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My journey into AI Governance began at the 2023 Second World Summit of Future Commissions held in Montevideo, Uruguay. This event, hosted by the Parliament of Uruguay and the Inter-Parliamentary Union, was a significant starting point. We discussed the need for anticipatory governance in AI, focusing on how this technology intersects with democracy, human rights, and societal structures. These discussions set the stage for a deeper exploration of AI’s transformative potential. Final Declaration

The topic continued to gain prominence in my agenda during IBM Think 2024 in Boston, where leading experts delved into AI’s impact on business innovation. The event emphasized the critical role of governance in harnessing AI’s benefits while mitigating its risks. This conversation reached a new level of depth at the DAMA Philadelphia and Delaware Valley event in Philadelphia on September 19, 2024, featuring a presentation by Sunil Soares, a leading authority on AI Governance. Sunil’s extensive experience, including authoring 11 books on data management, provided a solid foundation for understanding the best practices in this emerging field.

What is AI Governance?

AI Governance is the framework of policies, practices, and controls that ensure AI systems are developed and deployed ethically, transparently, and securely. In an era where AI is increasingly shaping business decisions, governance provides a necessary structure to prevent unintended consequences, such as bias, data privacy issues, and decision-making opacity. It’s about ensuring that AI aligns with both organizational values and broader societal expectations.

Sunil Soares’ 13-Step Model for AI Governance

At the DAMA event, Sunil Soares introduced his comprehensive 13-step model for AI Governance, which covers the entire lifecycle of AI implementation: 

1. Establish Accountability – Designate leadership for AI Governance and create oversight mechanisms.
2. Assess Regulatory Risks – Consider applicable laws and sector-specific regulations.
3. Gather Use Case Inventory – Collaborate with stakeholders to identify and prioritize AI applications.
4. Increase Data Value – Manage data quality, rights, and accessibility to maximize AI’s potential.
5. Address Fairness and Accessibility – Mitigate biases and ensure AI systems are inclusive.
6. Improve Reliability and Safety – Test and validate AI models rigorously to avoid failures.
7. Heighten Transparency – Increase explainability of AI decision-making processes.
8. Implement Human-in-the-Loop Mechanisms – Ensure humans remain central to critical decisions.
9. Support Privacy and Data Retention – Focus on data minimization, anonymization, and security.
10. Enhance Security – Protect AI systems from threats like data poisoning and unauthorized access.
11. Manage AI Model Lifecycle – Track AI models throughout their lifecycle for compliance.
12. Oversee Risk Management – Develop robust risk management strategies specific to AI.
13. Realize AI Value – Measure the success of AI initiatives and scale accordingly.

You can access Sunil’s book, AI Governance Comprehensive: Controls, Regulations, Tools & Vendors, for more details. 

A structured, multi-step framework like Sunil Soares’ model is crucial when implementing AI use cases in any organization. AI projects are inherently complex, involving diverse stakeholders, data sources, and potential risks. A well-defined governance model ensures no aspect is overlooked—aligning AI applications with business goals, complying with evolving regulations, or safeguarding against biases.

Such a model provides a clear roadmap to navigate the challenges of AI deployment, enabling organizations to move beyond isolated experiments to fully integrated, scalable AI solutions. It helps create a consistent and reliable foundation for AI across all levels of the organization, ensuring that innovation is achieved responsibly and sustainably. This holistic approach ultimately drives the technical success of AI initiatives and their strategic and ethical alignment with the organization’s values and long-term vision.

Sunil’s model is supported by broader industry trends highlighted by recent reports. Gartner‘s 2025 Top 10 Strategic Technology Trends identified AI Governance Platforms as a crucial development. These platforms are designed to manage and control AI systems, ensuring they are reliable, fair, and aligned with ethical standards. Such platforms provide transparency and accountability, essential attributes for AI that interact with critical business and societal systems.

A report by MIT Technology Review-Databricks also emphasizes the need for democratizing AI across industries. It highlights that real-time data access and unified AI Governance models are key for organizations seeking to maximize value and maintain competitive advantage. The goal is to break down data silos, ensure regulatory compliance, and enable all employees—from technical experts to business leaders—to engage with AI effectively.

Conclusion: A Call for Comprehensive AI Governance

As we move forward, it’s clear that effective AI Governance requires a multi-faceted approach. Companies must blend best practices from thought leaders like Sunil Soares with insights from industry reports to create robust, adaptable frameworks. This journey demands technical solutions and a cultural shift towards anticipatory governance, ensuring AI serves humanity positively while respecting ethical boundaries.

By adopting a comprehensive AI Governance strategy, businesses can navigate the complexities of this transformative technology, aligning innovation with responsibility. My experiences at the global and local levels have reinforced the importance of this journey—one that I believe is essential for any organization looking to thrive in an AI-driven future.

To enhance my writing, I utilized ChatGPT and Grammarly to refine my language and clarity.