Business schools in the generative AI era: the six keys to success

The rapid evolution of generative AI technologies has fuelled competitive races in many sectors. However, many organisations are struggling to harness their value, leaving them vulnerable to competitors. The higher education sector is particularly vulnerable and faces an existential threat as these new technologies exacerbate existing challenges.

Surviving that threat and succeeding in this new environment requires these 6 strategic actions.


  1. Re-think teaching approaches and purposes.

Generative AI applications do offer great promise for the higher education sector, especially in relation to innovative teaching. Although progress on this is quite slow, and not radical enough, there is a growing practice of interesting approaches. For example, Professors Mitchell Weiss at Harvard, Lance Weiler at Columbia University, Mike Sharples at the Open University, and Dr Lukas Meier at Cambridge University are leveraging generative AI for intensive case study preparation, creating immersive experiences and artwork, as Socratic debate partners, and teaching medical ethics, respectively.

As generative AIs becomes even more advanced, business schools will face a bigger challenge when policymakers and students start questioning their purpose as learning centres. Already AI tools like GitHub Co-pilot can generate almost expert level code and highly realistic business simulations that enable students to gain practical experiences outside of a business school setting. In areas like marketing and advertisement content creation, MusicLM and MusicGen are now able to generate new music from text prompts whilst image generators are already producing art that can sell for $425000 or win competitions like the piece Théâtre D’opéra Spatial. Additionally, applications like Flair AI are now capable of professional product photography generation through text prompts.

In the sciences, advances such as Alpha Fold have made new molecular compound generation possible and OpenProteinSet’s launch earlier this month is set to revolutionise this even more. These and the fact that applications like Google’s Med-PaLM 2 can perform at an "expert doctor" level raises questions about how future medical and other science students should be trained and what they need to learn. There is a greater urgency for answers given that recently a generative AI application outperformed some Stanford University Medical students on a free response clinical reasoning exam.

Business schools face the same questions too, with the advanced technological capabilities of tools like GitHub Co-pilot as well new ones like Code Interpreter, which have disruptive implications for traditional teaching and learning across a wide range of subjects including accounting, finance, economics, and business-related areas.

Success in this new era will require two crucial changes: a fundamental shift from academics as instructors to coaches and guides, and an added value that transcends traditional lectures and assessments.


  1. Develop a business school-wide response to the technology.

Quite rightly, discussions in the sector have started to shift from concerns about cheating to exploring the potential of generative AI tools in lectures. Business schools, however, need to go beyond reactionary bans of the tools and limited usage in classrooms to thrive in this new era. Towards this end, the value of generative AI in various other areas, such as faculty recruitment, finance, sustainability, and student well-being, must be recognised.

A school-wide AI audit is a key first step to this, followed by focusing on building what Professor Liz Bacon – a leading authority on AI – terms "Digital Estates" with almost the same focus as “Real Estates” such as lecture theatres and seminar rooms.

Also, crowd-sourcing ideas, as done by Amazon, is an effective way to tap into the knowledge base of employees across business schools, which can yield valuable ideas and staff buy-ins.


  1. Ensure senior leadership teams’ AI literacy.

Leadership teams need to understand the technology well enough in order to develop strategies that truly unlock its potential value, which McKinsey estimates at $6.1 to $7.9 trillion. This AI literacy must include its impact on competitive dynamics, an important aspect, as unlikely competitors start to reshape the sector’s competitive landscape: Hello History AI, Socratic AI and Khanmigo are some such examples. Indeed, traditional advantages like physical campuses are no longer sufficient in this rapidly evolving environment, as HMV discovered when they sought to continue relying on large stores and brand recognition in an emergent internet era. Generative AI-literate senior leadership teams will recognise the need to adapt, reinvent and act quickly.


  1. Engage with humans, both staff and students.

Across the HE sector, concerns that generative AI will be a pretext for further job cuts create unease amongst staff. To address this, business schools must engage in open, transparent conversations with staff, addressing their fears and uncertainties. As well as that, support packages should be developed to help staff understand and leverage the technology. This can free up their time by automating tasks like student support – as Harvard plans with CS5O, a generative AI bot – and assessments.

Engaging students proactively is crucial. They should be educated about the benefits and risks of these technologies. Efforts will however be needed to mitigate against inequalities as tiered payment structures may restrict access for certain students, worsening existing disparities in outcomes. This can be done through institutional licenses. Also needed are clear policies on generative AI use by students and academics alike.


  1. Exploit opportunities for new income and revenue streams.

For business schools, there are three areas of opportunities in relation to generative AIs.

Firstly, innovative generative AI education courses, ranging from degrees to flexible programs or micro-credentials for upskilling employees. The demand for AI skills is evident with reports indicating rising numbers of job openings and companies struggling to find qualified individuals.

Secondly, the rapid development of the technology opens avenues for new applications, products, and services. There are opportunities here for well supported start up incubator ecosystems and revenues from spinouts that explore novel business models based on Large Language Models.

Thirdly, there is great potential for research utilising generative AI capabilities in crucial areas like climate change, circular plastic innovation, medicine, and pharmaceuticals. Whereas business schools can explore these individually, better societal outcomes will come from collaborations with other faculties or institutions. Initiatives like Midland Mindforge and Responsible AI UK are certainly steps in the right direction.


  1. Develop profound understandings of the implications for using the technology.

The implications of generative AI technology are still unfolding, but some concerns have already emerged especially without strong regulatory frameworks yet in place. For instance, biases inherited from training data and tendencies toward misogyny and misinformation in applications are evident. There are ethical concerns too, such as exploitation of low-wage moderators and the appropriation of others' works.

There is also the disturbing potential for misuse, as individuals without expertise could use Large Language Models to develop harmful viruses.

Such an understanding will ensure guardrails are in place to reduce reputational and financial risks.


Dr Chris Odindo, MBA, PGCHE, FHEA, CMBE is an Associate Professor at Leicester Castle Business School, De Montfort University





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