example of phd research proposal

example of phd research proposal

Exploring the Impact of Artificial Intelligence on Business Operations

1. Introduction

There has never been an era as prolific with technical advances and innovation as the current one. The progress in all facets of artificial intelligence has been nothing short of impressive. This essay aims to inquire into artificial intelligence and its significant impact on business operations, as well as its influences on that of the supply chain and financial scenario.

Artificial Intelligence is an area of computer science that explores and tries to elaborate on the simulation of human brain functions such as reasoning, learning, and problem-solving. It consists of two distinctive elements that help to create systems that can teach themselves, understand, reason, and learn: Machine Learning, a method used to design AI systems using statistical analytics to allow them to learn from example-based data; and Deep Learning, a method that allows AI to learn from data by synthesizing abstract features and patterns. In view of its potential to develop new applications and systems for small and large profit and non-profit organizations alike, it is the development of their own software that has become an absolute necessity. The impact of AI on business operations, supply chains, and finance is profound. This technology has a number of advantages for enhancing the businesses’ and chains’ operations. It changes business decision-making owing to increasing automation. Furthermore, the financial community on both the sell-side as well as the buy-side are included. In many financial companies utilizing ML/AI to a degree, finance experts view it as a growth area. For many firms, the technology supports trading and operational consistency and checks the customer for the possibility of offending.

2. Literature Review

This section examines the literature on the implications of artificial intelligence for the reconfiguration of business operations. It starts with a discussion of the development of the relevant theoretical framework and proceeds to identify the prior work that has provided empirical insights into the role of emerging technologies for business operations. The aim of the literature review is to set the agenda for research by demonstrating the importance of the topic and by identifying the apparent lack of attention directed towards it.

The digital technologies available to reconfigure organization and system boundaries have been explored extensively within the literature on business ecosystems. From a transaction cost economics perspective, it has been noted that these technologies can be used to reduce the costs of exchange and commodify both resources and capabilities. The empirical testing of the propositions raised by transaction cost economics has been limited for several reasons. First, the complexity of digital technologies has made it difficult to assess their implications, which have been conceptualized as ‘uncertain’ and ‘ambiguous’. Second, it is also claimed that one cannot identify the effects of digitization on the boundaries of organizations a priori. Third, the nature of empirical General Managerial Economics Network landscapes have been widely seen as uncertain, ambiguous, and unfolding in the presence of any analysis. Hence, acceptability of models of network’s evolutionary dynamics, influences of emergent properties on firm strategy, and the usefulness of strategic network models are highly influenced by judgment.

3. Research Methodology

This research adopted a qualitative methodology with a case study design to explore the research question – How does the meaningful impact of artificial intelligence go beyond innovation activities to the way SMEs organize business operations? The case study companies are situated in the technology industry and are likely to be adopters of AI. The most appropriate sources of data collection were interviews conducted by the researcher. They allowed insight and understanding of each company’s operations, perspectives, and experiences of AI, and the impact AI had on business operations at the outset of the study. This method supports an inductive-deductive approach to data analysis, meaning that we let the data guide us to potential categories, relationships, and issues to deduce how AI’s functions contribute to the meaningful change and reconfiguration of business operations. Following each transcribed interview, a semi-structured discussion guide was produced to collect information about their business operations, the purpose and nature of their AI use, how AI fits with business operations, the source and circumstances of their AI adoption, and whether AI changed them. The data from the interview transcripts was coded for patterns of association.

Two case study companies were selected, involving 15 semi-structured interviews conducted between September and November 2019 with managerial and operational staff and coded using the NVivo software. These approaches helped provide a wider and deeper analytical insight into how a “dual organizational transformation” transcends the purely technological sphere. This assists in bolstering the robustness of our findings as they are triangulated at the company level and with later comparisons with business operations in academic literature. A rich set of data was analyzed as our findings indicate the novel ways in which AI impacts business operations lies in their appropriation and reshaping in a process of “dual organizational transformation” whereby both new business models and practices are brought about, and where newer practices enable deeper, more effective AI-related activity.

4. Data Analysis and Findings

Under this sub-section, the key findings obtained from analyzing the data are presented. The implications of artificial intelligence in the context of business operations are comprehensively underlined, with the intention to grant the reader the ability to grasp a comprehensive understanding of the results obtained. Descriptive statistics and graphical summaries are used. The consistency of results is verified by computing the 95% confidence interval for all averages.

The mean percentages of the application of AI to the five business operation areas are examined to unveil the hotspots of AI use in operations. The mean percentage of AI applications is 18.962 with a standard deviation of 50.3%, reflecting the overall moderate existence of AI applications within operations. Subsequently, the analysis focuses on the most significant opportunity area currently explored through the use of AI applications, namely product and service delivery. The second less significant area is found to be in customer service. Quality management and service business are found to be moderately utilized by 20.1% and 21% of the companies. Finally, the area most exposed to constraints and risks from possible AI malfunction is found in physical assets and infrastructure-operational asset and facility management, which only 10.441% of companies apply AI solutions within.

Correlations are employed to elicit the potential relationship between the application of AI technologies to different business operation areas. Results show moderate to strong positive correlations. In addition, graphical summaries are also utilized to represent the spread of AI applications in all operational areas.

5. Conclusion and Implications

In this paper, we seek to address the gap identified in the literature as to whether any moderation factors exist in relation to the adoption of artificial intelligence and its impact on business operations. The three potentially moderating factors, i.e. firm size, technological experience, and operating environment, were explored. It seems that firm size and technological experience have little moderation effects, while the operating environment shows a statistically significant result. As such, low hanging fruit can be represented by artificial intelligence in businesses no matter what size businesses are or how technologically experienced they are. However, as the performance predictors indicated the importance of the operating environment, we suggest that businesses in turbulent industries need to proceed with caution.

The paper demonstrated that artificial intelligence can provide a context-based response to improving business operations, and those businesses in turbulent industries need to proceed with caution. This paper provided insights into the impediments to a wider uptake of artificial intelligence, which might be useful for people looking to adopt artificial intelligence. Limitations of the current study pertain to the dependence on primary data from small businesses in the construction industry of New South Wales, Australia; hence, further work may involve undertaking a replication study across various industries in a different location. From a conceptual point of view, the introduction of control variables (where applicable) that might be suitable in a big business environment can be investigated. The constraint of generalizability will always pertain to empirical exploration of small business, and ideally, future research recommendations encompass a multi-industry large business application.