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MBA vs. Data Science qualifications: Does #AI and #DataScience explain the fall in MBA applications?


Last week, the financial times wrote that there was a sharp fall in MBA applications in the USA.

The Elite MBA programs were not affected by this trend, but most others were 

Many factors contribute to this fall in applications – including Visa/immigration issues, rising tuition fees for MBA programs etc.

In this post, I offer an alternate explanation

Does AI and Data Science explain the fall in MBA applications?

Many people commence their MBA program after a few years in the industry.

They believe that the program would give them a jump in their career to higher paying ‘management’ type jobs.

However, this once secure progression path looks now severely flawed.

Today, for many people, AI and Data Science qualifications offer alternate avenues to career progression.  (see Landing a 150k USD #datascientist job)

In addition, Enterprise AI itself could impact many of the functions of middle management (report writing, employee reviews etc).

 AI could take over middle management jobs as per CIO magazine.

Other jobs that need an MBA such as investment bank analysis jobs could also be taken over by AI

Having said that, there is scope to rethink the MBA by putting AI at the core of every business processes in an Enterprise.

Currently, such a program does not exist, but I expect it will soon.

It would also be much more technical/ analytical than existing programs – for example in the need to code.

How would an AI centric MBA program work?

For any enterprise, the business processes have remained the same.

Based on SAP Enterprise business processes – an Enterprise would comprise of the following functions. Sales and marketing, Customer support, legal, Procurement, Production, HR, Finance, IT Enterprise Intelligence.

To incorporate AI at the heart of each business process:

  • Firstly, we would look at the metrics for value creation for the process i.e. how do we quantify the value of the business process.
  • Then, we would look at what information underpins the value of the process.
  • Next, we would determine how can this information be learned (not just with data – but also with simulation)
  • AI/ML are well suited to problems that are arduous, complex or inscrutable: Arduous problems are those in which people are competent, and could codify a solution into a program, but it would be impractical to do so. For complex problems, people are competent but codifying that capability into a program is prohibitively difficult (ex : Object recognition). Inscrutable problems are those in which people do not have competence. In these fields, we cannot label or organise data to underpin a predictive engine. Deep learning excels at inscrutable problems because neural networks can determine the parameters to optimise through automatic feature detection.
  • We would then consider the productivity of the process from first principles with humans and AI working together
  • Finally, managing productivity is only half the battle. The more exciting aspect of AI deployment in the Enterprise is to understand the ‘known-unknowns’ i.e. try to understand, predict and manage many processes that are currently not understood quantitatively.

Thus, ironically, future MBA programs could be transformed by embedding AI deeply within the MBA itself

The views in this post are my own and not of any organization I am associated with

 

Image: Frederick Taylor – who was one of the first to apply scientific principles to management https://en.wikipedia.org/wiki/Scientific_management

 

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