Accelerate with Confidence: Building a Strong AI Governance Framework

Diligent’s President and CEO Brian Stafford offers commentary on accelerating with confidence via building a strong AI governance framework. This article originally appeared in Insight Jam, an enterprise IT community that enables human conversation on AI.

Everyone is racing to deploy and govern AI, especially with the rise of agentic systems, but only a small number of organizations are doing it correctly. Amidst the technical boom and race to perfect AI, the business landscape is also grappling with a constantly evolving regulatory environment. According to a report from ProveAI, only 5 percent of executives reported that their organization has implemented an AI governance framework. However, 82 percent said implementing AI governance solutions is a priority, with 85 percent planning to implement them by summer 2025.

Strong governance ensures AI systems are designed, deployed, and maintained responsibly, aligning with legal, ethical, and strategic business objectives. While some fear that governance might hinder innovation, finding the right balance – where accountability and transparency mitigate risk without slowing progress—is key. Though the direct return on investment may not always be immediate, effective governance can enhance long-term value by reducing risk and ensuring sustainable AI deployment.

Inside the Blueprint: Four Pillars of a Robust AI Governance Framework 

Communication and Accountability as the Backbone of AI Governance

An effective AI governance framework is not just about oversight; it is about enabling speed, scale and confidence in AI deployment. Clear and consistent communication, enhanced by agentic AI, forms the foundation of successful AI initiatives. By leveraging agentic AI, organizations can automate routine tasks to ensure that boardrooms are equipped with real-time insights to encourage more informed and strategic discussions. As organizations continue to adopt agentic AI specifically, the need for robust communication and reporting protocols becomes even more necessary. These AI systems don’t just provide insights; they act on them.

Organizations must also outline in detail who is responsible for the various functions of AI oversight, whether it’s the technical teams managing model development or the senior leadership team. However, technical teams should not be solely responsible for AI governance. Rather organizations should develop a cross-functional AI governance committee including representatives from legal, compliance, IT and business leadership. This ensures policies remain adaptable and aligned with industry best practices while shifting the perception of AI governance from a constraint to an enabler of responsible innovation. The framework should support AI’s strategic value while implementing necessary compliance measures.

Transparency that Builds Confidence

When leveraging AI in an organization’s governance framework, transparency is critical to building confidence with both internal and external stakeholders. So, what does transparent decision-making look like? It means providing a clear understanding of how AI systems make recommendations, and why those systems are being deployed in the first place. This transparency ensures that all stakeholders – from data scientists to the C-suite – can trust and validate the use of AI tools. By clearly defining roles and responsibilities, organizations foster shared ownership, making AI governance an important part of product development and business growth.

Ethical AI as a Business Advantage

In today’s market, responsible AI use is becoming a competitive advantage. While it’s important to address risks like bias, inaccuracies and data security, top organizations are prioritizing AI systems that are reliable, transparent and efficient. Well-trained AI tools built on high quality data can strengthen oversight while also improving performance and speed.

AI models require constant validation, so conducting periodic risk assessments ensures these models function as intended and do not shift over time. Organizations that document their AI models’ lifecycle—from training to deployment with periodic updates— and ensure transparency throughout development will be better prepared to mitigate risks as the models evolve.

Empowering Teams with AI Literacy

A successful AI governance strategy requires AI literacy, especially at the executive and board levels. This can be achieved through specialized training, seminars and toolkits that highlight the effective use and adoption of AI tools in the workplace. This ensures leaders stay informed about evolving AI risks, compliance obligations, and technological advancements. It is imperative for decision-makers to integrate AI governance into strategic business goals rather than treating it as an entirely separate function.

Agentic AI: The Next Frontier for AI Governance Frameworks

The rise of agentic AI marks a significant shift, ushering in a new era of AI tools and workflow management for organizations.

Even though agentic AI hasn’t permeated the boardroom yet, its foundations are already being integrated into all operational levels of businesses. This clearly underscores the need for every organization to evolve its talent strategy, focusing on both upskilling and reskilling.

Upskilling and reskilling employees is not just about staying current; it’s about preparing your workforce to face the challenges of tomorrow. Taking this action with employees ensures that they understand responsible AI use, and how to mitigate the risk of misuse. Ultimately, a well-trained workforce becomes an active line of defense and accountability within a governance framework. Without a basic understanding of how AI systems function or fail, organizations risk falling behind. AI fluency empowers employees to adapt and learn quickly, collaborate more effectively, and stay aligned with strategic business goals.

In addition to training current employees, organizations should also consider how the younger workforce can support the integration of AI across all levels of the business. When recruiting new team members, it’s important to consider how these employees can contribute to the exploration, testing and prototyping of AI tools. For example, interns can act as experimenters. Their future-forward mindset and familiarity with emerging technologies positions them to drive hands-on growth from the bottom up. They aren’t just the future of the organization; they are today’s builders of internal agentic workflows.

The evolution of AI technology has led to a new generation of systems that operate with greater autonomy than traditional AI systems. These systems are not just tools; they’re intelligent agents capable of understanding and responding to their environments, much like a human would. This shift represents a unique opportunity to redefine AI governance frameworks. Rather than, passively asking users, “What do you want to do?”—a question that highlights the limitations of traditional AI capabilities – agentic systems are designed to be contextually aware. They can interpret contextual cues, anticipate user needs and proactively suggest next steps.

Unlike more traditional AI systems, which are often rigid and require explicit instructions, agentic AI introduces an almost human-like behavior. To effectively manage and integrate these advanced systems, organizations must adopt a governance framework that is dynamic, flexible, and extends beyond static policies. This transformation starts with preparing every level of the organization, from individual employees all the way up to the boardroom, to embrace and leverage the capabilities of agentic AI.

Winning AI

As organizations continue integrating AI into their operations, a well-defined AI governance framework is essential for mitigating risks and ensuring compliance. By focusing on accountability, ethical integrity, continuous model validation and cross-functional collaboration, businesses can develop governance frameworks that safeguard AI deployments especially as agentic systems operate with increasing autonomy and make decisions with less human oversight. Ultimately, effective governance isn’t about slowing progress; it’s about enabling organizations to scale smarter and faster, especially as AI systems gain autonomy. The companies that succeed with AI will be those that master governance now.