Associate Director – Technology, Enterprise Architect , Synechron, The Netherlands
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.
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.
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:
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.
Before embarking on the journey from data governance to AI governance, organizations need to assess their current capabilities and readiness levels. Synechron employs a combination of proven qualitative and quantitative techniques to perform this comprehensive assessment. This involves:
The readiness assessment culminates in a detailed gap analysis that highlights both the strengths and weaknesses of the organization's current state in relation to AI governance. This analysis is pivotal for identifying critical areas for improvement and involves:
With the insights gained from the gap analysis, organizations can then develop a tailored AI governance framework that builds upon their existing data governance foundations. This involves:
Associate Director – Technology, Enterprise Architect
Marc van Dongen is an Associate Director of Technology at Synechron, The Netherlands.
Marc is a Senior Enterprise Architect with expertise in various areas including Enterprise AI Governance, Data & Analytics (D&A), improving architectural efficiency, and enhancing Security Architecture, among others. He is a trusted partner for client challenges related to regulatory compliance, evidence reporting & lineage, Governance, Risk, and Compliance (GRC), agile architectures and efficient architecture strategies. Marc holds a range of certifications such as a Master of Science in Informatics, AI Oversight, AI Analytics Lead, The Open Group Architecture Framework (TOGAF), Data Management Association (DAMA) Associate, Open Group’s IT4IT Service Management, Open Agile Architecture, certifications in Cloud & Security Architecture, Lean Six Sigma (Black Belt), and Scaled Agile Architecture.
For more information, reach out to him at: [email protected]
Or connect with him on LinkedIn: linkedin
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