Home Online Business Blog The Role of Artificial Intelligence in Business Education: Automation, Machine Learning and MSBA Curriculum

The Role of Artificial Intelligence in Business Education: Automation, Machine Learning and MSBA Curriculum

12 Dec
Businessman using command prompt in a large language model-based chatbot

According to the global consulting company McKinsey, up to 70% of business operations across all industries could be automated by generative artificial intelligence (AI) by 2030.1 Without a deep understanding of the strengths and weaknesses of AI, as well as how it can be used to achieve an organization’s strategic objectives, business leaders won’t be equipped to succeed in an AI-driven economy.

This post explores the role of AI in modern businesses and how it’s being incorporated into business curriculum development.

Automating Business Operations With AI

Business automation uses technology to perform tasks that would otherwise require human intervention. The primary goal of automation is to streamline operations, reduce costs, and improve efficiency. Unlike conventional systems, automated solutions can work around the clock, offer higher consistency in output and free up human resources for more complex, creative tasks.2

Workflow Automation

While automation has been used in business operations since 1771, when Richard Arkwright invented a water-powered automatic spinning wheel, AI has achieved remarkable advancements in automating modern workflows.3 For example, Robotic Process Automation (RPA) mimics human actions in executing tasks across multiple software systems.

Unlike standard automation, which often involves single-task automation within one application, RPA interacts with multiple systems, just as a human operator would. By implementing algorithms and machine learning, RPA can handle data entry, manipulate data, trigger responses and even communicate with other digital systems. This capability makes it highly versatile, allowing businesses to automate complex processes that involve multiple steps and decision points.4

Automated Customer Service

Automated customer service solutions like chatbots can handle a variety of customer queries without human intervention. Using natural language processing, they can answer frequently asked questions, process orders, and even handle basic troubleshooting. This frees up customer service agents to handle more complex issues that require a human touch.5

Supply Chain Management Automation

The Fourth Industrial Revolution—the digitization of the manufacturing sector—is playing a significant role in creating more adaptive and resilient supply chains.6 Automation in supply chain management often involves using systems to track goods as they move from manufacturing to distribution points. Sensors and tracking software can automatically update the inventory, streamlining the supply chain and reducing the chances of overstocking or understocking.7

Data-Driven Decision Making With Machine Learning

Machine learning technology, a subset of artificial intelligence, uses algorithms to learn from and make data-based decisions. Unlike traditional rule-based systems, machine learning models can adapt and improve over time, offering more accurate and efficient solutions to various business problems.8

Machine Learning Models in Business Applications

In customer relationship management (CRM), machine learning algorithms can predict customer behavior based on historical data, helping businesses tailor marketing campaigns to individual preferences. In inventory management, predictive algorithms analyze patterns in sales, seasonality and other factors to optimize stock levels. In human resources, machine learning can sift through resumes to find the best candidates, and in finance, it can flag potentially fraudulent activities using fraud detection by analyzing transaction patterns.9

Data Analysis

Data-driven decision-making involves collecting and analyzing data to guide strategic business choices. Businesses collect vast amounts of data from various sources like customer interactions, operations and external market conditions. Machine learning tools can process this data to generate actionable insights.

A machine learning model amplifies the power of data science and data-driven decision-making by automating the analysis of large and complex data sets that are often beyond human capacity to interpret.

A well-designed machine learning model or supervised machine learning model can unearth relationships between variables that may not be readily apparent, enabling more nuanced and effective strategies such as algorithmic trading. The real-time analytics that machine learning can provide also helps businesses react more swiftly to market changes.10

Incorporating AI Into the MSBA Curriculum

AI’s role in analytics, decision-making and automation has become so integral that a deep understanding of these technologies is necessary for the next generation of business leaders.11 Here are some specific ways AI is being integrated into MSBA curricula:

Core Courses in AI and Machine Learning

Many MSBA programs are introducing core courses focused on AI and the fundamentals of machine learning applications. These courses often cover both supervised learning and unsupervised learning, neural networks, natural language processing and reinforcement learning. This gives students a solid grounding in AI methodologies they can apply to various business problems.

Predictive Models

Predictive models can forecast anything from consumer behavior and sales trends to inventory levels and risk assessment. Specific algorithms like linear regression, decision trees and artificial neural networks often form the backbone of these predictive systems.12

The MSBA curriculum may include tools to build these decision-making models, such as:13

  • Python libraries: Libraries like scikit-learn, TensorFlow and PyTorch offer pre-built functions for creating predictive models
  • R: Particularly popular in statistical modeling and data visualization, R offers packages like ‘caret’ for training machine learning models and ‘ggplo’ for data visualization
  • SQL: Knowing how to use SQL for data manipulation and retrieval is foundational in data analytics
  • Tableau: This business intelligence tool is commonly used for data visualization and offers some built-in functionalities for simple predictive analytics
  • SAS: Particularly prevalent in certain industries like healthcare, SAS offers extensive capabilities for data analytics, including predictive modeling

Ethics and Responsible Use

In 1921, Nobel Prize winner Christian Lange warned, “Technology is a useful servant but a dangerous master.”14 As AI algorithms make increasingly important decisions in business, his words are just as relevant today as they were 100 years ago. MSBA students must have a comprehensive understanding of ethical considerations such as data privacy, fairness, and transparency. Modern business leaders will need to be able to critically supervise AI to guard against harmful use.15

Prepare To Become a Forward-thinking Business Leader

Develop the skills you need to meet today’s business challenges and opportunities with William & Mary’s Online Master of Science in Business Analytics (MSBA). Business leaders who can harness the power of AI and analytics will be able to drive responsible corporate strategy and revenue growth.

In as few as 15 months, our expert faculty will prepare you for success in the rewarding and rapidly growing field of business analytics. This forward-thinking curriculum includes BUAD 5122: Machine Learning and Predictive Analytics, a course designed to provide you with a deep understanding of the theory and practice of supervised learning and unsupervised learning, including regression, classification and clustering. In BUAD 5802: Artificial Intelligence Applications for Business, you will focus on AI applications in business and implementations of contemporary AI techniques, such as deep learning, natural language processing and planning for solving business problems.

Contact an admissions outreach advisor today to learn more.

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  10. Retrieved on October 26, 2023, from mason.wm.edu/blog/how-data-science-drives-business-decision-making
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  15. Retrieved on October 26, 2023, from link.springer.com/article/10.1007/s43681-023-00306-4