Use Your Data Governance Framework to Boost Practical AI Governance

Introduction

By 2025, it is projected that the majority of financial services industry businesses worldwide will incorporate artificial intelligence (AI) into their operations. This would mark an unprecedented rate of adoption for such a transformative technology.

This trend is particularly remarkable considering the significant financial, legal, and reputational risks associated with AI. Despite its current nascent stage, the application of AI is expanding swiftly. Yet, the absence of a standardized framework for managing AI usage poses a challenge. What regulations should govern AI, and how can businesses ensure its safe and effective deployment?

A solution may already exist. Synechron's AI Governance proposition centers on expanding the existing data governance framework to rapidly establish dependable governance of AI.

Data Governance Frameworks

AI governance effectively extends the principles of data governance, which is the management of data availability, usability, integrity, and security within enterprise systems. Both frameworks share a commitment to policies, procedures, and practices that ensure proper data handling throughout its lifecycle. Since AI technologies are deeply rooted in data, the fundamentals of data governance play a critical role in shaping the development, deployment, and operation of AI systems. A robust data governance framework guarantees the accuracy, consistency, and security of the data used by AI algorithms, thus improving the reliability and performance of AI applications.

Nevertheless, while the two frameworks share similarities, responsible AI usage introduces new governance standards, expanding on data governance to cover ethical AI use, algorithm transparency, sustainability, and accountability. From a strategic standpoint, AI governance requires closer alignment with an organization's ethical beliefs and innovation goals to harness AI technologies effectively.

On the financial front, AI governance necessitates intricate risk evaluations concerning algorithmic bias, decision-making clarity, and adherence to AI-specific regulations, such as the recently approved EU AI Act from the European Parliament. FinOps - an operational framework and cultural practice that maximizes the business value of the cloud, enables rapid data-driven decision making, and creates financial accountability through collaboration between engineering, finance, and business teams - already concerned with optimizing business value, will place greater emphasis on Return on Investment (ROI) due to AI's higher energy demands. This shift is not just for budgetary reasons but also to meet regulatory sustainability goals outlined in Environmental, Social & Governance (ESG) criteria.

Key Dimensions of AI Governance

Organizations must consider these additional dimensions of AI governance -- affecting staff, processes, and technology -- using their data governance practices as a base. The focus should be on six key target areas:

1. Data Lifecycle Management: Vital for AI's reliance on quality data, ensuring accurate, consistent, and secure data management.
2. Foundation for AI Governance: Establishes the groundwork for AI governance through clear data management policies and procedures.
3. Ensuring Reliability and Relevance: Directly impacts AI system effectiveness by guaranteeing data integrity and relevance.
4. Compliance and Ethical Considerations: Incorporates legal and ethical compliance, crucial for ethical AI use and decision-making.
5. Risk Management: Identifies and mitigates risks related to data and AI, including privacy and security breaches.
6. Scalability and Futureproofing: Ensures AI systems can adapt and scale with organizational and technological growth.

Importance of Data Governance for AI

The interdependence of data and AI governance underscores the significance of comprehensive data lifecycle management and the establishment of a robust foundation for AI governance. As AI becomes more entrenched in organizational processes, the importance of data governance in ensuring trustworthy and efficient AI systems will only grow.

The expanded AI-data governance framework should show immediate value. Instead of a sweeping implementation, a gradual roll-out, beginning with high-priority areas is advisable, particularly in regulatory compliance. From a business adoption point-of-view, taking on hot topics like use case rationalization and data set selection can be interesting, as they need motivated business involvement. Starting with a readiness assessment will help prioritize key AI items. Performing a gap analysis on them and defining relevant measures will stepwise integrate AI governance into existing data governance practices effectively.