Written by: Michael Curcurito, Jonathan Daggett
What is AI Governance?
Artificial Intelligence (AI) governance refers to the framework, policies, and regulations that govern the development, deployment, and use of artificial intelligence technologies. As AI continues to advance and permeate various aspects of our businesses, it has become imperative to establish a comprehensive governance framework that addresses risk, ethical, legal, and societal concerns associated with this transformative technology.
At its core, AI governance aims to strike a balance between fostering innovation and ensuring responsible AI use. Therefore, there needs to be guardrails in place for implementing this new technology into your organization that is in line with your business strategy and risk appetite.
Implementing an AI Strategy
In any business, the aim of AI should be to accelerate goals and enhance innovation. To achieve this, the use of AI and the AI strategy within your organization must derive from the business strategy. In summary, for AI to be effectively implemented, there must be a business reason behind it. If it is unclear what AI is and how your organization should be utilizing it, then you should take a step back to define what AI is to you, how your organization plans to use it, and why you plan to implement it. After identifying the AI opportunities and aligning it with your business strategy, this will help facilitate prioritization of use cases and resource allocation. These aspects will help limit the risk of wasted time and resources, since there will be a path for your organization to use AI with a common vision and goal for this evolving technology.
Nevertheless, these visions and goals should be expressed from a top-down approach. Organizations must consider leveraging existing internal overseeing bodies or creating an oversight body to steer the implementation and governance of AI. Therefore, a top-down approach to AI implementation will help facilitate a unified deployment across your organization and provide the structure needed to confidently implement these technologies.
The risk of a bottom-top approach is that there is a lack of a shared goal, and individuals or departments are implementing AI differently that causes unbalanced resource allocation or misaligned priorities to the business goals. Additionally, this creates a poor foundation since there is no shared structure in how to implement and maintain this technology. While this emerging technology will continue to change and advance, it is critical to have a unified approach as the organization increases AI efforts. Therefore, below we detail a step-by-step process to consider when implementing AI governance:
8 Components of an AI Strategy
- Business objectives: Define clear business objectives that AI will support, such as revenue growth, operations efficiency improvement, cost reduction, or customer satisfaction. This should already be built out in a business strategy. Additionally, use the existing strategy to build on and involve key stakeholders from various departments, including IT, operations, marketing, and legal to collaboratively develop a comprehensive AI strategy. This strategy should outline your organization’s goals, desired outcomes, and the specific areas where AI can be beneficially integrated.
- Data strategy: Specify how data will be collected, stored, and managed to support AI initiatives, since data quality and security are critical. When selecting an AI model think about what data is going into the tool, what format (text, pictures, pdf, etc.), how you are going to redact sensitive data, and then train the tools inputs and outputs.
- Technology stack: Identify the AI technologies and tools needed, such as machine learning frameworks, cloud platforms, and data analytics tools.
- Talent and skills: Assess the skills and expertise required for the AI tools and governance. From there, invest in training and upskilling employees to bridge the AI knowledge gap within your organization. Then, assign AI champions within your organization to lead people that need guidance when navigating AI initiatives.
- Governance: Establish an oversight body and create policies for data privacy, compliance, and ethical AI use. This includes adding to the organizations acceptable use policy by creating an AI Policy.
- Project portfolio: Develop a roadmap with a prioritized list of AI projects aligned with business objectives and risk appetite. In addition, identify and prioritize use cases where AI can have the most significant impact. These use cases should align your organization’s core competencies, customer needs, and industry trends. Therefore, prioritization will help allocate resources effectively and avoid spreading efforts too thin.
- Metrics and KPIs: Define key performance indicators (KPIs) to measure the success and impact of AI projects, and continue or start with pilot AI projects that align with the prioritized use cases. By implementing AI initiatives in an iterative manner, it will allow for continuous learning and improvement. However, you must regularly monitor and measure the outcomes to ensure that they align with the expected business benefits.
- Feedback channels: These channels help encourage continuous improvement of AI technologies. Your organization should establish a continuous improvement process to accept feedback and measure the performance of the AI tools and use cases. Thus, when there is an interruption, or the AI tool is not working as intended, there will be a process to recollect the tool, return it to the desired state or improve it.
Aligning AI Strategy With Business Strategy
- Strategic alignment: Ensure that the AI strategy aligns with the broader organizational goals and objectives. AI initiatives should complement and enhance the business strategy rather than operate in isolation.
- Stakeholder engagement: Engage key stakeholders, such as executives and department heads to gather input and ensure alignment with their departmental strategies.
- Risk assessment: Assess the potential risks associated with AI initiatives, including data security, ethical concerns, and regulatory compliance. Align AI projects with your organization’s risk appetite and establish risk mitigation strategies.
- Resource allocation: Allocate resources (budget, talent, and time) based on the prioritized AI projects and their potential impact on the business strategy.
- Change management: Implement change management practices to ensure that employees understand and support AI initiatives, since these may involve new processes and workflows.
- Continuous evaluation: Regularly evaluate and adjust the AI strategy to stay in sync with evolving business needs and emerging AI technologies.
What the Future Holds
In a rapidly changing world, effective AI governance ensures that organizations harness the power of artificial intelligence responsibly and in a manner that supports their long and short-term business goals. By following the steps outlined in this article, organizations can navigate AI governance successfully, foster innovation, and enhance their business initiatives.
