Challenges of artificial intelligence in business curriculum

Artificial Intelligence (AI) is becoming an important component of various sectors and in decision-making in various domains. Research in AI has seen tremendous growth, thanks to big data, escalated processing speed, and innovations in AI-based models. McKinsey Global Institute predicts that by 2030, at least 70 percent of companies will have to adopt at least one type of AI technology and around 60 percent of the current occupations will be automated in the next ten years. Recognizing the importance of AI in almost every field, many countries have regarded AI as a national priority. To promote AI and the research involved, the USA launched the American Artificial Intelligence Initiative in 2019. Out of eight national strategies in this initiative, one strategy is to “provide training and education in AI for the American workforce” (National Science & Technology Council, 2019).

When it comes to AI education, the subject is in great demand by students from engineering or technical background, working professionals who are keen to adopt newer skills, etc. However, it is still a challenge in academia to introduce this very complex and important area of technology to audiences beyond students wit computing and engineering backgrounds. It has been observed that engineering students do not hesitate in opting for AI-related courses, but students from b-schools do hesitate and at the same time hope to remain relevant in the age of AI. According to industrialists and corporates, students from b-schools who are also aspiring managers, if equipped with highly sought-after AI and relevant skills can enhance their chance of employment now and in the future. According to academic researchers, industrialists, and corporates, AI  technologies are going to be present in almost every field in the future, therefore business professionals must become familiar with the principles on which these technologies function and also the risks they carry.

To prepare aspiring students for the era of AI, educators have felt the need and importance of this facet of technology and have started to incorporate AI training in higher education curricula. However, a survey of business school deans shows that many business and management schools are finding it difficult to incorporate AI training in business education. One of the biggest challenges that b-schools face is the task of curriculum development due to the lack of pedagogical resources. According to a review of some scholars, most of the textbooks in AI are written for technical audiences and very few are targeted at business audiences. 

Moreover, in this field, new techniques, libraries, as well as models get developed very rapidly making it a worthy, yet daunting task to keep the pace with latest innovations. On the other hand, skipping out some of the fundaments for the sake of students, who get a holistic view of AI and machine intelligence, will shortchange them in the future. Looking at the current demand for business managers with AI skills, most of the elite b-schools in India have started to make AI part of their curriculum.

However, these courses often focus on introducing statistical models and machine learning models concerning data analysis and hardly prepare students for future work environments where diverse AI technologies are used. The selection of effective teaching methods in curriculum development is yet another open question that remains unanswered. It remains unclear if coding-related assignments should be required for AI courses targeting business students. In engineering and computing disciplines, coding is used as one of the primary teaching methods which are important in a deeper understanding of the methods and logic. However, subjects on data structures and algorithmics, and coding have not been introduced in b-school yet; without these concepts, future business managers will have to consider AI as a black box and leave the critical decisions to machines.

B-schools always give importance to case study reading which is helpful many times even in understanding AI and the techniques involved and their presence at various sectors at the conceptual level. However, students can't gain the necessary understanding of AI by just reading the stories about how companies employ AI for decision-making and problem-solving. It is a fact that business graduates will not use all intricacies of AI as engineering graduates, but they still will require some practical knowledge of programming languages.  

Student learning outcomes and student perceptions are very important to design a curriculum and for continuous improvement. Since AI education has not been widely offered in b-schools, there has been little discussion about student learning outcomes towards various aspects of the curriculum. Design principles and guidelines are not clear or are yet to be designed for AI education in b-schools. The Association to Advance Collegiate Schools of Business (AACSB) has anticipated the importance of AI education in b-schools but they have not recommended a model curriculum for it. Most of the widely prescribed textbooks in AI contain a large number of mathematical formulas, computer algorithms, pseudocode, etc which most business students find very difficult, and even intimidating, to read. Consequently, the content of such books may appear less relevant to business students who wish to learn AI technologies and their applications in organizations.

Many authors these days are targeting textbooks of AI for business students that discuss applications of AI and case studies. Such books lack depth and students may not understand the implications of using AI in real-world problems. It is important to mention that during recent times, many courses on AI have appeared online, however, the courses or tutorials are often limited to one of the more specific topics which do not show any depth in AI. The majority of such online courses are not designed for business students.  

AI has a lot to hold for the future, the area is going to see a lot of growth which means more and more jobs will be created. Academicians and educators need to prepare students for the revolutionary change brought by AI technologies. This is high time that academicians and accreditation bodies think and discuss how to reshape the AI-related courses in the business curriculum. One suggestion would be to include courses such as ‘data structures’ and ‘python for data sciences’ as pre-requisite for AI aspiring students. This will help students with no computing background to acquire AI competence and skills. The course coverage also needs to achieve a balance of fundamentals and the latest technologies involved. Business students prefer practical assignments and value them over written ones. To achieve skill-based learning outcomes, it is important to make a careful design of coding-related assignments. This will ensure that students get hands-on experience and knowledge. Future business managers will not require to program every time to develop AI applications. However, the risk is that without sound knowledge of AI concepts and principles, they might end up making bad decisions. 

The article has been written by Dr. Akhter Mohiuddin, Professor - Data Sciences, Great Lakes Institute of Management, Gurgaon