From Business Intelligence to Artificial Intelligence

Abstract

With today's growing information and overloading of its volume based on tremendous size of data growing to the level of big data, Business Intelligence (BI) is not enough to handle any day-to-day business operation of any enterprises. It is becoming tremendously difficult to analyze the huge amounts of data that contain the information and makes it very strenuous and inconvenient to introduce an appropriate methodology of decision-making fast enough to the point that it can be, considered as real time, a methodology that we used to call it BI. The demand for real time processing information and related data both structured and unstructured is on the rise and consequently makes it harder and harder to implement correct decision making at enterprise level that was driven by BI, in order to keep the organization robust and resilient against either man made threats or natural disasters. With smart malware in modern computation world and necessity for Internet-of-Things (IoT), we are in need of a better intelligence system that today we know it as Artificial Intelligence (AI). AI with its two other subset that are called Machine Learning (ML) and Deep Learning (DL), we have a better chance against any cyber-attack and makes our day-to-day operation within our organization a more robust one as well makes our decision making as stakeholder more trust worthy one as well.

Artificial Intelligence at Work: Transforming Industries and Redefining the Workforce Landscape

“…Organizations recognize the importance of upskilling and reskilling initiatives, fostering a proactive approach to the changing employment landscape. The automation-driven job transformation is not just about adapting to technological changes but embracing a future where humans and machines collaborate synergistically, creating a workforce that is agile, innovative, and capable of harnessing the full potential of AI-driven automation [4][5][6].…”

“…In retail and healthcare industries, AI algorithms are at the forefront of creating tailored and immersive interactions for customers. This personalized approach is redefining the customer experience landscape, allowing businesses to move beyond a one-size-fitsall model to cater to individual consumers' unique preferences and needs [1][2][3][4][5][6].…”

The role of machine learning in transforming business intelligence

“…It involves the integration of various strategies, technologies, and tools to transform raw data into valuable information that drives organizational performance and competitiveness. The process of BI starts with data collection, where relevant data is gathered from multiple sources, both internal (such as databases, enterprise systems, and transactional systems) and external (such as market research reports, social media, and customer feedback) [3] . This data is then processed through data integration, where it is consolidated, cleansed, and transformed into a consistent and usable format.…”

“…patterns and trends to forecast and estimate future trends, behaviors, or events [3] . Predictive analytics has become an essential component of BI, enabling organizations to gain valuable insights, make informed decisions, and improve their business strategies.…”

Views on Deep Learning for Medical Image Diagnosis

“…Making computers smarter is the goal of artificial intelligence which is a branch of computer science (Zohuri & Moghaddam, 2020). Learning is one of the fundamental prerequisites for any cognitive behavior (Garcia-Pelegrin et al, 2022).…”