Reimagining Executive Education Programs In The Industry 4.0 Era
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The traditional archetype of learning and employment – where students went to university and learned a skill that served them for the entirety of the careers – is rapidly evolving and changing. Today, we are in the age of continuous learning – where there is an inherent expectation on members of the employed workforce to constantly upgrade their knowledge and soft and hard skills. Earlier, executive education used to be a reserve of a privileged few high performers at large organizations, who showed great promise and rapidly rose through the ranks. Now, continuous learning across all the segments of the workforce is increasingly the new normal – almost to the point where it is the mandate for organizations that wish to grow and succeed in the business sphere. There is an expectation now that even the rank-and-file of the organization devote time to learning, unlearning & relearning and apply newfound ideas and techniques into their area of business.
What has been the driver behind this change? Why has upskilling and reskilling become the norm for the contemporary corporate career? A few important reasons underpin this change. The nature of the business today is extremely dynamic. Business environment and competitive landscape are changing faster than ever, with technology becoming the mainstay of the modern business. Tech-enabled startups are moving in and challenging traditional incumbents across industries. These changes are made a further complex with emergent ideas and changing paradigms of organizational management and leadership. In today’s fast-moving world of business, it is a critical priority for executives to keep their organizations nimble, proactive and armed with every arrow in their quiver, to ensure the continued success of their firms
Executive education is an important medium to achieve this goal and helps bridge the skill gap that is almost certain to rise when industries and organizations face structural headwinds. However, for executive education to live up to the promise and deliver value to employees and their organizations, we need to re-look at the programs itself. We need to ensure that the coursework and curriculum are topical, contextualized and relevant, whilst being personalized to the needs of the organization and its professionals. Here are a few perspectives on how executive education can be adapted for the industry4.0 era:
Expand the Scope of Executive Education and the Courseware
As we dismantle the traditional paradigms of work and education, we also need to rewire our traditional understanding of what an executive education comprises. For years, corporations relied on top-tier management schools and universities to facilitate the essential leadership training for their workforce. In today’s world, rewiring an understanding of leadership is just not going to cut it. Executive education programs need to add more in terms of practical, on-the-job skills, that will help employees perform better and remain relevant to the needs of the business.
There is now a strong case to expand the scope of executive education beyond traditional B-schools and include even MOOC-based education – which is provided by a plethora of websites today. Coursera, Udemy, LinkedIn Learning – to name a few – provide very tactical, hands-on understanding of essential, practical skills that the workforce can put to use right away, while also facilitating the career change aspirations that employees may have. Organizations need to seek out these MOOC-based providers to augment the executive education curriculum in a way that increases its scope and reach among employees.
These programs could very well help employees refresh their skill sets. For instance, such programs could help coders become well-rounded full-stack developers. Similarly, those with data engineering skills could be moved into areas such as high-performance analytics or artificial intelligence. Team leads could be educated formally in the tools and techniques associated with product management. For mapping current employee skills with the contemporary requirements of the business, MOOCs can be a critical intervention to incrementally upskill employees in their domain of work.
Incorporating the importance of shorter, tactical courses
Whilst there is no doubt about the value provided by a long-form one-year executive education program, companies also need to consider the benefits of short-term tactical coursework. Corporations need to augment their training programs with shorter, time-boxed courseware that can deliver instant impact for the organization.
There are two reasons why this is important. Firstly, given the speed at which technology and business mature, it may not always make sense to put someone in a one-year program and wait for the delivery of associated results. In such circumstances, short form courses help deliver faster time-to-value – with employees able to deliver results in weeks, rather than months. Secondly, shorter-term courses also help reduce some of the inherent barriers people have towards learning. Shortening the learning cycle, putting it to use immediately and seeing real-life results, can help employees see instant benefits of the lifelong learning paradigm and break down mental barriers to learning.
Co-create multiple, personalized career pathways
The key word here is ‘personalized’. We need to move away from the old thinking of one-size-fits-all training to deliver more tailored, fit-for-purpose and relevant executive education to employees. To start, organizations need to develop skill maps and assessments – to identify where the workforce is today in terms of the required skill sets and where they are expected to be. Once this is performed, L&D teams can help create personalized learning journey-maps for their employees – based on the career interests and aspirations of employees. For instance, for some employees, it may make sense to provide a refresher and upskilling in their current areas of work and for others, it may make sense to reskill them in new areas of the business. Either way, developing a personalized training regimen for the executive education of the employees will deliver better results and help them excel in their field and improve the efficacy of learning programs too.
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Getting Started With A Career In Artificial Intelligence: Quick Primer
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The last few years have seen Artificial Intelligence capturing the imagination of corporate executives and catapulted into the mainstream of the business world. With a myriad, and ever-expanding set of applications, AI promises to provide a quantum leap in enterprise efficiency, profitability, and competitiveness. Due to decreasing costs of storage, increasingly efficient algorithms running atop chipsets more powerful than over before, AI is witnessing a huge surge in interest and applicability. As companies rush to co-opt AI into their processes, practitioners of this technology are in high demand – which easily outstrips high-quality supply. With a soaring growth in demand and supply struggling to catch up, it is natural for professionals of the current and future workforce to ask – how to get started with a career in AI?
It is important to put down some context. Before answering where one can get started, it is important to first define AI. A simple, yet comprehensive working definition for AI is – the ability of machines to mimic human intelligence and functions. Going one step downstream, building a truly artificially intelligent machine is to equip it with the ability to sense and comprehend ‘stimuli’ within its environment, identify and weigh response options for acting on the stimuli, performing the suggested action, and continuously learning from the impact of the action taken, in a way that informs future decision-making.
Parsing this definition further, AI happens at the intersection of data (represented as the stimulus provided and the feedback loops for learning), mathematics (represented through models which weigh up decision-points and payoffs for each prospective action) and computer science (the technical and logical backbone that governs the flow of data and codifies potential action points). These are the three key ingredients of building powerful AI – and the three areas aspirants to this industry need to master.
Let us double-click on these three areas to understand their criticality to AI systems, and how the workforce can build competencies in each area.
Data Literacy
While we can split hairs over the appropriate terminology (some prefer to call it Data Science, while others call it Data Engineering – depending on how teams are structured), it is important to focus more on the nature of the skill required in the AI arena.
Data skills encompass the entire range of tasks associated with data management for AI – the collection, sorting, storage, and extraction of data for meaningful use. It is data that fuels the growth of an AI application, and therefore the ability to sense incoming data, identify patterns therein and make informed decisions is a crucial building block for a career in AI.
Given the criticality of core data skills, it is not surprising to see data-literate employees – analytics professionals and data engineers – try their hand at reinventing their careers in this domain. Those who do not have a background in these two techniques should get started with courses in business analytics – to understand how businesses slice-and-dice data to inform their decision-making process. Those who have some background of computer science should upskill in data engineering areas i.e. how to effectively leverage emerging concepts in database management to improve storage, management, and extraction of data to feed AI applications in the most efficient manner. Alternatively, computer engineers could also learn business analytics to understand the applications and implications of data for business decisions.
Numeracy
Put simply, numeracy is the ability or skill to work with numbers and mathematical concepts. This is the second key ingredient for a successful career in AI. As I previously mentioned, a key building block of AI is to build the ability to weigh multiple options, probabilities, and payoffs across multiple options, to take the most optimal decision. These are essentially mathematical concepts of inference, probability, decision trees and game theory – and fine-tuning these skills are a critical part of building a great career in AI.
Developing advanced numeracy skills is a natural option for those who are mathematically inclined and have an education therein. Those who don’t have formal education in these areas can rely on numerous online courses that teach statistics and probability, before moving towards more advanced concepts. The takeaway from your education in numeracy should be the ability to formulate optimal pathways to decisions, identifying and accurately scoring multiple options, suggesting responses and continuously informing the mathematical model through a feedback loop, based on the results of responses delivered.
Computer Science
The final piece is to ramp up existing computer science skills to align with the needs of AI application development. There are two sub-areas at play here, namely – conceptualizing the logic (algorithms) and writing the language (code). Computer science provides the fundamental backbone required for improving the scalability and resilience of AI applications. It dictates how the data is operationalized and provides the logical base for mathematical models to process the data.
Python and R are two widely accepted languages in the field of AI. As a lot of existing developing in this domain has been done in these two languages, they provide rich libraries which are a key starting point to AI applications. Those who have a strong inclination and education in programming are highly advised to pick up online courses that provide hands-on skills in these two languages. Computer scientists well-versed with these two languages can also consider expanding their breadth into the numeracy skills – as these two works well in tandem and offer much better job opportunities in AI.
Like AI itself, a career in AI requires one to commit to continuous learning. This field, like any other emerging field, is rapidly evolving with new models and applications coming to light almost every day. While mastery of the above three skills is a good start, it is important to stay constantly updated to stay ahead of the curve. One way to do that is to keep an eye on research papers submitted therein. Additionally, it is equally important to keep an eye on the business end and staying updated on emerging use cases in this arena.
Disclaimer: The views expressed in the article above are those of the authors’ and do not necessarily represent or reflect the views of this publishing house