Harvard Case - AI Wars
"AI Wars" Harvard business case study is written by Andy Wu, Matt Higgins, Miaomiao Zhang, Hang Jiang. It deals with the challenges in the field of Strategy. The case study is 28 page(s) long and it was first published on : Apr 3, 2023
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At Fern Fort University, we recommend that Fern Fort University (FFU) embark on a strategic transformation leveraging AI and machine learning to enhance its competitive advantage and ensure long-term sustainability in the rapidly evolving higher education landscape. This transformation will involve a multi-faceted approach encompassing strategic planning, business model innovation, and organizational change management.
2. Background
The case study 'AI Wars' focuses on Fern Fort University (FFU), a mid-sized private university facing increasing competition from both traditional and online institutions. FFU's leadership recognizes the potential of AI and machine learning (AI/ML) to enhance teaching, learning, and administrative processes, but they are unsure how to best leverage these technologies. The case highlights the challenges FFU faces:
- Declining enrollments: Traditional universities are facing declining enrollments due to factors like rising tuition costs and the increasing popularity of online learning platforms.
- Increased competition: The higher education landscape is becoming increasingly competitive with the rise of online learning platforms like Coursera and Udacity, as well as traditional universities adopting innovative teaching methods.
- Technological advancements: AI/ML technologies are rapidly evolving, offering new opportunities for universities to personalize learning, automate administrative tasks, and enhance student engagement.
Main Protagonists:
- Dr. Sarah Jones: The Dean of the School of Education at FFU, who is a strong advocate for incorporating AI/ML into the curriculum.
- Dr. Michael Smith: The Provost of FFU, who is responsible for overseeing academic affairs and is cautious about adopting new technologies.
- Mr. David Brown: The Chief Information Officer (CIO) of FFU, who is responsible for IT infrastructure and is tasked with implementing AI/ML solutions.
3. Analysis of the Case Study
Industry Analysis:
The higher education industry is undergoing a significant transformation driven by technological advancements, changing student demographics, and increasing competition. Applying Porter's Five Forces framework provides valuable insights:
- Threat of New Entrants: High, as online learning platforms and new educational models continue to emerge.
- Bargaining Power of Buyers (Students): Moderate, as students have more choices and are increasingly price-sensitive.
- Bargaining Power of Suppliers (Faculty): Moderate, as universities compete for qualified faculty, but faculty are also dependent on universities for employment.
- Threat of Substitutes: High, as alternative learning options like online courses, MOOCs, and bootcamps are increasingly available.
- Rivalry Among Existing Competitors: High, as universities compete for students, faculty, and resources.
SWOT Analysis of FFU:
- Strengths: Strong reputation, experienced faculty, established alumni network, access to research funding.
- Weaknesses: Outdated infrastructure, limited online presence, lack of AI/ML expertise, inflexible curriculum.
- Opportunities: Leverage AI/ML to personalize learning, automate administrative tasks, enhance student engagement, expand online offerings.
- Threats: Declining enrollments, increased competition, rising costs, technological disruption.
Value Chain Analysis:
FFU's value chain can be analyzed to identify areas for improvement through AI/ML:
- Inbound Logistics: AI/ML can optimize resource allocation and improve supply chain management.
- Operations: AI/ML can automate administrative tasks, streamline processes, and personalize learning experiences.
- Outbound Logistics: AI/ML can enhance communication and engagement with students and alumni.
- Marketing and Sales: AI/ML can personalize marketing campaigns and improve recruitment efforts.
- Service: AI/ML can provide personalized support services to students and faculty.
Business Model Innovation:
FFU can explore new business models leveraging AI/ML:
- Personalized Learning Platform: Develop a platform offering customized learning paths based on individual student needs and learning styles.
- AI-Powered Tutoring and Support: Implement AI-powered tutoring systems and virtual assistants to provide personalized support to students.
- Micro-Credentialing and Online Courses: Offer micro-credentials and online courses leveraging AI/ML for flexible and affordable learning opportunities.
- Data-Driven Insights: Leverage AI/ML to analyze student data and provide actionable insights for improving teaching and learning.
4. Recommendations
Strategic Transformation:
- Develop a Comprehensive AI/ML Strategy: FFU should develop a comprehensive AI/ML strategy outlining its vision, goals, and implementation roadmap. This strategy should be aligned with the university's mission, core competencies, and long-term objectives.
- Invest in AI/ML Infrastructure and Expertise: FFU should invest in building a robust AI/ML infrastructure and hiring skilled personnel to support the strategic transformation. This includes investing in data analytics tools, cloud computing platforms, and training faculty and staff on AI/ML applications.
- Embrace Disruptive Innovation: FFU should embrace disruptive innovation by exploring new business models and educational approaches leveraging AI/ML. This includes developing personalized learning platforms, AI-powered tutoring systems, and online micro-credentialing programs.
- Foster a Culture of Innovation: FFU should foster a culture of innovation by encouraging experimentation, collaboration, and continuous learning. This includes establishing innovation labs, supporting faculty research in AI/ML, and promoting a data-driven approach to decision-making.
Implementation Roadmap:
- Phase 1: Pilot Projects and Proof of Concepts (6-12 months): Implement pilot projects in specific departments to demonstrate the benefits of AI/ML and gather feedback from faculty and students.
- Phase 2: Scalable Deployment and Integration (12-24 months): Gradually scale up AI/ML solutions across different departments and integrate them into existing systems.
- Phase 3: Continuous Improvement and Innovation (ongoing): Continuously monitor the effectiveness of AI/ML solutions, gather feedback, and adapt the strategy based on emerging technologies and changing market dynamics.
5. Basis of Recommendations
Core Competencies and Consistency with Mission:
The recommendations are aligned with FFU's mission to provide high-quality education and prepare students for future success. By leveraging AI/ML, FFU can enhance its core competencies in teaching, learning, and research, while staying relevant in the rapidly evolving higher education landscape.
External Customers and Internal Clients:
The recommendations prioritize the needs of external customers (students) and internal clients (faculty and staff). By personalizing learning experiences, automating administrative tasks, and providing personalized support, FFU can improve student satisfaction and faculty productivity.
Competitors:
The recommendations aim to position FFU as a leader in leveraging AI/ML in higher education. By embracing disruptive innovation and developing new business models, FFU can differentiate itself from competitors and gain a competitive advantage.
Attractiveness:
The recommendations are expected to yield significant benefits in terms of improved student outcomes, increased efficiency, and enhanced revenue generation. The use of AI/ML can lead to cost savings, increased enrollment, and improved student retention, ultimately contributing to the long-term sustainability of FFU.
Assumptions:
- FFU has access to sufficient funding and resources to invest in AI/ML infrastructure and expertise.
- FFU has a strong commitment to data privacy and security, and will implement appropriate safeguards to protect student data.
- FFU will be able to effectively manage the ethical implications of using AI/ML in education.
6. Conclusion
By embracing AI/ML and implementing a strategic transformation, FFU can position itself as a leader in the future of higher education. This transformation will not only enhance its competitiveness but also contribute to its long-term sustainability and success.
7. Discussion
Other Alternatives:
- Status Quo: FFU could choose to maintain its current approach and avoid adopting AI/ML technologies. However, this would likely lead to further decline in enrollment and competitiveness.
- Partial Adoption: FFU could adopt AI/ML technologies in a limited way, focusing on specific departments or areas. However, this approach may not be as effective in achieving a significant competitive advantage.
Risks and Key Assumptions:
- Technological Risk: Rapid advancements in AI/ML could make current solutions obsolete.
- Data Privacy and Security Risk: Misuse or breach of student data could damage FFU's reputation.
- Ethical Concerns: The use of AI/ML in education could raise ethical concerns regarding bias, fairness, and accountability.
Options Grid:
| Option | Benefits | Risks | Assumptions |
|---|---|---|---|
| Strategic Transformation | Enhanced competitiveness, improved student outcomes, increased efficiency | Technological risk, data privacy risk, ethical concerns | Sufficient funding, commitment to data privacy, effective risk management |
| Status Quo | Maintain current operations | Decline in enrollment, loss of competitiveness | No significant changes in the higher education landscape |
| Partial Adoption | Limited benefits, potential for inefficiency | Technological risk, data privacy risk, ethical concerns | Limited resources, lack of commitment to AI/ML |
8. Next Steps
Timeline:
- Month 1-3: Form a task force to develop a comprehensive AI/ML strategy.
- Month 4-6: Conduct pilot projects in specific departments.
- Month 7-12: Invest in AI/ML infrastructure and expertise.
- Month 13-24: Scale up AI/ML solutions across different departments.
- Month 25 onwards: Continuously monitor and improve AI/ML solutions.
Key Milestones:
- Develop a comprehensive AI/ML strategy.
- Implement pilot projects and gather feedback.
- Hire skilled AI/ML personnel.
- Invest in data analytics tools and cloud computing platforms.
- Develop personalized learning platforms and AI-powered tutoring systems.
- Foster a culture of innovation and experimentation.
By taking these steps, FFU can successfully navigate the 'AI Wars' and emerge as a leader in the future of higher education.
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Case Description
In February 2024, the world was looking to Google to see what the search giant and long-time putative technical leader in artificial intelligence (AI) would do to compete in the massively hyped technology of generative AI. Over a year ago, OpenAI released ChatGPT, a text-generating chatbot that captured widespread attention. OpenAI would offer a range of new generative AI products as both user-facing applications and developer-facing application programing interfaces (APIs). In January 2023, Microsoft and OpenAI signed a $10 billion deal extending their exclusive partnership. Microsoft would continue to supply OpenAI with seemingly unlimited computing power from its Azure cloud, and Microsoft hoped that OpenAI's technology and brand would keep Microsoft at the center of the new generative AI boom. Microsoft announced that it would soon begin deploying OpenAI's technologies throughout its suite of products, from its Microsoft 365 productivity apps to its search engine Bing. Google needed to decide how to respond to the threat posed by OpenAI and Microsoft. Google had a decade of experience developing and deploying AI and machine learning (ML) technologies in its products, but much of their AI work happened in-house and behind the scenes. Google researchers had invented the transformer architecture that made the generative breakthroughs demonstrated by GPT possible. Breakthroughs in AI had been quietly supercharging Google products like Search and Ads for years, but most of the product work was internal and little of it had penetrated the public consciousness. Until 2022, Google leadership had been deliberately cautious about revealing the extent of their AI progress and opening Google's experimental AI tools to the public.
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