1.1 Introduction

The use of artificial intelligence (AI) in the analysis of large volumes of data is transforming the way that industries are operating and changing the employment dynamic in today’s digital world, while also improving productivity. The ability to analyze huge datasets at a rapid pace and with high accuracy, offering businesses more insight for better decision-making and creatively exploring solutions, is another key benefit of AI integration. For example, AI’s ability to analyze large volumes of data can enable companies to make predictions, streamline supply chains, and tailor their services to individual users, all of which can give them an edge. As revealed in Jada and Mayayise’s study, the level of productivity that can be gained from implementing AI in data analytics is up to 40%, which is truly a transformative 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 a problem that can manifest itself in many industries and sectors, impacting businesses, the health care sector, educational institutions, and more. Adversity faced is manifested as inefficiencies, increased expenses, and failure to take advantage of opportunities for innovation and development (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 felt especially in the sectors where making decisions is highly reliant on data, e.g., 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 leads to sub-optimal use of the data, higher costs and lost opportunities to innovate and grow – in many cases, these businesses lack appropriate methods and strategies with regard to efficient data usage.

In today’s scenario, where organizations are facing challenges with the inability to fully leverage the capabilities of big data without AI integration, the problem lies in the lack of proper AI integration. Businesses or healthcare service providers find it difficult to effectively make data-informed decisions and thus experience poor performance metrics and less competitiveness. The desired state is a future where these entities have fully embraced AI in their data analytics workflows, seamlessly analyzing data to generate actionable insights, improve decision-making, and fuel organizational growth and success. The gap in practice is between the actual struggles with the integration of AI and the potential for optimized use of data.

1.4 Purpose of the Project and Project Questions

Purpose

The team needs to include a purpose and a set 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. Through the identification and understanding of these strategies and skills, the project will help close this gap in practice by ensuring that data is used more effectively and improving productivity in industries that are heavily reliant on data, such as 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 strategies have business leaders been able to employ that have proven effective in tackling the challenges of incorporating AI into big data analysis?

1.5 Preliminary Terms and Definitions

Artificial Intelligence (AI): AI, or artificial intelligence, is intelligence exhibited by machines, which, like humans, are capable of learning and making informed decisions. In big data analytics, AI helps automate processes, augment data analysis, and create insights that can assist in better decision-making and operational efficiency.

Big Data Analytics: Big Data Analytics is the ability to analyse these large and varied datasets or “big data” to discover information, including hidden patterns, correlations, market trends, and customer preferences. 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: Data-driven insights are worthwhile insights that can be gained from studying data, and they assist in making well-informed business decisions. Insights from patterns and trends within the data sets can help a lot in the decision-making process. These insights can be improved by AI’s capacity to take data into account and analyse it more effectively, as it’s able to process huge amounts of data more efficiently (Oncioiu et al., 2019).

1.6 Project Justification

The intended project is to combine artificial intelligence with big data analysis to improve leadership development in organisations. This project has identified a need highlighted in practitioner and scholarly literature that explores the need to harness AI tools to manage and analyze vast amounts of data in today’s business landscape. The amount of information organizations are collecting has never been greater, and it’s no longer possible to rely on conventional data analysis techniques to cope. AI can greatly enhance decision-making and leadership development initiatives by analyzing and comprehending vast amounts of information. The emphasis on business leaders is important because they are key decision makers when it comes to implementing change or innovation in the organization, and their choices in using AI will dictate 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 are designed to reveal the potential of using AI analytics to discover and nurture essential leadership qualities that will help leaders be more agile and attuned to market shifts. Reaping the value of the project is not only the improvement in leadership development but also showcasing the capabilities of AI in strategic decision-making processes, which are gaining momentum in the modern business landscape steeped in 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 offer these stakeholders insights into how AI can be integrated into their processes to boost the effectiveness of their leadership development programs, helping to create more capable and dynamic leaders within the organization and ultimately strengthening its performance.

Reflection on Alignment

The overall organization and development of the theme ensure that there is a coherent approach to the theme. The overall problem is how to integrate AI into Big Data Analytics to improve the process of leadership development. 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 aim of the project is to investigate and present evidence on AI optimisation of leadership qualities and decision-making processes. 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.

Figure 1

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 the business leader in the United States doesn’t have the right skills and strategies to embed AI in their big data analytics processes, thereby failing to realize the potential of these data and reduce productivity.

Gap in Practice

Practice gap: AI is yet to pervade big data analytics, resulting in an underutilization of data and a resulting impact on productivity in finance, healthcare, and other sectors.

Purpose

The qualitative inquiry project aims to delve into the required strategies (using a case study methodology and skills) for United States business leaders in integrating artificial intelligence (AI) into their big data analysis processes.

Questions

Which skills do leaders in business feel are essential to successfully making AI a part of their big data analytics?

Business leaders have adopted what strategies to be the most effective in tackling the integration of AI challenges in big data analytics?