The Impact of Artificial Intelligence on Organizational Operations

Running head: ARTICLE ANALYSIS
ARTICLE ANALYSIS
Name of the Student
Name of the University
Author Note

1ARTICLE ANALYSIS
Introduction
The most important factor that has been analysed in the article is based on the ways
by which AI or artificial intelligence can transform various industries. The growth that is
experienced in the GDP rates of different countries is predicted to increase further within the
year 2030. A major rise has been depicted in the application of artificial intelligence in
different organizational operations. The extensive usage of artificial intelligence has a major
effect on the profitability levels and revenues. The increasing importance of artificial
intelligence or AI is an issue that has been examined in the article (Arnott, Lizama and Song
2017).
Review based on the findings of the study
The findings of the case study are mainly based on effective levels of implementation
of the technologies that are related to the pilot projects that have been started by different
modern organizations. The questions are raised in this case are based on the ways by which
artificial intelligence can affect the different processes in the organization. The gains that are
received by the modern organizations are based on the short term and the long term based
operations that are conducted in different parts of the world. AI implementation is considered
to be a key issue that can have an influence on the levels profitability (Cockburn, Henderson
and Stern 2018).
Discussion based on the literature review
As discussed by Davenport and Ronanki (2018), artificial intelligence is the process
that is implemented by the organizations in order to develop the effective levels of simulation
of the intelligence levels of humans along with the usage of machines in the various
processes. The major concepts that are able to influence the processes of the modern

2ARTICLE ANALYSIS
companies include, reasoning, learning and the process of self-correction (Dedić and Stanier
2016). The article has been able to provide major traits that are based on the proper
implementation of AI by the leaders or managers. The capabilities related to AI or artificial
intelligence have an impact on the successful operations of the organizations. The proper
growth of the pilot projects is based on the ways by which success can be ensured to the
organizations in an effective manner (Cockburn, Henderson and Stern 2018).
As discussed by Foster et al. (2015), the major issues that are considered in order to
analyse the proper development of the AI based pilot projects are mainly based on some
important factors that are, ability of the implementation to offer short term value, importance
of the effective application of artificial intelligence (Gill and Phythian 2018). The
implementation of AI in the organizational operations is based on the experiences that have
been gained by the previous leader of the Google Brain Team. The AI project is also able to
gain momentum after processes are improved with the help of appropriate support that is
provided to them. The significance of the choice based on the projects is considered to be
specific based on the industry and operations of different organizations (Hendler and
Mulvehill 2016).
According to Li and Du (2017), the pilot project is able to provide value to the
customers and the employees with the help of proper implementation of the AI or artificial
intelligence in the modern organizations. The proper application of AI in the practical
operations of the organizations in their respective industries. The most significant part of the
article is based on proper and effective implementation of AI in the different organizations
(Liew 2018). The individuals have been able to develop different types of traits that are
required for the proper functioning of artificial intelligence related activities. Traits of
different individuals are considered to be an important factor is able to facilitate the was by
AI based processes can be implemented (Miorandi et al. 2016).