Volume 43, Issue 2, August 2024, Pages 222–228
Bharath Reddy1 and Reginald Decastro2
1 Process Automation RandD, Schneider-Electric, Foxborough, Massachusetts, USA
2 Process Automation RandD, Schneider-Electric, Foxborough, Massachusetts, USA
Original language: English
Copyright © 2024 ISSR Journals. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
Today, we are at the cusp of transitioning or welcoming a new era of computer science called ‘Artificial Intelligence’ (AI). A plethora of cutting-edge Artificial Intelligence technologies would now help augment or replace older technologies ranging from customer service, business analytics, gain knowledge, recommend decisions and at times predict the outcome of the strategies being implemented in-house. The prediction is done by Machine learning models which use various modelling techniques unique to Artificial Intelligence to mine vast amount of data. This helps organizations focus on other important tasks like production efficiency, sales growth, customer base retention, seek out new strategies to grow the bottom line and improve their risk and safety protocols. We are seeing a lot of Artificial Intelligence models today from CHAT-GPT, Co-Pilot, Gemini and others which are integrated into their existing services. With the advent of AI, Microsoft’s Co-Pilot is a personal assistant that helps you find right information, faster than ever. So, the opportunities for a company to improve efficiency, make better business decisions to grow and expand are endless. One can see the number of automated bots ranging from car dealerships to telecom business today. These automated bots take the heavy burden of streamlining the customers to different categories and improve customer service. Some machine learning (ML) models can perform data analysis, detect anomaly, forecast cost expenditure, predict competition and dynamic pricing. In this paper, we would look in detail how ML and AI, is both an opportunity as well as a challenge for companies.
Author Keywords: Artificial Intelligence, Knowledge, Machine Learning, Data Analysis, Dynamic Pricing.
Bharath Reddy1 and Reginald Decastro2
1 Process Automation RandD, Schneider-Electric, Foxborough, Massachusetts, USA
2 Process Automation RandD, Schneider-Electric, Foxborough, Massachusetts, USA
Original language: English
Copyright © 2024 ISSR Journals. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
Abstract
Today, we are at the cusp of transitioning or welcoming a new era of computer science called ‘Artificial Intelligence’ (AI). A plethora of cutting-edge Artificial Intelligence technologies would now help augment or replace older technologies ranging from customer service, business analytics, gain knowledge, recommend decisions and at times predict the outcome of the strategies being implemented in-house. The prediction is done by Machine learning models which use various modelling techniques unique to Artificial Intelligence to mine vast amount of data. This helps organizations focus on other important tasks like production efficiency, sales growth, customer base retention, seek out new strategies to grow the bottom line and improve their risk and safety protocols. We are seeing a lot of Artificial Intelligence models today from CHAT-GPT, Co-Pilot, Gemini and others which are integrated into their existing services. With the advent of AI, Microsoft’s Co-Pilot is a personal assistant that helps you find right information, faster than ever. So, the opportunities for a company to improve efficiency, make better business decisions to grow and expand are endless. One can see the number of automated bots ranging from car dealerships to telecom business today. These automated bots take the heavy burden of streamlining the customers to different categories and improve customer service. Some machine learning (ML) models can perform data analysis, detect anomaly, forecast cost expenditure, predict competition and dynamic pricing. In this paper, we would look in detail how ML and AI, is both an opportunity as well as a challenge for companies.
Author Keywords: Artificial Intelligence, Knowledge, Machine Learning, Data Analysis, Dynamic Pricing.
How to Cite this Article
Bharath Reddy and Reginald Decastro, “Benefits of Machine Learning and Artificial Intelligence,” International Journal of Innovation and Applied Studies, vol. 43, no. 2, pp. 222–228, August 2024.
