Business School Innovation in the Age of Generative AI

Report Icon Briefing
Wednesday, November 29, 2023
By AACSB Thought Leadership
Image by istock/Blue Planet Studio
Generative AI is here to stay. How can business educators best prepare learners for the possibilities—and the risks—of integrating this disruptive technology?

Last September, AACSB’s Innovation Committee, chaired by Sherif Kamel, dean of the School of Business at The American University in Cairo and vice chair-chair elect of AACSB’s board of directors, met in Copenhagen to launch an exploration into the world of generative artificial intelligence (AI) and its potential transformative effects on business education.

Since the integration of ChatGPT—an AI-enabled language-generating technology—into mainstream applications, education and business leaders have heightened their interest in the impact of generative AI on our work, leisure, and learning activities. Within these dynamics, business schools are central players. They develop future leaders who can effectively manage organizations in a digital, AI-enabled world; serve as the upskilling and reskilling hubs for existing workers; and house incubators for novel solutions in a dynamic business environment.

The committee’s discussions revealed that business schools around the world are at different stages of understanding and integrating generative AI into their schools’ operations and educational activities. However, one undeniable consensus emerged from the meeting: this revolutionary technology has significant implications that will shape the future of business schools—from learner preparation to faculty research and engagement to the operational and strategic landscapes of these institutions.

The following narrative focuses on two major questions business educators are considering in light of generative AI:

  1. How will business schools need to rethink the ways they prepare a new generation of learners?
  1. What ethical and equity implications will the technology create for business schools globally?

Rethinking Student Preparation

Current State

Generative AI is poised to significantly influence a variety of jobs and tasks spanning multiple industries. It will have an outsized impact on knowledge-based occupations—typically higher-paying jobs with higher educational prerequisites—versus manufacturing occupations, which have been most disrupted by prior industrial and technological revolutions. As hubs for future talent, business schools are tackling the risks of future job displacement by equipping students with the tools and skills needed to navigate the changing workplace. The displacement of certain jobs or roles by generative AI is inevitable. As this looming risk becomes more of a reality, business schools are called on to create “a new generation of knowledge workers who can handle a digital future.”

Generative AI Landscape

Potential Threats
  • Much of humans’ cognitive work will become redundant.
  • Certain jobs and tasks will be displaced by AI.
  • Critical thinking skills will diminish.
  • Human connection and personalization will decrease.
  • Outputs will become more homogenous, and creativity will wane.
Potential Opportunities
  • Productivity will increase as certain tasks become more efficient.
  • New jobs, roles, and industries will be created.
  • Workers will have more time and space for engaging in higher-level or creative thinking.
  • Leaders will benefit from greater data-driven decision support.
  • Advancement will accelerate across industries and economies.

3 Key Questions on Student Preparation

 What Will We Teach?

  • With the ongoing potential for generative AI to disrupt multiple industries, as well as alter job roles and skill requirements, business schools must enhance their offerings with opportunities that prepare future leaders to strategically leverage AI in business operations and processes.
  • The overall portfolio of skill development priorities may undergo changes. For instance, while a deeper understanding of AI will remain critical, certain technical skills like coding, which many schools have recently integrated, might become redundant given generative AI’s capacity to take over these tasks. Some educators are concerned that employers will rely too heavily on generative AI for cognitive tasks, potentially undermining creativity and critical thinking. This concern highlights the need for schools to cultivate soft skills in their curricula, encouraging students to harness the unique human capabilities essential for successful organizations.

How Will We Teach?

  • The significance of practical, experiential learning will grow, along with a focus on nurturing students with a learning mindset rather than a knowledge mindset. Business schools will need to embrace new types of pedagogy, shifting from a learning experience that asks students to memorize information to one that helps them learn to ask critical questions.
  • Generative AI should be seen not as a substitute for traditional classroom education but as a valuable tool that can enrich and elevate the learning experience. Educators are already exploring the use of generative AI in various ways, such as summarizing lectures, facilitating interactive and dynamic engagement with educational materials (in contrast to static textbook reading), assessing the accuracy of information, and offering feedback to students.

Who Will Teach?

  • Faculty roles are evolving to encompass mentoring, and their ability to facilitate discussion and learning may become equally crucial alongside their need to have deep disciplinary expertise. Some faculty may be less inclined to embrace AI compared to their more tech-savvy students, necessitating additional professional development on how to incorporate AI as an educational tool. Faculty will need access to more deliberate training, even starting in their doctoral programs, that equips them with the skills needed to integrate technology into their teaching and research.
  • Although generative AI can serve as a valuable tool in course design, it is essential that faculty continue to develop courses, ensuring their authenticity and educational quality. Faculty members are encouraged to innovate with AI solutions that can aid in activities like providing timely feedback to students, delivering sections of the core curriculum, supplementing asynchronous learning, or co-teaching. These adaptations will require that faculty invest their time and energy into learning and that the school incentivizes and creates space for faculty to experiment, test, and learn.

Could the absence of “struggle” in learning have adverse impacts?

One discussion during the meeting focused on the notion of “struggle” within the learning process and whether generative AI might eliminate this crucial aspect of education. Although the idea of struggle might initially seem negative, research suggests it is a vital component of learning, fostering resilience, grit, deeper understanding, and innovative problem-solving. On one hand, generative AI’s ability to automate tasks and swiftly retrieve information can grant students more time and cognitive capacity to grapple with complex concepts, implications, decisions, trade-offs, and more in their learning journey. Conversely, some of the tedious tasks that generative AI can automate, like web research and key information identification and assembly, may hold educational value. How will we come to view the future generation? As one that is unaccustomed to “struggling” and expects information to be readily available, or as one that can focus on the most critical elements needed for timely decision-making?

Ethical and Equity Implications of Generative AI on Business Education

Current State

The potential for adverse consequences stemming from generative AI is a legitimate concern openly debated within the AI community. Some experts acknowledge AI’s potential superiority over human intelligence in its ability to scale, process vast amounts of information, and “learn.” Consequently, some individuals fear that less intelligent entities controlling more advanced systems could develop unforeseen behaviors, such as the ability to generate and execute their own code or learn how to plan, negotiate, prioritize, collaborate, and possess other uniquely human skills. Human operators maintain control over the algorithms they feed into the system and that dictate its actions. However, this control comes with ethical responsibilities and exposes us to risks; we may impose unintentional biases or allow the technology to fall into the hands of malicious actors. 

Ethical and Equity Risks

  • Data Bias: When the data used to train AI has biases, it can lead to biased results, increasing the risk of discrimination in areas like hiring, healthcare choices, credit decisions, and social evaluations.
  • Data Availability: Generative AI requires a lot of data to learn from. If some groups or views aren’t well-represented in the data, the AI might not understand or include them, further marginalizing certain groups.
  • Unequal Access: Those with resources and expertise will benefit most from the technology, leaving less-resourced players behind and exacerbating existing disparities.
  • Intellectual Property: Copyright, ownership, and plagiarism issues may arise in AI-created content. Determining the originality of AI-generated content and defining ownership rights presents complex questions and can disadvantage creators who may be unaware of their rights.
  • Privacy and Misinformation: Generative AI could be used to generate realistic content, such as deepfake videos that violate individuals’ privacy and manipulate public perceptions.
  • Regulatory Challenges: Developing regulations and standards for the ethical use of generative AI is challenging and uneven across contexts. Balancing innovation with safeguards against negative impacts will require careful consideration and collaboration.
  • Power Dynamics: Organizations that develop and control advanced generative AI models can amass significant influence and power, potentially concentrating decision-making among already-influential groups and amplifying existing inequalities.
  • Hidden Costs and Environmental Impacts: Training large AI models can contribute to increased energy consumption; for instance, a significant amount of water is required to cool data servers and generate electricity to run data centers. Generative AI is also dependent on energy-intensive computer chips with short lifespans, and regular use of these can contribute to higher carbon emissions and environmental strain.

On the Minds of Business Education Leaders

How equitable and inclusive is generative AI … or will it be? The use of AI to predict student outcomes raises ethical concerns that educators should be mindful of and approach cautiously as the technology continues to advance. While the mainstream nature of platforms like ChatGPT creates readily accessible solutions for almost any individual, the use of generative AI technology at scale and in more sophisticated ways may present an advantage to elite and well-resourced institutions with the funds to hire expertise and build the necessary infrastructure. This reality prompts questions about whether, in the long run, generative AI will be a great equalizer or create an even wider digital divide.

Will continued advancement of generative AI require a greater emphasis on ethical and critical thinking in the curriculum? As generative AI continues to infiltrate society and show promise as a powerful tool for creating organizational efficiency and productivity, its responsible use will become an important focus in business ethics curriculum. As students become more accustomed to interacting with machines, educators will need to elevate the importance of critical thinking development to ensure that students can exhibit the high-level judgment needed to make well-informed, responsible decisions for all stakeholders and not merely rely on spoon-fed information generated by AI.

How do I establish regulatory frameworks when circumstances are continually changing? Developing appropriate standards and regulations on the responsible use of generative AI is a challenge faced not only on the geopolitical stage but also among universities and business school leaders, as well. From questions about data bias and integrity to intellectual property and copyright issues, developing a standardized approach is proving to be difficult. As much as business schools aim to be innovation leaders leveraging the latest technologies, they must also prioritize the safety of their stakeholders. The enigmatic nature of generative AI presents opportunities to advance cross-disciplinary dialogue, including partners and experts in business, tech, legal, and government sectors to understand the broader implications and determine the most appropriate solutions.

What’s Next?

AACSB calls on schools to lead boldly and embrace innovation as they deliver value to their student, industry, and societal stakeholders. The far-reaching impact of generative AI in the many areas that business schools touch is a major test of their innovation agility. In the first half of 2023 alone, venture capitalists invested 15 billion USD in global generative AI ventures. McKinsey’s projections of up to 4.4 trillion USD in potential value across sectors underscore the technology’s promising economic impact. Not surprisingly, industry leaders are eager to harness this vast economic potential and productivity gains and are placing new expectations on business schools for their role in preparing a highly skilled and resilient workforce and advancing knowledge in a rapidly changing environment.

These expectations usher in a new set of obstacles that business schools will need to be able to address, such as:

  • Competing with new, formidable players in the business education arena. These competitors are equipped with sophisticated platforms and abundant resources to scale generative AI solutions. Tech giants like Google and Microsoft are a couple of examples, alongside a host of other emerging tech enterprises.
  • Struggling to secure resources. Prestigious and well-endowed educational institutions have a distinct advantage, leveraging their ample resources to attract top faculty, forge strategic partnerships, and secure funding to enhance their academic offerings.
  • Planning for the future. In a landscape where even AI experts are grappling with the full extent of generative AI’s capabilities, strategic planning proves to be challenging.
  • Falling victim to “shiny object syndrome.” Some institutions may pursue innovation and substantial investments in emerging technologies to stay competitive; unsuccessful experiments might cast doubt on their overall quality and effectiveness.
  • Navigating the fine line between experimentation and anticipation. in the face of evolving technologies. Should institutions embrace experimentation immediately, or risk being left behind irreparably?

Over the next several months, the Innovation Committee will continue to explore this topic and inform AACSB business schools on how they can lead innovation in a dynamic, competitive landscape while ensuring the quality and educational excellence for which they are known.

Ultimately, AACSB aims to identify innovative practices, successful approaches, and actionable recommendations. These insights will equip educators with the knowledge and tools they need to harness the full potential of AI-driven advances and ensure that business schools not only adapt to, but thrive in, this digital transformation.

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