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To That of Artificial Intelligence, Passing Through Business Intelligence
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Author(s): Ruchi Doshi (Universidad Azteca, Mexico), Kamal Kant Hiran (Sir Padampat Singhania University, India & Lincoln University College, Malaysia), Maad M. Mijwil (Baghdad College of Economic Sciences University, Iraq)and Darpan Anand (Sir Padampat Singhania University, India)
Copyright: 2023
Pages: 16
Source title:
Handbook of Research on AI and Knowledge Engineering for Real-Time Business Intelligence
Source Author(s)/Editor(s): Kamal Kant Hiran (Sir Padampat Singhania University, India & Lincoln University College, Malaysia), K. Hemachandran (Woxsen University, India), Anil Pise (University of the Witwatersrand, South Africa)and B. Justus Rabi (Christian College of Engineering and Technology, India)
DOI: 10.4018/978-1-6684-6519-6.ch001
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Abstract
Business intelligence (BI) is no longer adequate to handle the day-to-day operations of any firm considering the ever-increasing volume of data and the resulting overload. As the amount of data grows, it becomes increasingly difficult to evaluate, making the introduction of a decision-making methodology that can be described as real-time BI, very taxing and cumbersome. Because of this, it is becoming increasingly difficult to implement effective decision-making at the enterprise level that was driven by BI, so that the company may remain robust and resilient to both man-made dangers and natural calamities. With today's sophisticated malware and the growing importance of the Internet of Things (IoT), we require a more sophisticated intelligence system, which we currently refer to as Artificial Intelligence (AI). We have a better chance of surviving a cyber-attack thanks to AI and its two other subsets, Machine Learning (ML) and Deep Learning (DL). These technologies strengthen our organization's day-to-day operations and help us make more reliable decisions as stakeholders.
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