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Capella University, DB FPX Assessments, DB-FPX8720

DB FPX 8720 Assessment 5 Harvard Business Review Summit- Virtual Presentation

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DB FPX 8720 Assessment 5 Harvard Business Review Summit- Virtual Presentation Student Name Capella University DB FPX 8720 Professor Name Submission Date Slide 1 Title Slide Hello, everyone. Greetings, my name is, and I am about to present. I am happy to see you here today. I will present part of my work on my new project called “Fundamentals of Strategic Digital Transformation: Changing Jobs with the use of AI and Big Data,” which implies that I explore the concept of utilizing AI as a big data analysis tool to enable the transformation of jobs and professions in any type of organization and business. Slide 2 Introduction Machine learning and natural language processing are two technologies that can support the automated performance of complicated processes, analysis of large amounts of data, and making reliable decisions in a limited time period. Big data analysis helps spot trends and advance looking in the future, thus making it possible for businesses to use AI to make more forecasts and decisions, thereby improving their business processes. When coupled with big data, businesses can become more productive than ever before: the AI manages to take care of all the routine responsibilities, and the work processes become more efficient. Utilising resources better, improving precision and making tasks faster (e.g. in marketing, human resources and customer service) saves time and costs, enabling more efficient processes that lead to improved business outcomes and a competitive edge in the market. Slide 3 Strategic Digital Transformation Fundamentals To drive digital change within an organisation, there are a number of key principles that need to be adhered to: vision, leadership, culture, and technology adoption. The vision gives the digital transformation process a clear purpose and direction to make decisions and unify stakeholders around a shared vision. There must be effective and clear leadership where the leader can motivate, communicate, as well as empower employees by giving them the power to embrace and make the change a reality. The leaders who approve digital programmes and provide the environment, which is flexible, innovative, and supportive environment will assist in ensuring that innovation and digital development thrive. In order to have a sustainable change in the digital world, it is important to develop a favorable culture. This involves the provision of an environment that embraces creativity, learning, and teamwork, and that permits experimentation and the learning process without necessarily being afraid of failure. There needs to be continuous learning and a focus on rewarding learning practices that can lead towards digital innovation. Last but not least, it is crucial to strategically implement technology. It includes emphasis on initiatives offering value, prototypes, and pilots as a way of testing out new ideas and methodological approaches to integrating technology to enhance processes and output. Slide 4 Best Practices for Developing a Digital Transformation Strategy Best practices exist that can be adhered to in order to come up with an effective digital transformation strategy. First of all, it’s important to evaluate what this organization currently has in place and how developed the technology is, to determine where it can improve and to make sure that the organization is prepared for the change. The initial step is conducting a thorough analysis of the current processes, systems, and market conditions. Defining goals is the next step that is crucial. Goals should be specific, measurable, achievable, relevant, and timely (SMART) to provide a sense of direction and allow tracking of progress. Slide 5 The other important component is the stakeholders; it is necessary to engage them and ensure that all those who will be affected by the transformation are involved and ready to help in bringing about the transformation. Effective stakeholder engagement means knowing all the stakeholders, their needs, and what they are interested in and engaging them in the decision. Lastly, this strategy can be implemented iteratively in order to be in a position to give continuous feedback and improvements. That way, risks can be alleviated, and change will be on schedule with business purposes and the demands of the market. Slide 6 Leveraging AI for Big Data Analysis AI has significant potential to enhance big data analysis as it automates the process of data analysis, identifies trends, and gives actionable insights that would otherwise take a much longer duration to be developed. With the large volumes of data, AI can process this data to discover patterns, outlier data, and other correlations that are challenging to discover with conventional analysis approaches. This aspect enables companies to make more effective, timely, and accurate decisions. AI has been operating over the decades to promote activities within the supply chain, whether it is anticipating changes in demand or inventory, resulting in reduced expenses and improved customer satisfaction in businesses like IBM. The financial industry has seen AI-driven analytics being utilized to detect fraudulent transactions, resulting in a reduction in fraud and billions of dollars in yearly savings. In medicine, as an example, AI has been utilized to analyze patient data and predict disease outbreaks and launch timely actions to prevent them and utilize available resources more efficiently. The examples demonstrate applications of AI in better data analysis and how it can be better translated into better business outcomes through making better and more proactive decisions. Slide 7 Foundations of Organizational Learning Ongoing learning, knowledge sharing, and adaptation are vital initial components of organizational learning. Knowledge Sharing refers to the scientific or processual method of disseminating essential knowledge and experiences to the workforce, in a way that will see quality knowledge reach everyone in the workforce. The digital transformation is about making organizations learn by establishing tools and platforms that can support good continuous learning, sharing knowledge, and adaptation. Individualized and scalable training courses, which can be done using Artificial Intelligence, machine learning, and cloud-based technology, can encourage continuous training within the company. The exchange of knowledge is also enabled by these digital tools since they are used to create central databases and communication channels through which knowledge can easily be

Capella University, DB FPX Assessments, DB-FPX8720

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

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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 What are business leaders’ specific skills that they feel they need to be successful with the integration of AI in their big data processing? 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

Capella University, DB FPX Assessments, DB-FPX8720

DB FPX 8720 Assessment 4 Creating an Example of Student Work

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DB FPX 8720 Assessment 4 Creating an Example of Student Work Assessment 4: Creating an Example of Student Work The Role of Technology in Modern Supply Chain Management: Enhancing Efficiency and Resilience Student Name School of Business and Technology, Capella University DB-FPX8420: Teach Business in Higher Ed Professor Name Submission Date Introduction The world has become global, and supply chain management in today’s world is undergoing a transformation owing to technological advances. IoT, referenced as AI, Blockchain, and the IoT, are turning the game around. These are the technologies that are transforming how businesses streamline their operations using tools that enhance operational efficiency, simplify operations, and reduce risk all the way along the supply chain. AI can help better predict customer needs, and the business can adapt to them, optimising its stocks, etc. The blockchain is traceable and is secure, which ensures that traceability of products is transparent as they move along a route to their destination. In the meantime, the IoT devices allow real-time monitoring and tracking of products, which results in improved supply chain visibility and decisions. The essay will discuss the effects of new technologies on supply chain management and how they can be used to enable companies to maintain their operations and cope with disruptions. It will also propose a strategic plan that will be a practical blueprint that organizations can exploit these technology trends. This type of agile, co-creative, and continuous innovation process is the secret to remaining competitive and tough. Technological Advancements and Their Impact on Supply Chain Management With the cost of AI and machine learning eating into operations in the supply chain, more precise and nimble decision-making is observed in business. Decision-making in the supply chain processes has been strengthened by the incorporation of AI and machine learning into the decision-making processes, and it has enabled companies to make decisions that are more accurate and agile than ever before. Their proactive publications make AI-powered forecasting tools use diverse data that includes market trends, past sales, and customer real-time behaviors. Businesses can use this analysis to plan demand changes and make decisions based on them, including avoiding a shortage of stock and reducing overstocks, and simplifying resource utilization. These predictions are enhanced with machine learning algorithms, which learn the patterns and trends of the data as it comes in, giving even more precise predictions. AI does have the potential to optimise delivery routes through assessing traffic, pricing of fuel and the delivery restrictions in order to reduce delivery time and transportation costs and increase operational efficiency (Magd & Ruzive, 2021). In the same vein, blockchain has revolutionized the aspect of transparency and traceability in supply chains by providing a record in the supply chain to track products. This system is decentralized, which enables the tracing of the products and authentication that reduces risks of product fraud, counterfeit products, and compliance breaches by suppliers to end consumers. Through the transparent and tamper-evident process of transactions, a blockchain enables the instilling of trust among the stakeholders and enhances accountability. In addition, it helps firms to be aware of and rectify such problems as product recalls or safety risks. It may provide more transparency and ensure compliance with more and more rigid rules worldwide and promote confidence and integrity of the supply chain (Fernando, 2024). Framework Recommendation for Adapting to Technological Trends An important aspect of Agile Supply Chain Management is embracing technology. The capacity to easily work around unpredictable changes in the market, such as changes in consumer demand, supply, or even crises, creatively enables businesses to exploit this approach. Real-time data analytics and linking up with suppliers will allow businesses to make decisions more quickly and change their approaches to continue operations (Christopher & Towill, 2001). It is also possible to integrate AI, blockchain, and IoT technologies into the Agile Supply Chain Framework and make organizations work more appropriately and transparently, and with a higher level of traceability. Such innovations may be taken up and incorporated, and businesses get the upper hand in the market, making them more efficient in their supply chain. Agility within supply chain management can save on lead times, eliminate wastefulness, and enable companies to better respond to customer demands in terms of speed and precision (Christopher and Towill, 2001). Having a common structure within the supply chain, the Agile Supply Chain Framework also fosters cooperation with their suppliers and other stakeholders, providing efficient communications and shared purposes, which improve the overall performance of the supply chain. By implication of this real-time data, all partners know the current development, can make choices, and most importantly act in a co-ordinated fashion and be ahead of the curve regarding challenges. The strategy will enable supply chains to be more adaptive and resilient to change, and uncertainty is the only thing left after change in these changing times (Christopher & Towill, 2001). Real-World Application A well-known example of the Agile Supply Chain Framework, which has been put into practice, is the use of AI systems for stock monitoring and inventory management in real time for the retail giant Walmart. Using machine learning and predictive analytics, Walmart has the potential to better align itself with demand and reduce the chances of stock-outs and overstocking stores. The benefit of this real-time information is that Walmart is able to optimise its inventory replenishment processes in order to ensure that its products are available to meet customer demand and minimize the risk of excess stock, minimizing holding costs and waste. The agility in operations that is expedited by the flexibility in handling the inventory contributes to the customer satisfaction and efficiency in the operations (Magd & Ruzive, 2021). AI is not the only disruptive technology in the food industry: blockchain technology is applied in blockchain transparency in supply chains and traceability. Besides AI, blockchain technology has also been transforming food supply chain traceability and transparency as well. The IBM Food Trust blockchain platform is capable of tracing food to farms and providing complete visibility of