Bridging Business and Intelligence: Integrating Artificial Intelligence into Undergraduate Business Education

Abstract:

The rise of artificial intelligence (AI) is transforming the global economy, reshaping how organizations make decisions, optimize operations, and interact with consumers. Despite this shift, traditional business degree programs often lag in equipping graduates with relevant AI knowledge and competencies. This thesis explores the strategic integration of AI into a business degree curriculum, proposing new course frameworks, interdisciplinary learning models, and industry-aligned outcomes to better prepare students for AI-powered business environments.

Table of Contents (Thesis Structure):

  1. Introduction

    • Background: AI in business today

    • Problem Statement: The skills gap in business graduates

    • Research Objectives

  2. Literature Review

    • AI in higher education

    • Business applications of AI

    • Interdisciplinary education models

  3. Methodology

    • Qualitative analysis: Case studies from top business schools

    • Quantitative survey: Employer expectations vs. graduate skillsets

    • Curriculum design framework development

  4. Findings

    • Competencies needed in AI-literate business graduates

    • Gaps in current business programs

  5. Proposed Curriculum Model

    • Core courses, electives, and capstone integration

    • Pedagogical strategies (experiential learning, case-based learning)

  6. Course Ideas and Outlines (see below)

  7. Implementation Strategy

    • Faculty development

    • Industry partnerships

    • Assessment models

  8. Conclusion

    • Summary

    • Recommendations

    • Future research directions

Sample Course Ideas & Outlines:

1. AI Fundamentals for Business Decision-Making

Level: Year 2
Objective: Teach core AI concepts tailored for non-technical business students.
Outline:

  • Introduction to Machine Learning, NLP, and computer vision

  • AI use cases: Marketing, finance, supply chain, HR

  • Business decision-making with AI dashboards

  • Ethical and legal implications

  • Tools: ChatGPT, Tableau, PowerBI, Google AutoML

2. Data-Driven Strategy and AI in Business Models

Level: Year 3
Objective: Align AI capabilities with competitive strategy and innovation.
Outline:

  • Business model innovation through AI

  • AI in customer segmentation and personalization

  • Predictive analytics for strategic planning

  • Industry case studies (Amazon, Netflix, Tesla)

3. Ethics and Governance in AI-Driven Enterprises

Level: Year 3
Objective: Explore governance frameworks and responsible AI deployment.
Outline:

  • AI transparency, fairness, and bias

  • Legal frameworks (GDPR, CCPA)

  • Stakeholder impact and governance models

  • Building responsible AI policy in firms

4. AI-Enhanced Marketing and Consumer Insights

Level: Year 3
Objective: Apply AI to enhance customer journey mapping and campaign performance.
Outline:

  • Sentiment analysis and NLP in market research

  • Programmatic advertising

  • AI-powered CRM systems

  • Tools: HubSpot AI, Google Marketing Platform

5. Capstone: Designing an AI-Integrated Business Solution

Level: Final Year
Objective: Cross-disciplinary team project simulating real-world AI deployment.
Outline:

  • Problem identification with real client or use case

  • Data acquisition and model selection (using no-code or low-code tools)

  • ROI assessment

  • Final presentation to faculty + industry panel

Key Thesis Argument:

Business students don’t need to become AI engineers, but they must understand how AI can unlock value, shape strategic decisions, and disrupt traditional roles. The thesis argues for practical literacy, strategic thinking, and ethical awareness rather than deep technical training.

Potential Tools & Platforms to Integrate:

  • Microsoft Copilot

  • Tableau, PowerBI

  • ChatGPT Enterprise

  • IBM Watson Studio

Salesforce Einstein