Optimize AI Talent: Perception from Across the Globe

Despite the AI hype, the AI skill gap is turning into some pariah while businesses are accelerating to become demigods.

Reports from the “Global Talent Competitiveness Index (GTCI) 2020” cover multiple parameters both national and organizational to generate insight for further action. This report compiles 70 variables including 132 national economies across the globe – based on all groups of income and at every developmental level.

The sole purpose of the GTCI report is to narrow down the skill gap by delivering the right data inputs. The figures mentioned in the report could be of value to private and public organizations.

GTCI report covered multiple themes that need to be addressed: –

As the race to embrace AI spurs, it is evident to address the challenges faced due to AI and how best these problems can be solved.

The pace at which AI is developing is transforming the way we work, forcing a technology shift, change in the corporate structure, changing the innovation system for AI professionals in every possible way.

There’s more that is needed to be done as AI and automation continue to affect the way we work.

  • Reskilling in workplaces to eliminate dearth of talent

As the role in AI keeps evolving, organizations need a larger workforce, especially to play technology roles such as AI engineers and AI specialists. Looking closely at the statistics you may not fail to notice that the number of AI job roles is on the rise, but there’s scarce talent.

Employers must take on reskilling as a critical measure. Else how will the technology market keep up with changing trends? Reskilling in the form of training or AI certifications should be emphasized. Having an in-house AI talent is an added advantage to the company.

  • Skill gap between growing countries (low performing and high performing) are widening

Based on the GTCI report, it is seen there is a skill gap happening not only across industries but between nations. The report also highlights which country lacks basic digital skills, and this highly gets contributed toward a digital divide between nations.

  • High-level of cooperation needed to embrace AI benefits

As much as the world shows concern toward embracing AI, not much has been done to achieve these transformations. And AI has huge potential to transform society and make it a better place to live. However, to embrace these benefits, corporations must engage in AI regulation.

From a talent acquisition perspective, this simply means employers will need more training and reskilling opportunities.

  • AI to allow nations to skip generations

On a technological front, AI makes it possible to skip generations in developed nations. Although, not common due to structural obstruction.

  • Cities are now competing to become talent magnets and AI hubs

As AI continues to hit the market, organizations are aggressively coming up with newer policies to attract and retain AI professionals.

No doubt, cities are striving to attract the right kind of talent as competition keeps increasing. As such many cities are competing in becoming core AI engines in transforming energy grids, transportation, and many other multiple segments. Cities are now becoming the main test beds for AI-based tools i.e. self-driven vehicles, tele-surveillance, and facial recognition.

  • Sustainable AI comes when the society is equally up for it

With certain communities not adopting and accepting the advent of AI, it is difficult to say whether these communities will not try to distort AI narratives. As a result, it is crucial for multiple stakeholders to embrace AI and developed the AI workforce in parallel.

Not to forget, regulators and policy-makers have an equal role to play to ensure there’s a smooth transition in jobs. As AI-induced transformation skyrockets, educators and leaders need to move quickly as the new generations’ complete focus is entirely based on doing their bit to the society.

Two decades passed ever since McKinsey declared the war for talent – particularly for high-performing employees. As organizations are extensively looking to hire the right talent, it is imperative to retain and attract talent at large.

Despite the unprecedented growth in AI technologies, it is near to being unanimous regarding having hold of organizations to master in AI, forget about retaining talent. They’re not even getting better at it.

Even top tech companies such as Google and Amazon, the demand for top talent outstrips the supply. Although you may find thousands of candidates applying for the same job role, the competition just gets tougher since such employers are tough nuts and pleasing them is not an easy task.

If these tech giants are finding it difficult to hire the right talent, you could imagine the plight of other companies.

Given the optimistic view regarding the technology future, it is much more challenging to convince that the war for talent truly resembles the war on talent.

The good news is organizations that look forward to adopting new technology and reskill their employees will most likely thrive in the competitive edge.

Top influential AI skills to target in 2020

Artificial intelligence in 2020: a trustable year!

The AI market is deemed to reach USD70 billion in the present year, thus causing a drastic effect on the government market, consumer, enterprise globally. 

With market uproars, it is certain obstacles are bound to intercept. However, the effects of artificial intelligence are poised to have huge potential in democratizing expensive services, or boost poor customer service, or assist during medical breakthroughs, and even lightened the work of the overburdened workforce. 

If you’re a tech optimist who believes in creating a world where man and machine can come closer and work together with humans, then you better need to possess mandatory AI skills. 

Based on this report, “2020 Workplace Learning Trends Report: The Skills of the Future,” you will come across how AI is reshaping the world and what are the skills that are a must-know for upcoming AI professionals. 

The report also states that the investment funds that are managed by AI are accounted for 35% of the stock market in America.

Finance machines will rise says a recent article in ‘The Economist.’ 

Trending by the industry’s insights and job trends, these are the skills AI professionals need to master in 2020:

  • Machine learning 

People often use the terms artificial intelligence and machine learning interchangeably, both being entirely different from each other. There’s a lot of confusion between both these terms, however, let us briefly understand what exactly is machine learning. 

Machine learning uses algorithms that obtain knowledge and skill through experience and without human intervention. It relies on big data sets that remind the data to find common patterns. 

For instance, if you provide machine learning programs with a lot of data on skin conditions and tell them what these conditions signify, these algorithms can easily mine the images (data) and help in analyzing the skin conditions even in the future. 

Now the algorithms will compare the images with the previous image data and try to identify the pattern that exists between them that have similar kind of patterns. 

However, if the algorithm is given a new image of a skin condition and the condition is unknown in the future as well. The algorithm will take the image to analyze it with the past and the present situation, thus the prediction of the condition will remain improper since one needs to feed new data for the algorithm to learn in order to predict what the condition is. However, AI will only learn by obtaining knowledge and learn how to apply these knowledges. Artificial intelligence helps to train computers aiming at a result that could provide a better outcome than a human can do. 


  • Python 

As the phrase is used, ‘orange is the new black’ so are AI and ML becoming the new black of the IT industry. With the extensive expansion of volumes of data, machine learning and artificial intelligence are used progressively for processing as well as analysis. To be honest, a human brain can function and analyze huge amounts of data but a human brain is restricted by limitations and can only contain a certain amount of processing or analyzing. 

AI in that aspect has a huge capacity and requires no limitation. 

Now Python plays a major impact on AI and machine learning as we undergo an upsurge for AI engineers. 

According to the latest trend search on Indeed, Python is said to be the most popular language for AI and ML, as stated by Jean Francois Puget, from the machine learning department in IBM. 


The how’s and why’s?

  • Python offers a low entry barrier for data scientists to effectively use Python without wasting much effort in learning the language. 
  • It has a great choice of library ecosystem, no wonder why everybody loves Python. For instance, Pandas for producing high-level data structure or data analysis, Matplotlib to create 2D plots and histograms, or Keras that is widely used for deep learning. 
  • Python as a language is flexible and is a great choice for machine learning. AI professionals can easily use Python along with other programming languages to achieve their target. 
  • Python offers easy readability for every developer, thus making it easy to understand the codes of their senior, change or develop a new one whenever needed. 

React (web)

If you’re a web developer thriving to enter the AI world, here’s a great chance for you. You can now build sweet AI apps using React.js. These offers web developers a new platform to bridge the gap between web developers and professionals who are getting trained in AI skills. 

A web developer can now easily build apps leveraging artificial intelligence that can learn from experiences or learns to react to the user’s inputs like facial expression, etc. 


If one isn’t aware, Google AI is build using Angular. Building a chatbot from scratch using a Dialog flow conversation platform, previously known as can be challenging. NLP (natural language processing) could be tough to deal with in machine learning. 


Docker is now used in every field of the software industry. Confused with the term? Well, Docker is nothing but a DevOps tool. 

Docker can also be called as an exploiter tool exploiting operating-system-level virtualization that basically helps in developing and delivering software in packages that are called containers. 

Though this may sound complicated, you need to simply know that Docker is a complete environment that offers you a platform to build as well as deploy software. 

In a nutshell, one may say that Docker can be employed for varied phases of the machine learning development cycle like data aggregation, data-gathering, model training, predictive analysis, and application deployment, etc. 

Tech professionals such as DevOps engineers and AI engineers will need to supercharge their skill set in 2020. Till the time this knowledge gap persists, we will continue to see talent shortage in the job market.