DB FPX 8720 Assessment 3 Business Project Idea- Developing a Business Study

Student Name

Capella University

DB-FPX8720

Professor Name

Submission date

1.1 Introduction

With its application in the analysis of extensive amounts of information, artificial intelligence (AI) is not only changing how industries are being run but also altering the employment relationship in the current digital era, as well as enhancing productivity. The next important advantage of AI integration is the level of accuracy and speed of analysis of large data, which provides companies with more understanding of how to make better decisions and to creatively investigate solutions. To provide an example, AI can predict and streamline supply chains, as well as deliver its services to individual users, all of which can give them an advantage due to the capacity to handle large volumes of data. According to the research of Jada and Mayayise, the percentage of productivity that can be achieved through the introduction of AI into data analytics is as high as 40%, and it is indeed a revolutionary opportunity. As effective big data analysis becomes more crucial, AI technologies are essential for businesses to maximize their benefits, thereby changing job descriptions and boosting productivity.

1.2 Problem of Practice

The general challenge is that organisations with a big data analysis theme are struggling to fully realise the value of big data because of the complexity and quantity of the data, and are not making optimal decisions and therefore missing opportunities. It is an issue that can be seen in most industries and segments affecting business, the health care industry, education, and so on. The adversity encountered is in the form of inefficiencies, increasing costs, and incapability to utilise opportunities to be inventive and develop (Kulkov et al., 2023). While artificial intelligence (AI) can definitely benefit big data analysis, it’s difficult for many organizations to effectively make use of AI technology.

The issue is that while U.S. businesses have access to all this big data, they don’t know how to use the right skills and strategies to adopt AI into their processes, leading to the inefficient use of data and loss of productivity. It is experienced particularly in the areas where the process of decision-making depends heavily on information, e.g., in finance and health.

1.3 Gap in Practice

Today, numerous organisations find themselves battling the challenges of massive amounts of data and are unable to make the best use of the skills and intelligence that AI technologies offer. This results into an under-utilization of the data, increased cost and missed opportunities to innovate and expand in most instances, these businesses do not have proper methods and strategies as far as efficient utilization of data is concerned.

However, in the current situation, where even organizations are struggling with the impossibility of maximally utilizing the strengths of big data without AI integration, the issue is the absence of appropriate AI integration. Opting to make data-informed decisions can prove to be hard for business- or health-service providers, leading to poor performance metrics and less competitiveness. The destination is a day when such entities will have exhausted AI in their data analytics processes, wherein they would fully analyze the data, producing actionable information, enhancing decision-making, and organizational growth and success. The gap that exists is the practice gap between the challenges of the integration of AI and the possibility of maximised use of data.

1.4 Purpose of the Project and Project Questions

Purpose

The team should consist of an aim and a list of questions. The team should have a purpose and project questions.

Qualitative inquiry project aims: Explore case study approach and skills that will help business leaders in the United States to effectively incorporate artificial intelligence (AI) into their big data analytics processes. By identifying and comprehending these strategies and skills, the project will bridge this gap in practice by making sure that data is more effectively applied and enhancing productivity within industries with high data reliance (including Finance and Healthcare). This study will employ a case study approach to gain more in-depth knowledge from business leaders who have been successful in implementing AI in their data analysis.

Project Questions

1. What are business leaders’ specific skills that they feel they need to be successful with the integration of AI in their big data processing?
2. What are some of the strategies business leaders have had the capability of using that have been successful in addressing the challenges of implementing AI into the analysis of big data?

1.5 Preliminary Terms and Definitions

Artificial Intelligence (AI): Artificial intelligence (AI) is intelligence exhibited by machines and, just like humans, machines can learn and make informed decisions. AI in big data analytics allows automating the processes, improving data analysis, and creating insights that can be used to make decisions and work more productively.

Big Data Analytics: The ability to analyze these large and varied sets of big data to extract information like hidden patterns, correlations, market trends, and customer insights is called Big Data Analytics. This information can prove useful to companies to aid them in making business decisions that are informed, and boost productivity and efficiency.

Data-Driven Insights: The insights that can be obtained through studying data are data-driven insights, which are valuable insights that can help in making informed business decisions. Patterns and trends in the data sets can be used to provide some insights that can assist in the decision-making process. The improvements can be enhanced with the help of AI that is able to consider data and analyse it better since it can process vast amounts of data in a more efficient way (Oncioiu et al., 2019).

1.6 Project Justification

The proposed initiative plans to use A.I. and big data analysis in order to enhance leadership training in organizations. Through this project, the rationale is that there is a need articulated in both practitioner and scientific literature that discusses the necessity of using A.I. tools to process and analyze large quantities of data in the current business environment. The amount of data being generated by organizations has never been higher. Consequently, traditional techniques of data analysis no longer work. It is A.I. that can help leaders to make decisions and develop leadership programs effectively by means of processing big data. The fact that business leaders are emphasized in this context is important because it is they who make decisions that influence the implementation of the changes or innovations; thus, their decisions about A.I. usage will lead either to success or failure.

The project questions are key in the process as they discuss how effective and efficient AI is in changing ‘big data’ into actionable information and how that can support their leadership development. The questions will unveil the possibilities of utilizing AI analytics to uncover and develop vital leadership traits that will enable leaders to be more responsive and responsive to market changes. The value of the project is not the increase in leadership development but presenting the abilities of AI in the process of strategic decision-making, which are on the rise in the modern business environment imbued with data.

This project’s outputs will especially have value for business practitioners like Human Resource (HR) professionals, leadership coaches, and corporate strategists. The project will equip these stakeholders with the knowledge of how AI can be integrated into their processes to make their leadership development programs effective enough to produce better quality and dynamic leaders within the organization and eventually improve its performance.

Reflection on Alignment

The general structure and evolution of the theme guarantees that the theme has a sound approach. The general question is how to incorporate AI into Big Data analytics to enhance the leadership development process. This focus is further constrained by tackling one specific issue: the lack of efficiency of existing leadership development programs in leveraging AI insights. The difference between practice and theory emphasizes the need for empirical research to establish the effectiveness of these practical applications of AI. The goal of the project is to explore and provide evidence on AI optimisation of leadership attributes and decision-making procedures. Each of the project questions probes different areas of the integration of AI and its implications for leadership development and connects to the other questions and to a common research objective.

Leveraging AI for Big Data Analysis: Transforming Jobs and Boosting Productivity

General Problem

The common issue is that people who are dealing with big data analysis are facing challenges in effectively handling big data, which results in sub-optimal decision-making and missed opportunities.

Specific Problem

The issue here is that business leader in the United States don’t have the right skills and strategies to embed AI in their big data analytics processes, thereby failing to realize the potential of this data and reducing productivity.

Gap in Practice

Practice gap: AI has not permeated big data analytics, and therefore, there is a lack of utilization of data, leading to the corresponding effect on productivity in finance, healthcare, and other areas.

Purpose

The qualitative enquiry project will explore the approaches (based on a case study approach and competencies) that United States business leaders need to adopt in their efforts to incorporate artificial intelligence (AI) into their big data analysis procedures.

Questions

1. Which skills do leaders in business feel are essential to successfully making AI a part of their big data analytics?
2. Business leaders have adopted what strategies to be the most effective in tackling the integration of AI challenges in big data analytics?

References

Banaeian, S., & Imani, A. (2024). Internet of Artificial Intelligence (IoAI): The emergence of an autonomous, generative, and fully human-disconnected community. Discover Applied Sciences, 6(3), 60–62. https://doi.org/10.1007/s42452-024-05726-3

Chioma, A., Omamode, H., Obinn. (2024). Big data analytics: a review of its transformative role in modern business intelligence. Computer Science & IT Research Journal, 5(1), 219–236. https://doi.org/10.51594/csitrj.v5i1.718

Fernandez, A. (2019). Artificial intelligence in financial services. SSRN Electronic Journal, 12(4), 8–12. https://doi.org/10.2139/ssrn.3366846

Grebe, M., & Heinzl, A. (2023). Artificial intelligence: How leading companies define use cases, scale up utilization, and realize value. Informatik Spektrum, 9(3), 5–7. https://doi.org/10.1007/s00287-023-01548-6

Humphreys, D., Koay, A., Desmond, D., & Mealy, E. (2024). AI hype as a cybersecurity risk: The moral responsibility of implementing generative AI in business. AI and Ethics, 9(3), 236. https://doi.org/10.1007/s43681-024-00443-4

Jada, I., & Mayayise, T. O. (2023). The impact of artificial intelligence on organisational cyber security: An outcome of a systematic literature review. Data and Information Management, 8(2), 100063–100063. https://doi.org/10.1016/j.dim.2023.100063

Kulkov, I., Bertello, A., Makkonen, H., Kulkova, J., Rohrbeck, R., & Ferraris, A. (2023). Technology entrepreneurship in healthcare: Challenges and opportunities for value creation. Journal of Innovation & Knowledge, 8(2), 8–12. https://doi.org/10.1016/j.jik.2023.100365

Oncioiu, I., Bunget, O., Türkeș, M., & Căpușneanu, S. (2019). The impact of big data analytics on company performance in supply chain management. Sustainability, 11(18), 4864. https://doi.org/10.3390/su11184864

Saha, G. C., Menon, R., Paulin, M. S., Yerasuri, S., Saha, H., & Dongol, P. (2023). The impact of artificial intelligence on business strategy and decision-making processes. European Economic Letters (EEL), 13(3), 926–934. https://doi.org/10.52783/eel.v13i3.386

Sullivan, S., Nevejans, N., Holzinger, A., & Friebe, M. (2023). The underuse of AI in the health sector: Opportunity costs, success stories, risks and recommendations. Health and Technology, 9(3), 5–7. https://doi.org/10.1007/s12553-023-00806-7