Businesses are witnessing significant traction on AI – through transformation of existing business models, operating models, channels, propositions and also innovations in business models. All of this is driving significant demand for professionals who can work on AI algorithms and have relevant skills. As of now, the demand side fairly outstrips the supply base of skills in this domain. And this is what is being termed as the “AI Skill Crisis“.

According to Max Life Insurance‘s internal observation of recruiting AI talent, the company found that for every single professional, there are at least three opportunities available. However, on the other hand, an average open AI role takes two months to find the right candidate and fitment in an organization. This is the scale of the AI skill gap we are witnessing today.

AI as a domain does require certain expertise – experience of certain platforms, technologies, concepts etc. Hence, there is a learning path that people need to go through which takes time. It is not just the learning but also applying that learning through real-world projects. While there is a good supply of talent, we are possibly seeing a lag because it’s taking time for all those gears to start turning and the talent pool to build up.

“There are essentially only three sources of talent. Upskilling your existing resources, hiring resources, which are bringing in native AI expertise and capabilities and the third source is short term/ extended talent on a project to project basis or case-by-case basis. Since we cannot meet the entire demand through any one of these levers, upskilling will definitely help here to bridge this shortage,” said Sushant Rabra, Partner, Digital Consulting, KPMG in India.

As the field is constantly evolving, it is necessary for professionals to upskill. At senior levels, apart from official training and understanding of AI technology, we also need a high degree of collaboration and evangelization across teams to enable the adoption of AI strategy into mainstream operations. At the leadership and senior levels, upskilling is key for bridging this gap. Once the senior leadership has been up-skilled to understand AI technologies, they play a pivotal role in driving the AI culture that empowers the tech and AI teams. This helps with much faster implementations and adoption throughout business processes.

How to Upskill talent in AI

To upskill AI talent, organisations must look at sources of talent beyond technology. There are people with functional or business knowledge who can be trained and who could create value for the organization through AI. 30-40% people who are trained on AI are non-technology professionals and are coming from other domains. Spread the net wider, don’t only look at IT, look in business as well.

1. Creating a learning culture:

In the tech world and especially in the field of AI it is critical for all of us to continue to upskill ourselves to remain relevant. At the same time, we are all busy professionals already, and these upskilling initiatives take time, focus, and energy. This makes it even more important to keep learning as and when possible

“We are Max Life Insurance, have provided online AI learning platforms and training by tying up with platforms such as Coursera and exposing the AI training meant for all levels of staff. We also set up workshops on leveraging the cloud stack conducted by our cloud partners . We have tried to provide an environment and tools where the teams can constantly test, experiment, and learn through building AI solutions,” said Suhail Ghai, EVP & CTO, Max Life Insurance.

2. Practical application of knowledge/skills:

People tend to forget 80 percent of what they learn if there is no opportunity to apply the knowledge/skills on a regular basis. Therefore, it is important for businesses to create an environment where employees can apply their knowledge.

“We at PayU, have partnered closely with employees and their managers so that together we create opportunities for the student to apply the newly gained AI knowledge in a safe learning environment. Sometimes this means collaborating on an existing AI project or starting their own mini-project, sometimes this means mentoring someone else on AI but it can also mean transitioning into a new AI-related role,” said Priya Cherian, Chief People Officer, PayU.

3. Creating a guiding star:

Leaders can look at the future of their business in 5 years, where AI would have a role to play. Depending on their sector, they can create an AI NorthStar which will guide their actions over the next 2-3 years and what is it they want to do on that. From there, leaders can derive the technology and talent requirements and drive that like a program in the organization.

The AI skills shortage is widely acknowledged as the most significant impediment to AI’s full potential. Cherian added that according to recruitment firms, it was estimated that the shortage of employees skilled in AI, ML, Blockchain, and other disruptive technologies could go up to 200,000 in 2020. And the number may have increased in 2021. Hence employees – existing and potential – need to be upskilled and trained extensively to address this gap.

While the companies try to solve the challenge of the AI skill crisis through upskill, they should not forget that the future is not going to be restricted to being technology professionals.

“AI talent could also be a business process or a functional professional who wants to build allied expertise in this domain. Given the way these AI platforms and solutions are evolving, you need not have very deep technological expertise to create use cases in that. Hence, we would possibly see a confluence of these skills in the way roles of the future are defined. For example, a supply chain professional in the future may be expected to have certain exposure to AI in that domain as well,” said Rabra.

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