top of page

INQ's Guide to AI Governance: Unlocking AI's Potential

Updated: 4 days ago

a canadian flag waving outside of an office building

by Carole Piovesan, Co-Founder / Principal

As organizations move to integrate more AI tools into their business practices, the need for responsible and ethical governance becomes increasingly pressing. This June, INQ is excited to delve into the complex and essential topic of AI governance with a comprehensive eight-part series. Throughout the month, we will explore different aspects of governing AI, providing valuable insights and practical tools for organizations navigating this rapidly evolving landscape.

What exactly is AI governance, and why is it necessary?

AI governance is about enabling the potential of AI is realized while mitigating the risks and potential harms it may pose. By establishing tailored governance practices, organizations can harness the power of AI to drive innovation, improve efficiency, and create products and services, all while upholding ethical standards and maintaining trust.

Let's explore what promises to be an insightful journey over the coming weeks.

Part 1: Understanding AI Governance

AI governance, in its simplest form, refers to the planning, oversight, and monitoring of high-risk AI applications and models. It involves managing both the value and risks associated with AI, ensuring that its use aligns with ethical principles, legal frameworks, and organizational values.

Governance is not a new concept, and AI governance should draw on existing practices in enterprise risk management, privacy, reputational risk, and financial risk, among others. However, the unique capabilities and potential pitfalls of AI demand a dedicated and tailored approach.

Effective AI governance is essential as it enables organizations to identify and mitigate risks, ensure compliance, and build trust with stakeholders, including customers, employees, and regulators. It also helps organizations avoid potential pitfalls, such as bias, privacy breaches, or ethical missteps, that could damage their reputation and hinder the realization of AI's benefits.

Here are six best practices to consider when establishing effective AI governance:

Establish a Tailored AI Governance Program: Develop a structured and tailored program that addresses the different aspects of AI governance, including strategic, operational, and technical elements. This program should be specific to your organization's needs and goals, with clear roles and responsibilities defined.

Foster a Culture of Responsible AI: AI governance is everyone's responsibility, and it's crucial to instill a culture of responsible AI within your organization. Ensure that employees at all levels understand the importance of ethical AI practices and their role in identifying and mitigating risks.

Implement Robust Data Governance: AI relies on data, so establishing robust data governance practices is essential. This includes data quality management, data privacy, and ethical data handling. Ensure you have processes to identify and rectify data biases and protect sensitive information.

Embrace Transparency and Explainability: AI systems should be transparent and explainable, particularly in high-risk applications. Adopt practices that promote transparency, such as documentation, model interpretability, and providing clear explanations of AI outputs to users and stakeholders.

Continuously Monitor and Audit: AI governance is an ongoing process. Implement regular monitoring and auditing procedures to identify and address any risks or issues that may arise over time. This includes monitoring changing regulations and ethical standards and adapting your practices accordingly.

Track the Law: Laws and regulations governing AI are emerging. For companies operating internationally, tracking these laws is essential as it helps navigate the increasingly complex regulatory landscape. By tracking these developments, organizations can ensure compliance, mitigate risks, and adapt their AI strategies to local and global standards.

Part 2: Vendor and Procurement Considerations

When adopting AI, one of the first steps is choosing the right vendors and partners. In this part of the series, we will explore the key considerations for selecting AI vendors, including their governance practices and responsible AI credentials. We will provide a checklist of criteria to evaluate vendors and ensure they align with your organization's values and governance standards.

Part 3: Risk Assessments and AI

Risk assessments are a fundamental tool in governance. We will delve into the specifics of structuring an AI risk assessment, identifying potential risks, and developing mitigation strategies. By the end of this section, readers will understand how to conduct a comprehensive risk assessment tailored to AI projects.

Part 4: Establishing a Responsible AI Committee

A dedicated Responsible AI Committee (RAIC) can be key to successful AI governance. We will discuss the role and responsibilities of an RAIC, including formulating and overseeing the organization's Responsible AI (RAI) principles. Readers will learn why these principles matter and explore examples of what they might entail, providing a foundation for ethical and responsible AI practices.

Part 5: AI and the Workforce

AI has the potential to significantly impact the workforce, and governing this relationship is crucial. This part of the series will focus on the human element, considering topics such as AI's effect on jobs, skills, and ethical considerations in AI deployment. We will offer guidance on managing this transition and ensuring a positive outcome for employees.

Part 6: Privacy and AI

With AI's ability to process vast amounts of data, privacy becomes an even more critical concern. Here, we will explore the intersection of AI and privacy, discussing best practices for ensuring data privacy and compliance. We will also delve into the potential risks and harms of AI in this context and offer strategies to mitigate them.

Part 7: AI for Executives and Boards

AI governance is a topic that reaches the highest levels of an organization. This section is tailored for executives and board members, providing an overview of their unique roles and responsibilities in sponsoring AI governance effectively. We will offer insights into strategic decision-making, risk oversight, and ensuring AI aligns with the organization's goals and values.

Part 8: The Role of the Chief AI Officer

The emergence of the Chief AI Officer (CAIO) role underscores the importance of AI governance. In this final part, we will look at the responsibilities and challenges faced by CAIOs, offering insights into establishing and succeeding in this pivotal position. We will also discuss how the CAIO can drive AI governance strategies and ensure their organization's AI journey is ethical and responsible.


AI governance is a dynamic and multifaceted field, and by delving into these nine parts, INQ aims to provide a comprehensive roadmap for organizations embracing AI.

Join us this June as we unlock the potential of AI through effective governance, exploring practices that foster innovation, trust, and ethical responsibility. Together, let's shape a future where AI drives progress while respecting the values and well-being of individuals and society.

Stay tuned, and let's embark on this exciting journey together!


INQ’s portfolio of AI services is customized to fit your specific needs and get you AI-ready. To learn more, visit our website at or contact us at To keep up with the latest in AI news, subscribe to the Think INQ newsletter.

80 views0 comments


bottom of page