The experimental phase of generative AI is over, and enterprises are now under pressure to move AI into production to streamline operations, enhance customer experiences, and drive innovation. However, as AI deployments grow, so do the reputational, legal, and financial risks. According to Gartner, enterprises that invest in AI governance and security tools can achieve 35% more revenue growth than those that don't. But many leaders are unsure where to start, as AI governance is a complex, evolving field that requires a thoughtful approach.

The challenges of AI governance are multifaceted. Gaining visibility into how AI systems interact with data remains difficult, as AI systems often operate as black boxes, defying traditional auditing methods. Solutions that have worked in the past, such as observability and periodic reviews of development practices, don't mitigate the risks of unpredictable behavior or prove acceptable use of data when applied to large language models (LLMs). Furthermore, AI's rapid evolution, including the emergence of autonomous systems, introduces new use cases but also substantial challenges.

The regulatory landscape also remains in flux, particularly in the U.S. Recent developments, such as the Trump administration's repeal of Biden's AI Executive Order, will likely lead to an increase in state-by-state legislation, making it difficult for organizations operating across state lines to predict the specific near-term and long-term guidelines they need to meet. This uncertainty leaves organizations struggling to prepare for a patchwork of state-specific laws while managing global compliance demands like the EU AI Act or ISO 42001.

In addition, business leaders face numerous governance frameworks and approaches, each optimized to address different challenges. This abundance of approaches forces business leaders into a continuous cycle of evaluation, adoption, and adjustment, often resulting in reactive, resource-intensive processes that create inefficiencies and stall AI progress.

However, there is a way forward. The governance journeys of SaaS and Web2 offer a proven roadmap for AI governance. Early SaaS and Web2 companies often relied on reactive strategies to address governance issues as they emerged, adopting a "wait and see" approach. But this reactive approach was costly and inefficient, leading to the adoption of continuous, automated governance. SaaS providers implemented continuous integration and continuous delivery (CI/CD) pipelines to automate the testing of software and deployed tools for real-time monitoring, reducing operational burdens. Web2 platforms implemented machine learning to flag inappropriate content and detect fraud at scale.

A similar shift is needed for AI governance. Manual, reactive governance strategies are proving inadequate as autonomous systems multiply and data sets grow. Decision-makers frustrated with these inefficiencies can look at the shift toward automation in SaaS and Web2 as a blueprint for transforming AI governance within their organizations. A continuous, automated approach is the key to effective AI governance, enabling companies to proactively address reputational, financial, and legal risks while adapting to evolving compliance demands.

By embedding tools that enable continuous, automated AI governance into their operations, companies can track data to ensure compliance with regulations, reduce the need for manual oversight, and allow technical teams to focus on innovation rather than troubleshooting. As organizations increasingly integrate AI into their operations, the stakes for effective governance grow higher. The companies that adopt governance strategies focused on continuous and automated monitoring will gain a competitive edge, reducing risks while accelerating deployment.

In conclusion, a continuous, automated approach to AI governance isn't just a best practice — it's a business imperative. Enterprises must learn from the lessons of SaaS and Web2 and shift from reactive to proactive governance to drive responsible innovation and stay ahead in the AI race.