International Research Journal of Engineering and Technology (IRJET)
e-ISSN: 2395-0056
Volume: 11 Issue: 12 | Dec 2024
p-ISSN: 2395-0072
www.irjet.net
Governance Strategies for Embedding Responsible AI in Enterprise Digital Transformation Renjith Ramachandran1, Gaurav Sharma2 1Independent Researcher and Solutions Architect, Hudson, MA, USA 2Independent Researcher and Associate Director Quality Engineering, Atlanta, GA, USA
---------------------------------------------------------------------***--------------------------------------------------------------------Abstract - Artificial intelligence (AI) is becoming an essential component for enterprises. Many organizations are either in the process of digital transformation or have already completed it. The rise of AI provides enterprises with numerous tools and technologies that can boost the productivity of individual teams and enhance overall organizational efficiency. Since AI systems are data-driven and constantly seek additional data, the quality and quantity of this data are crucial to making AI applications more effective. As enterprises increasingly adopt AI tools and practices, embedding responsible AI practices is essential to ensure that AI applications are ethical, transparent, and aligned with organizational values. These practices help enterprises comply with standards while leveraging the full potential of AI.This paper examines governance strategies for integrating responsible AI into digital transformation initiatives across industries. It provides enterprises with frameworks to manage the ethical, legal, and operational challenges posed by AI. By implementing these governance strategies, organizations can effectively navigate the complexities of AI, build trust, and maximize AI’s contribution to digital transformation. Key Words: Digital Transformation, Intelligence, AI Governance
anything, these advancements come with significant risks. AI risks are multifaceted, encompassing both preimplementation challenges and post-deployment consequences. This paper explores how effective AI governance strategies should be developed and applied when embedding AI into enterprise digital transformation solutions. Additionally, it examines research findings on the challenges of designing and implementing these governance strategies, emphasizing their importance in mitigating AI risks. The paper highlights the global implications of these risks for technology, business, and humanity.
2. DIGITAL TRANSFORMATION All the sectors are undergoing or in the process of Digital Transformation. Post Covid Era, there has been increased demand for Digital transformation. Digital transformation is about using technologies to create value, enhance productivity and bring in social welfare [14]. It is also about the transformation of Organizational Activities both internal and external to leverage the advantages of Digital Technologies [16]. For example, patients are able to book doctors’ appointments online using websites or apps. Patients can even schedule virtual consultation with the doctor avoiding travel [15]. Another use case is the growth in the use of online platforms for education and learning. The concept of face to face learning has moved to more online or hybrid modes of learning. These types of innovations have created enhanced values to customers.
Artificial
1.INTRODUCTION Artificial intelligence (AI) is reshaping enterprise digital transformation, driving revolutionary advancements with innovative, AI-powered capabilities. The surge of AI technology in software development has resulted in significant breakthroughs in machine learning, deep learning, neural networks, and foundation models such as BERT (Bidirectional Encoder Representations from Transformers), LLMs (Large Language Models), and NLP (Natural Language Processing). These advancements have exponentially enhanced capabilities, transitioning AI from instruction-based systems to self-learning and predictive models.
2. CORE OF AI AI is built upon a foundation of diverse technological subsets, including Machine Learning (ML), Deep Learning (DL), Natural Language Processing (NLP), Foundation Models (FM), Large Language Models (LLMs), Small Language Models (SMLs), Neural Networks (NN), and Computer Vision driven by Convolutional Neural Networks (CNNs). As a comprehensive superset of these advanced technologies, AI utilizes a variety of algorithms to facilitate the creation of sophisticated models, such as LLMs, Text-to-Speech systems, Generative AI (GenAI) models, and Image Processing solutions. [11]
Modern AI models can process and generate outputs across multiple modalities, including text, images, audio, video, and vision, showcasing their Generative AI capabilities. However, with the ability to generate almost
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