AI Governance Hub

Ensure your AI initiatives are ethical, compliant, and aligned with business goals. Discover how to create a robust governance framework for responsible AI deployment.

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Per Definition

AI governance is the framework for managing and regulating artificial intelligence to ensure ethical use, compliance, and accountability.

What is AI Governance?

AI governance refers to the frameworks and processes used to oversee and manage the development, deployment, and use of artificial intelligence systems.

It ensures AI initiatives align with regulatory standards, ethical guidelines, and organizational goals, while mitigating risks and enhancing accountability.

"It is necessary to understand AI before governing it."

AI Fundamentals – What is AI?

Artificial intelligence (AI) is the simulation of human intelligence processes by machines. These processes include learning, reasoning, and self-correction. AI systems function by ingesting large amounts of data, identifying patterns, and making predictions.

The primary goal of AI is performing tasks that typically require human intelligence in order to increase efficiency, improve decision-making, and rapidly process vast amounts of information.

AI applications encompass various technologies, including natural language processing, speech recognition, and image analysis.

The importance of AI Governance

Understanding AI before governing it is essential. You must be able to identify the technology’s capabilities and limitations in order to make informed decisions about its deployment.

This knowledge helps set ethical guidelines, ensure compliance with regulatory standards, and align AI initiatives with your organizational goals. When stakeholders comprehend how AI processes data, makes decisions, and learns over time, they can implement effective governance frameworks that mitigate risks such as bias, discrimination, and security vulnerabilities.

SAP LeanIX's AI Governance Survey 2024 reveals that almost all companies (90%) believe it is important to have a comprehensive overview of generative AI‘s use in the organization so as to facilitate effective governance. However, only 14% currently have such an overview. This means most organizations still have a long way to go when it comes to effective AI governance.

However, only 14% currently have such an overview. This means most organizations still have a long way to go when it comes to effective AI governance.

80 %

of companies are leveraging generative AI

90 %

of IT experts say they need a clear view of AI use in their organizations

14 %

say they actually have the overview of AI that they need

Report

SAP LeanIX AI Survey Results 2024

LeanIX surveyed 226 IT professionals working for organizations across the world to find out how they're dealing with AI adoption and governance.

Access the report

Building an AI Governance Team

Before your organization adopts AI, you should establish an AI governance team to oversee the organization’s AI systems. This team should be cross-functional to cover all aspects of AI governance, from technical implementation to ethical considerations.

It should also include stakeholders from various departments, including IT, security, legal, and business.

Enterprise architects (EAs) play a critical role in AI governance because they understand the organization’s IT landscape and strategic goals.

EAs ensure AI initiatives are integrated seamlessly into existing systems and align with the overall IT landscape.

Furthermore, their ability to bridge the gap between technical and business perspectives is vital for developing a governance framework that supports innovation while maintaining compliance and security.

Chief AI Officer or AI Governance Lead

Oversees the entire AI governance framework and ensures alignment with organizational goals.

Enterprise Architects

Ensure AI initiatives are integrated seamlessly into existing systems and align with the overall IT landscape.

Data Scientists and AI Engineers

Responsible for developing, deploying, and maintaining AI systems.

Compliance and Legal Experts

Ensure AI systems comply with relevant laws, regulations, and ethical standards.

IT Security Professionals

Safeguard AI systems against security threats and vulnerabilities.

Business Analysts

Align AI initiatives with business strategies and identify areas for AI integration.

AI Governance Frameworks

An AI governance framework is a structured set of guidelines and practices designed to ensure the ethical, transparent, and compliant development, deployment, and management of AI systems.

They help organizations align AI initiatives with legal standards, business goals, and ethical principles, while mitigating risks and promoting accountability.

Choosing the right AI governance framework depends on several factors, including the specific needs of your organization, the regulatory environment you operate in, and the nature of your AI applications.

Some of the most common established frameworks include:

Organization for Economic Co-operation and Development (OECD) Principles on AI

EU Ethics Guidelines for Trustworthy AI

NIST AI Risk Management Framework

SO/IEC JTC 1/SC 42

Artificial Intelligence Governance and Auditing (AIGA) AI Governance Framework

"Understanding key areas is crucial when building or following an AI governance framework."

1. Key Principles

There some key areas of any AI governance framework that are necessary to understand when building a new framework or following an existing one

The key principles that guide AI governance frameworks typically include:

1. Accountability: Establishing clear responsibilities and accountability for AI system outcomes.

2. Transparency: Providing clear, understandable information about how AI systems operate and make decisions.

3. Explainability: Ensuring that the workings of AI systems can be understood by humans to validate decisions and identify biases.

4. Data Privacy and Security: Protecting personal data and ensuring robust security measures are in place.

5. Compliance: Adhering to relevant laws, regulations, and standards governing AI use.

6. Inclusivity and Fairness: Ensuring AI systems do not discriminate and are inclusive of diverse groups.

7. Robustness and Safety: Developing AI systems that are reliable, safe, and resilient against misuse or attack.

8. Reproducibility: Ensuring AI systems can consistently produce the same results under the same conditions for validation and reliability.

9. Auditability: Enabling systematic checks and assessments to review and analyze AI systems for transparency and accountability

10. Continuous Monitoring and Improvement: Regularly evaluating and updating AI systems to ensure they remain effective and aligned with governance principles.

2. Data governance

Data governance is crucial in AI because the quality, timeliness and integrity of data directly impacts the performance and reliability of AI systems.

Without accurate data, AI applications cannot make accurate decisions. Proper data governance also ensures that data is managed effectively, protected from breaches, and compliant with data privacy laws and other regulations.

3. Risk management

AI can drastically increase and enhance innovation and efficiency, but it also presents several risks that organizations must manage, including privacy, security, compliance, technological limitations, and bias and discrimination.

4. Legislation

There are a myriad of different AI regulations and policies across many different countries and regions, including various US states. These laws cover a range of issues including data privacy, algorithmic transparency, accountability, and the mitigation of biases and discrimination in AI systems.

Staying up to date on these regulations is crucial for ensuring global compliance, avoiding legal penalties, and effectively navigating the diverse regulatory landscapes that impact international business operations and innovation.

5. Adoption and integration

AI adoption involves selecting and scaling AI technologies that align with an organization’s strategic goals and governance policies. It focuses on integrating AI into the business environment to drive value.

AI integration, meanwhile, embeds these technologies into existing systems, ensuring they work seamlessly with current processes and data sources.

Both AI adoption and integration require strategic planning, robust governance, effective data management, system interoperability, and ongoing monitoring to ensure AI delivers on its potential and remains aligned with business objectives.

Our view:
AI governance will require the right EA solution

EA can provide a structured framework for implementing and governing generative AI effectively across the organization while aligning AI strategies with business goals. When combined with the right EA solution, you will have the visibility, data, and cross-team coordination you need to integrate AI seamlessly into existing systems and processes, ensure compliance with all relevant regulations, and manage risks associated with AI deployments.

In other words, the right EA solution must have certain capabilities to handle the new challenges generative AI presents. Your solution enable EAs to:

Provide a complete overview of AI across the organization

The right EA solution must be able to provide an inventory of all the places generative AI is being leveraged in your IT landscape.
Track AI across the IT landscape

Your EA solution should provide an accessible, single source of truth that allows you to track AI, from who uses it to how it connects with other elements of your tech stack.
Model LLMs

Your EA solution should allow EAs to document each element of the LLMOps workflow and visualize how the components of your LLMOps toolchain interact with and depend on each other, as well as other IT systems.
Envision your future AI-enhanced state

The right EA solution should enable enterprise architects to prioritize where to integrate AI based on potential impact and alignment with strategic objectives.