Assessment of AI Companies Operational Performance in India

Title

Assessment of AI Companies Operational Performance in India

Creator

Singh, Shanu; Awasthi, Yashmita

Description

The article has delved into understanding the importance and performance of Artificial Intelligence companies in India with the data of the past five years. It scrutinizes the performance and significance of Artificial Intelligence (AI) companies in India over the past five years. It delves into the financial data, specifically examining Profit After Tax as an independent variable and its relation to net cash flows in these companies. The analysis involves nine prominent AI companies in India and employs statistical tools such as correlation, regression, standard deviation, and one-way ANOVA. The findings indicate varying relationships between Profit After Tax and net cash flows across different companies, underscoring the complexities within their financial dynamics. While some companies exhibit a positive correlation, others show no direct relationship. Additionally, the article explores government guidelines and existing laws that impact AI companies in India, emphasizing ethical and responsible AI usage. Despite the evolving market, the article suggests a promising future for AI companies in India, contingent upon their ability to tailor solutions to the unique challenges and opportunities in the Indian landscape. 2025 Author(s).

Source

AIP Conference Proceedings;Volume;3224;Issue;1;Article No.;20001;

Date

01-01-2025

Publisher

American Institute of Physics

Subject

AI Solutions; Artificial Intelligence Companies; Net Cash Flow; Operational Performance; Profit After Tax

Coverage

Singh S., School of Commerce, Finance & Accountancy, Christ (Deemed to be University), Delhi-NCR, Mariam Nagar, Meerut Road, Ghaziabad, 201003, India; Awasthi Y., School of Commerce, Finance & Accountancy, Christ (Deemed to be University), Delhi-NCR, Mariam Nagar, Meerut Road, Ghaziabad, 201003, India

Rights

Restricted Access; Hardcopy may be available in the library

Relation

ISSN: 0094243X; ISBN: 978-073545120-9;

Format

online

Language

English

Type

Conference paper

Identifier

https://doi.org/10.1063/5.0245928

https://www.scopus.com/pages/publications/85218694482?origin=resultslist