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AI Governance Essential for Manufacturing Digital Transformation

by - ThoughtFocus | October 12, 2023 |

Artificial Intelligence (AI) and Generative AI abilities significantly transform our approach to data management, governance, and business operations. This article explores the significant shift AI will have on conventional data governance. 


Data Governance is a systematic method of managing data within a company to guarantee that it is accessible, reliable, accurate, and secure. It involves establishing and enforcing guidelines and regulations for handling data, maintaining its quality, and determining the appropriate access and usage rights for individuals based on specific circumstances and methods.   


There are various tools and resources available to support data governance. These include tools for managing data quality, analyzing data to identify issues, protecting sensitive information, managing metadata, maintaining a single view of critical data entities, ensuring data security and privacy, cataloging data, defining and managing business terms, and providing a framework for business processes. Additionally, a data dictionary defines an organization’s meaning, source, usage, and format of data elements.  


AI governance is the next step after data governance, and it involves establishing a system for responsible and ethical use of AI. This system must consider factors like fairness, interpretability, privacy, security, robustness, and accountability in AI models and applications. To achieve this, policies and guidelines must be developed to regulate the creation, usage, sharing, and maintenance of AI and machine learning models.  


AI governance is crucial in the enterprise due to the growing adoption of AI technology.  


Concerns regarding AI transparency, bias, ethics, and societal impact are becoming increasingly significant. AI models can make discriminatory decisions against specific groups or be utilized in ways that violate privacy. Hence, AI governance is essential to effectively address these concerns and ensure AI’s ethical and responsible use.  




Generative AI is a branch of AI that uses machine learning to generate new content, such as text, images, code, and designs. It has various applications, including writing articles, creating artwork, and making personalized recommendations.  


The rise of Generative AI has implications for data and AI governance.  


It leads to a larger and more diverse amount of unstructured data, requiring better data management practices. However, it also introduces new ethical and practical challenges for strong AI governance.  


For example, Generative AI can produce misleading or harmful content and potentially violate copyrights and intellectual property rights. It can also automate decision-making processes that were previously done by humans, raising concerns about accountability and fairness.  




As enterprises transition from data governance to AI governance, they adopt new tools and technologies powered by AI. While traditional data governance tools are still relevant, these new AI-powered tools automate and optimize data governance processes, improving efficiency and reducing errors.  


For example, AI-powered data catalogs can automatically discover and classify data, identify patterns, and address data quality issues. Similarly, tools for AI governance help manage the lifecycle of AI models, including tracking their lineage, monitoring performance, addressing bias, and ensuring regulatory compliance.  




  • A large trading firm used to manually catalog their data, which was a slow and error-prone process. However, they introduced an AI-powered tool that automatically identifies, organizes, and categorizes data from different sources. This improved the speed and accuracy of data cataloging and allowed their data stewards to focus on more important responsibilities. 


These examples illustrate how Generative AI can influence the governance practices of businesses. By utilizing AI-driven tools, companies can automate and optimize their governance processes, improving efficiency and reducing mistakes. It will be crucial for organizations to integrate data governance and AI governance practices to effectively manage their data and AI assets.  


As AI becomes more widespread in enterprises, using AI governance tools will become standard practice. However, organizations must also address challenges such as understanding AI models, managing ethical considerations, complying with regulations, and safeguarding data privacy and security.  


By taking a proactive and strategic approach to AI governance, businesses can harness the potential of AI to transform their operations while ensuring ethical and responsible use of this groundbreaking technology.  




Although AI governance has the potential for significant benefits, several challenges need to be overcome.  


One challenge is the lack of understanding and expertise in AI and machine learning models, making it difficult for organizations to effectively build and manage these systems.  


Another challenge is managing the ethical considerations associated with AI, such as fairness, transparency, and bias avoidance. Additionally, complying with the complex regulatory environment surrounding AI is a challenge, especially for international organizations operating in multiple jurisdictions.  


Data privacy and security also pose challenges, as AI systems require large amounts of data and must adhere to privacy regulations.  


Despite these challenges, embracing AI governance can lead to transformative outcomes while minimizing risks. As Generative AI and AI governance become more prominent, businesses have the opportunity to innovate and differentiate themselves.  


Companies that effectively utilize these technologies while managing risks will be well-positioned for success. Regarding governance trends, we expect to see increased adoption of AI governance tools, integration of data governance and AI governance, standardization and regulation, and a focus on continuous learning and adaptation.  


However, challenges include a lack of understanding of AI, ethical considerations, and data privacy and security concerns. Despite these challenges, embracing AI governance can lead to transformative outcomes while minimizing risks.  




To overcome these challenges, businesses need to take a proactive and strategic approach to governing artificial intelligence (AI). This involves investing in education and training to enhance internal AI capabilities, establishing ethical guidelines for AI use, implementing strong data management practices, staying updated on regulatory changes, and utilizing AI governance tools. These measures will help organizations effectively manage the complexities and risks associated with AI while ensuring compliance and ethical use.  




In summary, the rise of Artificial Intelligence and its ability to generate new content greatly change how we handle data management, governance, and business operations. The shift from traditional data governance to AI governance involves using new tools and technologies, such as Generative AI, to automate and improve data governance processes. This can lead to greater efficiency and fewer mistakes. As AI becomes more common in businesses, it is important to integrate data governance and AI governance practices to effectively manage data and AI assets. By taking a proactive and strategic approach to AI governance, organizations can leverage the power of AI to transform their business while ensuring ethical and responsible use of this technology.