a phd research proposal sample

a phd research proposal sample

Exploring the Impact of Artificial Intelligence on Business Operations: A Phenomenological Study

1. Introduction

The use of artificial intelligence technologies has been embraced rapidly by a majority of large and small businesses. Technology experts have argued that the use of artificial intelligence in business operations has the potential to cause entire industries to become obsolete due to the disruption it is capable of bringing about. This research employed a qualitative phenomenological approach to understand the real impact and emotional response of current business managers and how their respective business organisations have innovated and integrated AI technology. Key business stakeholders participated in a semi-structured one-on-one online discussion to provide in-depth insights into the phenomena. A clear and consistent depiction of the impacts, both positive and negative, was obtained in various major core operational management areas including leadership, intra-team cohesion management, data management, corporate reputation, and business process efficiency. These findings validate the results of several predictive research studies in the operations management, management information systems, and artificial intelligence disciplines. Managers and executives are directly influenced by at least five specific operational management categories and emotional responses may cloud their decision-making.

Relatively few studies have been undertaken that link operational performance areas and the practical impacts of AI technologies. The specific research questions relating to the general question above and the anticipated outcomes achieved allowed for the categorization and analysis of the results and are now presented in turn. We begin by exploring the impact of artificial intelligence on leadership. This paper will attempt to identify firsthand in a phenomenological approach the real impacts, beyond speculation, of AI business operations and the responses of executives and employees ‘on-the-ground’. It will provide a synthesis of the major results from each of the categories of a major research investigation. The paper thereafter serves as an introduction for several expanded papers that explore the specific themes in detail and provides a firm grounding in leadership studies, especially emotional intelligence and situational leadership theory.

2. Literature Review

Many organizations are leveraging artificial intelligence (AI) to automate or augment their business operations. The interest in implementing AI in operations functions is also reflected in the rapid growth of VC investments in AI supply chain technology start-ups. Artificial intelligence (AI) consists of algorithms, data, and related technologies which provide systems the ability to perform tasks that normally require human intelligence: making decisions, understanding natural language, analyzing big data, and recognizing patterns. The operational processes in which AI may be implemented include user-focused operations (e.g., demand forecasting, new product development, demand sensing); supplier-focused operations (e.g., supplier selection and negotiation, spend analysis, supplier relationship management); internal operations of firms (e.g., inventory management, production planning, quality management, layout and flow design); and logistics operations (e.g., transportation, warehousing, information technology).

We review three areas to help guide our research: AI and its applications in the operations function; the impact of AI on organizations; and business adoption of AI. Beginning with AI in the operations function, the role of AI has been a central focus of operations research. AI has been examined with respect to its impact on the design of new operational processes such as new product development, supply chain processes, and forecasting procurement needs. These emerging roles of AI in the operations function suggest a need for research into the core phenomenon. We find limited research that evaluates the impact of AI on organizations. Conceptually, AI is viewed as a strategic resource that may be enhanced through subsequent innovation and organizational learning. We suggest that current theories do not adequately represent the unique emergent properties of AI, such as growing confidence in AI systems over time.

3. Research Methodology

Research Design

To address the research question, ‘How does AI affect the type and pattern of individual tasks across different business operations?’, semi-structured interviews were conducted with operational practitioners from a variety of industries. The authors chose a phenomenological research approach as it “is concerned with describing the lived experience and understanding of a phenomenon from the perspective of those experiencing it.” This is appropriate for understanding an individual’s perspective on the topic of inquiry, which is the impact of AI on work. A purposeful sampling strategy was adopted.

This involved two initial phases. Firstly, a convenience sample was used to identify a group of people who may be able to assist with participant recruitment. Interviews were conducted using video-conferencing facility Microsoft Teams. Microsoft Forms was used as a scheduling tool. Participants were invited to attend an interview at a time that was convenient for them, slots were available during the working day and early mornings/evenings to try and maximize participation. Interview consent forms and participant information were sent to participants ahead of their interview time. The sample was limited to participants working in sectors where the introduction and use of AI is of increasing importance, and has the potential to have a significant impact on their activities. The resulting interview data were then thematically analyzed, and the findings are discussed in the next section. Further methodological details are available in.

4. Data Analysis and Findings

This was a qualitative phenomenological study designed to explore the impact of artificial intelligence on business operations. I purposively sampled and interviewed 10 business professionals in president/CEO, HR director, assistant VP, VP, ERP director, director, chief digital officer, and chief operating officer roles within various manufacturing, financial services, and services companies located in Arkansas and Georgia. Each participant was asked a series of open-ended questions regarding organizational challenges, industry trends, and solution strategies pertaining to a growing reliance on AI technologies. Following each participant interview, the audio was transcribed verbatim. I read through each transcript to ensure accuracy and then instructed the NVivo software to generate an AI-driven coding scheme. Descriptive codes emerged, and matrices, typing schemes, and word clouds demonstrated the context, connection, and frequency of these codes. Finally, I incorporated axial coding to draw connections across themes, concluding with member checks for validation.

Descriptive coding results suggest themes of innovation emphasis, technology adoption, industry disruption, the labor challenge, strategic processing, and regulatory compliance. Meaning from these codes is then deduced and verified through axial coding. Eye care business participants interviewed recognize a general innovative emphasis in the growing reliance on AI. To some members, e.g., K2Co, Rhinestahl, Cincinnati Incorporated, the push towards innovative AI is becoming a raced stratification with some operators (such as surgeons, venture capitalists, and business professionals) leading the charge while reducing labor and increasing repeatability. Patino and Pickerings believe there is also evidence, however, that AI reliance will be inclusive through accentuating human knowledge, enabling several worker segments to be more productive with links to better diagnostic ability.

5. Conclusion and Implications

5.1 Conclusion

Drawing from the findings of our study, we assert that a unanimous opinion favoring AI and its transformative potential in rendering positive impacts on business is lacking. Given the decreasing investments and issues that regimes, businesses, and societies identify within digitized landscapes, AI does not come without adverse impacts. This study presents that AI trade theory does not suffice without explication of the border-crossing forces surrounding AI—the trade-offs that occur within times of change as appropriate investments in infrastructures, partners, selves, job skill sets, and vision are necessitated in order to digitally transform today’s businesses. With these observations in mind, AI and businesses’ attitudes must align to better comprehend the impacts its deployment has on their operations. The learning system approach provides a detailed insight for stakeholders to consider how the AI system intricacies uniquely intertwine with the operational foundations of today’s diversely complex operations.

5.2 Implications

One of the most significant implications of this study is the operational impact AI has on front and back end businesses, markets, and societies as technological relationships expand and exasperate implications presented from front to back end. The interactions of RQ1-3 with society, managers, and workforce illustrate that the operation of AI may not be local; the drivers of problems can induce border-crossing power within industries and institutions affecting firm linkage resilience. Yet, AI may also challenge entities to continue incremental change in differing directions. The paralysis AI necessitates of human resource infrastructures has purported the need for workforce skillset investment (e.g., more data scientists) and interfacing capabilities. This presents a significant workforce utilitarian point that has been echoed in the literature as a contributory reason for resistance, anxiety, and dismissal of employing AI systems. The fragmentation of RQs 4-5 demonstrate relevant boundaries exist, and this is synonymous with established AI issues. However, upon qualitative consensus, businesses do invest in competing itself, potentially causing isolationist damage and rethink MSS interfaces. Our study substantiates case study nuance from ECA firms in this connection. The implications of our study assist all stakeholders. Screening multilateral and social dynamic forces causing AI personal choice trade-offs is foundational, as our work and illustrations of companies who use AI and MSS interfaces to compete with themselves driving away or minimizing different external stakeholders illuminates.