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When Elon Musk announced that the artificial intelligence (AI) will be smarter than human intelligence by 2025, it made global headlines. What surprised everyone was not the forecast itself, but the timeline he predicted. Musk, after all, is seen as one of the AI and tech era mavericks. There is no debating the transition to AI-based intelligent machine systems, but many researchers feel more time is needed to overcome the challenges and till products are more homogeneously visible, much like the transition to steam and electric power and then electronic. This gives aspiring and future managers time to gain perspective, evaluate themselves, and reassess if and how they can be equipped to manage demands of the Industrial Revolution 4.0 (IR 4.0).
That change is a constant, is now a way of life for companies, but the exponential and multi-dimensional pace of change expected to be brought by AI is making businesses fundamentally reassess their business models. Today, there is a global flux in how businesses should work in the AI era, all of which has been exaggerated multi-fold by the Covid-19 pandemic. The fact is, we are already transitioning into IR 4.0, and as with previous industrial revolutions, it is bound to affect working, jobs and business structures -- the extent of which will become clearer with time. The McKinsey Global Institute forecasts up to a third of US and German workers, and nearly half of Japanese workers would be replaced by automation by 2030.
However, as Musk himself admits, it is not that the role of humans will cease to exist, but it is just that it will be increasingly constrained. Depending on the nature of the organisation a manager works with, intelligent AI systems would be their co-workers, enablers, or even supervisors, where the managers enable the AI system for them to process and analyse. What sets the humans apart from the machines is the soft skills and their emotional intelligence. AI systems will increasingly start stepping into human roles, these softer skills, that will set the boundary of where the role of AI ends, and that of humans begin.
All this sets the stage for chaos. Management is after all the opposite of chaos, and hence the managers of the future will need to excel at handling the chaos and emotional challenges that this AI era of volatility, ambiguity, complexity, and ambiguity, or ‘VUCA’ in business school parlance, is likely to throw at them, and their teams. It is here that the experience and breadth of cross-functional knowledge that comes with experience become pivotal.
This transition is already underway, as the initial findings of large-scale, international research by IESE Business School (IESE), highlight. In the US, and other advance markets, it is cross sectoral and has been expedited by Covid-19. The share of managers with AI skills in overall recruitment is continuously rising, even as there is a relative decline in core AI engineer jobs. Also, most of these AI focussed managerial postings come from companies that are either very large or have huge R&D budgets or are cash rich, reflecting how corporate leaders with adequate resources are increasingly looking to embrace AI and seeking mangers who have deep understanding of their business and of key principles of how AI works, to be able to connect two in a meaningful way.
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Simply put, it is high time we redesignated or reinterpreted MBA as Management by Algorithm (MbA), where the students build understanding of AI, its usage and application in business, all the while honing emotional intelligence based soft skills, which would form the core of future human workforce. It is hence critical that the focus of management learning and education immediately shifts to developing these skills at the individual, business school and larger education system level business school and larger education system level, where aspiring managers are selected based on both qualitative and quantitative skills. Business Schools like IESE Harvard, INSEAD, Oxford universities, are already inculcating AI based management in their MBA courses, to prepare students for the future.
In India, a country with over a half a million aspiring managers produced each year, this has immense implications. The fact that there is only one Indian management institute in FT’s Global Top 25 list (ISB), and that there are thousands of management institutes that do not even appear in India’s domestics rankings, is reflective of the quality of management education in the country. The challenge would be to make management education ready for the AI revolution.
It is here that the New Education Policy (NEP), presents a lot of hope, especially in the sphere of management education. The fact is most of the development and application of AI-based systems is happening in the developed economies and China. While campuses of foreign universities are some time away, content sharing and joint programmes in partnership with existing management institutions, especially from the private sector, can provide the much-needed quantum leap that management education needs both to upgrade standards, and more importantly to be future ready.
To make the NEP work, it is critical to align the selection process with future skills. The Common Admission Test (CAT), which is heavily inclined words quantitative and logical reasoning skills, appears somewhat inadequate.
Most leading management education institutions are offering specialised high quality management programmes like those in healthcare, policy, design, architecture among many others, and it would be advisable that students align their interest, skills and expertise to effectively manage and apply AI systems to gain competitive advantage, as also achieve emotional satisfaction or their Ikigai.
The author is a strategic communication and marketing professional, with keen interest in education, policy, and advocacy. He has published research papers on business strategy and on communications and has conducted digital marketing classes at IMT Ghaziabad.
