Wijethilaka, Hewawasam P. G. D., Yadav, Mohit
and Vij, Rohit
(2024)
Optimizing business models in entrepreneurship: the role of AI in iterative business planning.
In:
Improving Entrepreneurial Processes Through Advanced AI.
IGI Global, pp. 71-98.
ISBN 9798369314968
Abstract
This chapter explores the role of Artificial Intelligence (AI) in optimizing business models through iterative planning. As AI continues to advance, it offers entrepreneurs powerful tools for data-driven decision-making, enhancing customer insights, market segmentation, dynamic pricing, and resource allocation. The chapter examines key AI technologies such as machine learning, natural language processing, and AI-powered simulations, which enable continuous refinement and adaptation of business strategies. It also addresses challenges such as data privacy, AI transparency, potential biases, and the balance between automation and human intuition. Ethical considerations, including the responsible use of AI, are discussed to ensure sustainable innovation. The chapter concludes by exploring future trends, including autonomous decision-making and the democratization of AI, emphasizing its potential to transform business models and drive long-term success.
Item Type: | Book Section |
---|---|
Keywords: | Artificial Intelligence (AI) | Business Models | Machine Learning | Data-Driven Decision-Making | Ethical Considerations |
Subjects: | Social Sciences and humanities > Business, Management and Accounting > Business and International Management Social Sciences and humanities > Business, Management and Accounting > Management of Technology and Innovation Physical, Life and Health Sciences > Computer Science Social Sciences and humanities > Social Sciences > Social Sciences (General) |
JGU School/Centre: | Jindal Global Business School |
Depositing User: | Users 37 not found. |
Date Deposited: | 28 Oct 2024 13:51 |
Last Modified: | 02 Jul 2025 09:21 |
Official URL: | https://doi.org/10.4018/979-8-3693-1495-1.ch004 |
URI: | https://pure.jgu.edu.in/id/eprint/8730 |
Downloads
Downloads per month over past year