Three ways to optimise your business school’s AI-related offering

Advancements in new technologies - notably artificial intelligence (AI) – are significantly and rapidly altering the way we work and, in turn, the skills we need. Work practices have evolved both in terms of conditions (our relation to spaces, teams and interactions) and tasks (tools to be mastered, company technological stacks to be integrated into management practices) even this century. And thanks to AI, automation is targeting more and more jobs. In place of traditional quantitative skill sets, AI-leveraging employers are placing a premium on machine learning-related capabilities, as tasks such as data entry, basic data analysis and low-level coding activities are automated. Training savvy and legitimate leaders involves looking ahead of the trends and anticipating the needs for companies.

As workforces change, chunks of business schools’ curricula can become outdated and irrelevant. So institutions have a responsibility to adapt their content and teaching to meet the demand of the market and narrow the seemingly ever-growing skills gap. 

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“But my institution already offers an AI-specific degree programme. What more can we do?” you ask. 

Here are three things for business schools to consider when optimising AI-related teaching. 

Experiment with and adapt teaching content

One of the biggest mistakes a business school can make is not to review or adapt its teaching content regularly. The job requires more than your institution simply offering a specialised MSc in, say, AI or data analytics. Providers must ensure their content is updated to reflect the market and their students. In seeking to improve their courses, schools must embrace experimentation. 

At ESSEC, in an effort to ensure our teaching is both impactful and insightful, we routinely experiment with our teaching, whether through testing, teaching AI-specific skills and knowledge for alternative industries (such as the arts and law) or allowing students to personalise their learning experiences to best meet their needs. In doing so, teaching becomes an interdisciplinary activity while also being tailor-made for students. 

Seek out academic and industry partnerships

None of the above can be achieved without institutions finding the right partners in academia and industry. Cultivating a strong connection with industry is a no-brainer; these organisations (which are also the future employers of your students) can offer real-time feedback on the challenges they face and the skills they need to overcome those obstacles. 

We constantly experiment with our corporate partners to cover research areas that might affect businesses in the long run. These insights will inform the review and adaptation process. We believe that co-creation is critical and have worked on new positions such as AI model owners for the banking group BNP Paribas and the ethics of AI with the transformation consultancy Onepoint. Our latest initiative, “AI for responsible leadership”, with Accenture, aims at highlighting the competencies and management skills for the leaders of tomorrow.

Partnerships with other academic institutions also serve to enrich students’ learning experiences on existing programmes and can expand the breadth of your school’s teaching. Engaging in cross-sector teaching is a key feature that distinguishes a great business school from a good one. Business schools must look to partner with institutions with expertise beyond their own. ESSEC’s collaboration with CentraleSupélec, a leading French engineering school, is a great example of this. Via the BSc in AI, data and management sciences and the MSc in data sciences & business analytics, ESSEC and CentraleSupélec have combined the insights of a business school with those of an engineering institution to offer students, at both undergraduate and postgraduate levels, a breadth (and depth) of learning that, delivered alone, would not be possible. 

The key is not simply to collate courses but to mesh them completely and create new forms of expertise. This isn’t to say that institutions shouldn’t look to cultivate internal expertise – far from it; to partner with the best academic counterparts, your institution must meet the standard, too. Therefore, attracting faculty that will actively advance your efforts to become a leading provider of AI-specific courses is essential. But to truly optimise teaching, institutions must acknowledge their limitations and know when to leverage external insights. 

Build student buy-in through ambassadors

Whether experimenting with existing programmes or looking to bolster your offering with new courses, insights from industry or other academic institutions are simply not enough. Schools need the buy-in and participation of their students. They must be led by the feedback they’re given to ensure that teaching reflects the needs and desires of programme participants. Beyond this, however, schools need to cultivate internal ambassadors – champions of the cause they’re working towards. The explosion of generative AI has masked the reality that, often, when educating around new technologies, institutions are dealing with the theoretical; quite simply, they’re educating about issues and concepts that could take place at some point in the future. This requires a willingness from students to engage in such future-facing learning. But, as we’ve seen in the case of AI, all concepts and challenges seem theoretical until they’re not. 

To get ahead of these issues, schools must prepare students for challenges that are not yet on the horizon – and to do so, they need student engagement. At ESSEC, the student-led thinktank Metalab IDEAS is a space for all emerging questions and topics that might interest future cohorts. 

The skills gap, as we know, is widening. As the need for digital skills mounts, it’s up to educators to meet that demand. Business schools must look to optimise AI-specific teaching to plug that gap. But there is no quick fix. Institutions must be prepared to adapt, leverage external insights and cultivate student buy-in to address the skills crisis. 

Abdelmounaim Derraz, Julien Malaurent and Guillaume Chevillon are co-directors of the ESSEC Metalab for Data, Technology & Society at ESSEC Business School, France.

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