control can have serious repercussions as governance processes are often distributed across multiple departments, leading to inconsistent oversight and rising shadow AI risks.
The missing layer: a system of record
A successful AI governance strategy requires a combination of oversight, compliance, operations and culture. Effective governance requires a clear view into AI assets, accountability for ownership, clear policy alignment, and structured oversight and approvals.
Organizations need a centralized source of truth that captures AI models, agents, use cases, policies, and approvals. Without it, governance efforts remain fragmented and reactive.
This is the challenge SAS AI Navigator is designed to address. It provides a unified, lightweight governance layer that centralizes information about models, agents and use cases as well as the internal policies, external standards and regulations that an organization applies to them.
This system of record:
- Improves visibility and transparency by showing a complete AI inventory – accessible to both executives who need to prove that AI is governed and any employee who wants to know what uses of AI are in place today, and whether an idea for AI use is permitted.
- Helps tackle shadow AI by documenting how AI is being used today, from intake to post-deployment, and regardless of whether the AI is built in-house or purchased from a third-party vendor.
- Helps define AI governance roles and responsibilities, giving organizations a clear understanding of who owns what.
- And aligns usage with organizational policies and regulatory requirements, so legal and compliance teams can document relevant laws and standards.
Learn more
AI governance is foundational to scaling AI responsibly – and this scalable, trustworthy AI can help organizations continue to innovate in a rapidly changing AI and regulatory landscape. And the benefits of a strong AI governance foundation can include higher brand equity, consumer trust, and safeguarding long-term AI viability.
To learn more about AI governance and start planning your approach, explore session content on this topic and much more from SAS Innovate.
