Artificial intelligence (AI) is revolutionizing our way of life by automating decisions, predicting outcomes, and optimizing processes. From our phones to shopping, medication, banking and manufacturing, AI is everywhere.

However, there is growing concern that advances in AI are being slowed down by a shortage of trained talent that's needed to scale AI solutions across organizations. This talent shortage is slated to cause a massive imbalance in AI adoption and its scalability across the enterprise.

But what is causing this shortage of talent? Is there really a shortage, or is the problem our inability to utilize talent effectively? 

There is much discussion across forums about the right enablement and talent strategy for AI. But the underlying problem is not the lack of skills but the lack of the right individuals connecting with the right opportunities. There are many extraordinary people in the market who would be perfect fits for a career in AI, but the industry simply is not doing enough to provide the right platform to launch their careers.

That's because there are no best practices and standards developed for the next generation of deep learning and AI skills, and adoption at most organizations is still nascent. Even several entrenched players do not have a strong talent development strategy in place for nurturing their existing AI/ML talent.

An AI talent development strategy

The solution lies in creating a strong talent development strategy along with the right platforms and frameworks for talent to be cultivated, by:

    Top skill sets most suited for transitioning to AI roles

    Reskilling/upskilling will ensure adequate scaling of enterprise AI and leveraging transferable skills that are relatable to AI. Today, the top transferable skills for an AI career are linear algebra, probability, statistics, ML algorithms, data science, programming, AIOps, text analytics, image analytics and data mining. 

    In general, mathematics plays an important role in AI, and specifically in ML. Skills in applied mathematics in the areas of linear algebra, probability theory and statistics, multivariate calculus, algorithms and optimization are particularly relevant. As ML works with huge amounts of data, data science competencies help in predictive analytics, data modeling, analytics and other aspects of AI. There are also multiple programming languages to cater to the algorithms, libraries and frameworks in AI that cover text analytics, image analytics, compute deep learning and neural networks. 

    Fixing the skills gap by focusing within the organization and bringing about internal transformation will take some patience and conscious effort. But this is an investment worth making, as creating a robust talent pool and pipeline will be one of the primary requirements for seizing the opportunities that the next generation of the AI revolution will provide. 

    Balakrishna DR, popularly known as Bali, is the executive vice president and head of the AI and automation unit at Infosys.



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