The pace of new tech advancements today is such that the ‘only constant in life is daily change,’ to update the famous quote by the Greek philosopher Heraclitus. In just one week last month, we witnessed the release of more than 100 Artificial Intelligence (AI) applications in various domains.
AI technology mimics human intelligence to perform tasks more efficiently and quickly. While the quality of AI varies and depends on the quality of its training data, it can iteratively improve itself rapidly.
Several corporates are either building their own AI systems or customising the systems developed by others by integrating plug-ins into their products. The paradigm for the future of work revolves around human-AI interaction.
Accordingly, the job market landscape for would-be managers is continuously evolving. Along with developing a deep understanding of subject-specific knowledge, the approach to delivering management education must integrate innovative methods to make students industry-ready with the following skills that complement the new tech rather than competing with it.
Today’s would-be managers must have skills to handle large and complex data sets and clean and pre-process data to feed into AI systems. Even if they outsource such tasks, they must be able to make sense of errors in data management — this is not a new skill, but one that has been much underrated. AI systems are only as good as the data used to train them.
Therefore, ensuring that the data used to train AI models is representative and diverse is essential. Managers must be aware of this challenge and take steps to mitigate it by regularly auditing data used to train them. Statistics and business analytics courses in management education should take this into account.
Management schools should introduce if not already done, AI courses to their curriculum to ensure that MBA students are familiar with AI concepts, technologies, and their applications in various business scenarios.
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Industry-ready graduates would need to be sound ‘prompt’ engineers. They must know how to ask and refine questions for the AI systems to get the desired output and detect AI-system errors. These include errors in the AI written codes, biases, discrimination in its responses, misinformation, or false information.
There are already courses available on online educational platforms such as Udemy for mastering prompt engineering. Management education either needs to have a full-fledged course for developing this skill or make it a part of the evaluation component for individual courses.
For example, in a recent assignment for MBA students, I asked them to produce the original prompt to ChatGPT, one of several AI tools, and the subsequent refinement to the prompts to get the best output. Students then detect errors, misinformation, and a wrong context, and indicate modifications to the AI output to reflect their value add over the ChatGPT output.
AI provides valuable insights and data, but it’s up to humans to make decisions based on it. Individuals with strong critical thinking skills can analyse AI-generated output and make informed decisions.
The key value added by humans will come from the ability to spot changes in trends and patterns that are not observed in the past data and hence, overlooked by the AI systems. Higher educational institutions must modify their curriculum to develop students’ critical thinking and problem-solving abilities.
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EI refers to understanding and managing our own and others’ emotions and behaviour. Individuals with high EI can complement AI by providing empathy and understanding customers or clients, at least until AI systems learn to understand feelings and emotions. The broader skill set here includes the ability to motivate and inspire others.
Higher educational institutions should carry out a psychosomatic assessment of students and then customise the necessary inputs to help students improve the necessary parameters of EI.
The importance of integrity and personal ethics is ever-rising in AI systems, where managers and leaders must deal with data privacy, consent, and security conflicts.
Another challenge is the lack of transparency in AI systems’ decision-making which makes it challenging to justify them and creates a lack of trust in AI systems. Business leaders must ensure that AI systems are used ethically and comply with relevant laws and regulations.
The core course of ethics in management education should now add a component aimed at understanding and resolving AI systems’ ethical and trust considerations.
AI technology is excellent at analysing data based on its training. However, at present, it has not mastered the creative thinking that the human mind is capable of. Highly creative individuals who think outside the box will add value to AI projects by developing new ideas and solutions.
Management education in higher-tier institutions already has courses such as design thinking and entrepreneurial mindset which would rise in prominence. Every domain course should introduce a creativity component.
For example, in a behavioural economics course this year, one of my class assignments was redesigning an existing website by incorporating nudges to encourage people to move away from the fad of fast fashion.
Effective oral communication will be one essential skill that will distinguish in-demand managers. AI systems can now write well and produce emails, letters, speeches, reports, and so on. However, individuals who can articulate complex AI concepts, models, and output to non-technical stakeholders and communicate new ideas would be sought after.
Good communication also improves teamwork skills essential to work effectively with cross-functional teams and collaborating with others.
Management education, accordingly, is increasing its focus on improving the delivery and the style of articulation by students. However, scaling up the verbal component of the course evaluation would pose a challenge, given the vast number of students. The answer may lie in the AI-trained robot evaluators!
AI technology is constantly evolving, and individuals who are adaptable, resourceful, and can learn new skills and tools quickly would be valuable assets to their companies. There is little scope for learning and testing the adaptability skills in traditional lecture-based teaching and theoretical learning.
Management education needs to raise its focus on experiential learning, technology integration in classrooms, and hands-on training via real-world live projects and industry collaboration for the ongoing application of technology in various business scenarios.
Management education should incorporate interdisciplinary learning by encouraging students to take courses from other domains such as computer science, statistics, and psychology.
This would help MBA students develop a deep understanding of human behaviour, AI technologies, and integration of human-AI.
Higher educational institutions such as management institutions much adapt to new teaching content and pedagogy to better prepare students for the workforce.
(Dr Vidya Mahambare is Professor of Economics and Director of Research, Great Lakes Institute of Management, Chennai.)