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Why upskilling is key for an organization to become AI-driven!

Why upskilling is key for an organization to become AI-driven!

Why upskilling is key for an organization to become AI-driven!

Published on 11.08.2020 by Dr. Nitin Kumar

While reading this article some of you might think that the author runs an educational training academy so his goal for writing this article is to sell their services. I wouldn’t blame anyone thinking this way and to tell you the truth, had I not been following the market trends in AI, I would have thought exactly the same. Saying that, let me inform the reader: the motivation here is not to capture new clients but is rooted in evidence and facts presented by multitude of surveys conducted by key consulting players in AI industries (ex: Big 4s). My sole purpose for writing this article is to bring awareness in the Swiss Labour Market about why Upskilling/Reskilling is a key pillar in becoming AI-driven for an organization.

 

As an AI education strategist, I am in regular contacts with Swiss Businesses on their AI strategy, with a special focus on reskilling. One thing is for sure: every one of them wants to use AI, has a goal to become data-driven, and is worried about not being able to compete in the age of AI. Though various companies are at different stages in their AI journey (very beginning, somewhere between beginning-to-middle, or advanced), very few of them are thinking about reskilling their workforces. I do not blame them; leaders are on average 3.1 times more likely to replace employees with new AI-ready talent, than focusing on retraining, even though the talent availability works highly against them (1). Let me inform all those leaders whose preferred modus operandi is firing hiring and working with an army of consultants, that this is a short-term strategy. There are two main forces here at play in build-buy strategy: 1) importance of your business processes and 2) costs. If Data and AI are as important to you as any other core business process, and if you are happy to handover your core processes to externals, feel free to do so with your AI strategy as well. For short-term well-defined projects where external expertise is often needed, borrowing consultants is always a good idea. However, if your goal is to become an AI-driven business, forget that path. When looking at costs, numbers clearly state that build-buy (ex. reskilling) is cheaper than buying and borrowing (2).

 

If you are serious about AI in your organization, hopefully, this blog will help you convince your management to invest in people. If you do decide to invest in people, you will not be alone. The examples are already there: Amazon has announced a $700 million fund to reskill 1000,000 workers (3). Orange, the French telecom giant, €1.5 billion (4) and PwC has set aside $3 billion (5).

 

Not too far in the past (only in 2018) Europe decided to start with a coordinated effort to address challenges presented by AI (6,7) and identified four key areas to focus on: 1. increasing investment, 2. data availability, 3. fostering talent, and 4. ensuring trust. The shortage of ICT professionals and the goal to attract talent has led the EU to come up with a Blue Card system, a visa easing program to attract talent from outside Europe.

 

MIT Sloan Management Review, in collaboration with Boston Consulting group in their report on Winning with AI (8), clearly state that AI Pioneer companies focus heavily on upskilling their existing workforce. What they also report is organizations actively re-skilling their workforce see more impact on their AI efforts than their counterparts (56% in former vs 19% in later). What they also mention is Chinese Pioneers work more in collaboration with outside partners on reskilling than their global peers (65% vs 20%). Is upskilling why China has become an AI Silicon Valley? I believe so. In the same year just one-month later, McKinsey reported that AI high performers (adopted AI in 5 or more business activities) are 3.5 times more likely to have active continuous learning programs on AI (9). Similarly, high performers are more likely to hire/train translators that work between businesses and analytics roles.

 

Investing in AI talent and training

Reference 9

 

A recent survey conducted by Deloitte on around 1,900 IT and Business executives’ states that 68% of them report moderate to extreme skills gaps in the AI space (1). Employers report heavy difficulty in filling AI job openings impeding their growth and giving them no choice but to reskill their workforces. Though business executives would prefer to hire talent, 59% companies across the globe are still training their IT staff to deploy AI solutions (figure below) where German companies are clearly leaders in retraining.

 

Companies are focused on training for the future with AI

Reference 1

 

If you are an employee reading this article, I highly suggest you try to convince your employer to think about reskilling. If you are an employer, I hope the message is clear to you. It might sound easier to replace an existing workforce with a new one, but it comes at the cost of losing business knowledge, loyalty, trust in an organization and above all from a financial perspective, it will cost you more to buy-borrow than to build. AI or not AI, investing in people brings unlimited rewards of growth for organizations and a sense of satisfaction and joy among employees knowing that their employer cares for them. Knowingly or unknowingly, joy is what we all seek after all.

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