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As AI adoption continues to grow, governance models have become more dynamic.
As AI continues to proliferate in its adoption across industries and applications, effective leaders have shifted from relying on traditional risk management and governance protocols to more dynamic models, leveraging AI’s potential while protecting the organization.
With thanks to a collaboration with WTW’s Kyle Heurich, leaders take the following actions as they transition to new models:
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Understand where traditional governance models fall short – Classical governance structures and processes often fail to address AI’s unique challenges and opportunities. Often, they cannot keep pace with the rapid advancements in AI technologies or the nature of large language models (LLMs). Typical hierarchical or static governance models generally provide stability and predictability through clear and centralized decision making. However, these models may not adequately cover data protection, product development, ethics and customer/user protection in a quickly changing and interconnected environment. Effective leaders don’t just bolt dynamic features on top of traditional governance systems – rather they build on what works and then fill gaps to create more dynamic models that meet the needs of AI and related tools.
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Establish clear and meaningful guiding principles – AI requires governance models that adapt and evolve in a constantly changing landscape where traditional rules-based constructs can be inadequate for addressing the daily risks of AI. As a result, effective leaders adopt guiding principles for dynamic governance models that allow their organizations to benefit from AI technologies while reducing risks and increasing trust and accountability. Well-constructed guiding principles enable dynamic models to provide a more agile and sustainable approach, allowing leaders to identify and act on internal and external signals and respond quickly to the changing regulatory and risk landscape. Meaningful guiding principles may include transparency, integrity, fairness, accountability, legal/regulatory compliance, safety, privacy, stakeholder engagement, and bias management.
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Create a mindset of responsible AI governance – Generally speaking, “responsible AI” is the process of developing and operating artificial intelligence systems that align with organizational purpose and values, while achieving desired business impact, in a dynamic manner. In addition to focusing on flexibility and agility, effective leaders use guiding principles focused on who they are as an organization and how they want to operate to achieve business objectives ethically and safely.
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Balance centralization and decentralization – Finding balance in dynamic models can be difficult. Too much decentralized decision making can result in a lack of cohesion and direction as well as an increase in risk. Too much centralization creates delays, bureaucracy, missed opportunities for innovation, and missed learnings in the field. Effective leaders navigate this delicate balance, ensuring their organization can act decisively when necessary while promoting responsible governance.
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Develop dynamic protocols – Dynamic governance models are flexible and responsive, with mechanisms for regular updates, feedback loops and continuous improvement. These attributes allow leaders to tailor governance practices to their AI objectives and adapt to internal and external changes. Doing so enables models to remain effective and relevant both the short- and long-terms. Effective leaders build dynamic governance models that include the following protocols:
- Flexibility: Adjust governance frameworks in response to changing circumstances
- Responsiveness: Quickly address emerging issues and capitalize on new opportunities
- Continuous improvement: Ensure governance methods remain effective and relevant
- Feedback loops: Enable organizations to gather input from stakeholders and adjust their governance frameworks accordingly
- Regular updates: Reflect the latest developments in AI technologies and best practices
- Experimentation and innovation: Promote innovation by enabling organizations to experiment with new approaches and technologies in a balanced manner
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Focus on execution – Implementing a dynamic governance model requires disciplined execution that aligns with the organization’s overall AI strategy and objectives. Effective leaders follow key steps including:
- Assessing readiness to evaluate the organization’s current governance practices and identify areas for improvement
- Defining objectives of the dynamic governance model, including its scope, goals and key performance indicators
- Engaging stakeholders, including senior leadership, AI practitioners and external experts, to obtain feedback and ensure buy-in
- Implementing mechanisms for regular updates, feedback loops and continuous improvement, such as regular reviews and audits
- Monitoring and evaluating the dynamic governance model as well as making adjustments as needed to ensure continued relevance and effectiveness
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