From Automation to Innovation: The Role of Generative AI in the Enterprise

Authors

Swetha Sistla

Abstract

The rapid evolution of generative AI is hastening a revolution in how enterprises think about automation, shifting the paradigm from traditional task-based automation to innovation-driven processes. This abstract explores the transition from automation to innovation, where generative AI plays the main role in a changed enterprise strategy, better productivity, and ingenious solutions for every industry. Early automation focused on repetitive and predictable tasks, while today's generative AI technologies-including LLMs-drive a range of advanced capabilities: empowering the enterprise to solve complex problems, craft highly personalized customer experiences, and accelerate decision-making.

Most generative AI goes beyond automating tasks by generating original content, surfacing data insights, and driving strategic plans-forward-thought tasks that have historically been strictly the domain of humans. It also has implications for roles across the board, from operations and customer service to research and development. Yet with these benefits come a number of challenges in managing ethics around AI-driven decisioning, ensuring data privacy, and upskilling the workforce to work in harmony with the AI systems. This paper explores some use cases of generative AI across industries, discusses some best practices that are emerging, and lays out a path that companies might take in order to onboard generative AI at their companies in a successful manner while encouraging innovation, accountability, and alignment to business objectives.

Keywords

Generative AI, Enterprise Automation, Artificial Intelligence (AI), Large Language Models (LLMs), Decision Making, Data Insights, Ethical AI, Business Transformation, Intelligent Automation, AI – Driven Innovation.

From Automation to Innovation: The Role of Generative AI in the Enterprise. Swetha Sistla. 2024. IJIRCT, Volume 10, Issue 4. Pages 1-6. https://www.ijirct.org/viewPaper.php?paperId=2411082