Artificial intelligence (AI) is a core part of business strategy, so if you’re not already using AI, you may be falling behind. But because AI applications are everywhere across diverse business units and embedded into your business processes, effectively governing AI is even more important. This article gives you an intro to AI governance, explaining the role of AI governance and the challenges in developing AI systems; a four-step approach to AI governance; and the best practices for implementing AI governance in your enterprise.
In this article, you discover
- AI governance and its challenges
- How to support your AI governance framework
- Implementation of AI governance
Explaining the role of AI governance and its challenges
Many organizations are realizing that AI transformation is a problem of governance instead of just a technical hurdle. Fundamentally, AI governance is your organization’s blueprint for using AI responsibly. It outlines guidelines, policies, and frameworks that keep you from making common AI mistakes. In the world of large-scale AI, small problems can quickly snowball into major headaches, so AI governance and its safeguards become essential. Having AI governance in place helps you with the following:
- Defining best practices and processes to ensure completeness, transparency, and accountability
- Supporting corporate goals
- Facilitating regulatory compliance
- Enabling the unbiased, accurate, and ethical use of data and AI in all applications
- Protecting sensitive data
- Integrating data and managing high-volume, high-velocity data for AI
- Maintaining high-quality data
- Ensuring data privacy, filling protection gaps, and facing regulatory and compliance scrutiny
- Improving data and AI fluency
- Ensuring AI delivers accurate output that’s ethical, resilient, secure, unbiased, accountable, and transparent (responsible AI)
- Using fragmented technology and complexity
Supporting your AI governance framework with a proven approach
Discovering the best way to find and manage diverse data, enrich it, protect it, and provide excellent and trustworthy results from AI requires a reliable approach. That approach also needs to support your AI governance framework. The methodology is both simple and comprehensive in its execution, and it’s designed to address the challenges in the preceding section of this article.
To provide a catalyst for the adoption of reliable AI systems and promote business innovation, you want to follow four steps:
- Inventory: Create a comprehensive inventory of data and AI assets.
- Control: Control access to data and the AI systems that use it.
- Deliver: Deliver trustworthy, high-quality diverse data at scale.
- Observe: Bring monitoring and observability to your AI systems.
These steps provide a blueprint to ensure that your AI systems are accurate and trustworthy while keeping data and AI operations safe and secure.
Implementing AI governance
Implementing AI governance is a process, but if you want to foster the kind of growth and efficiency that only AI can bring to your organization, follow these best practices:
- Establish clear AI governance vision, sponsorship, and buy-in.
- Establish integrated and connected data management.
- Maintain updated inventories.
- Centralize your metadata.
- Capitalize on AI power for AI governance.
- Improve your AI governance literacy.
- Implement responsible AI.
- Adapt governance to shifting markets, tech, and regulations.
- Analyze success by tracking return on investment and celebrating your wins.
- Iterate: observe, learn, and refine.
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