Let’s talk about talent shortage
There’s been a lot of talk about the shortage of data scientists and engineers, and unfortunately, the problem is going to get worse before it gets better. When you consider the increasing demand for Artificial Intelligence (AI) expertise in all types of businesses and the role that AI is playing in making companies more competitive, there’s no question that it’s a serious issue.
We’re seeing AI applications across industries, in situations as diverse as saving the environment, predicting who will be re-admitted to hospitals or which medical device might fail, and it seems like use cases keep on coming. As Andrew Ng, a noted computer scientist, was quoted as saying, “I actually have a hard time thinking of an industry that I don’t think AI will transform in the next several years.”
And, industry statistics bear that out. According to a Stanford University, AI Index report, there are 4.5 more jobs in the field since 2013. Glassdoor found that data scientists lead the pack when it comes to salary, job satisfaction and available positions. And, an Ernst & Young survey found that the biggest obstacle to implementing AI projects throughout the organizations was the shortage of AI expertise, according to 56% of respondents.
Part of the problem is that we’re just not graduating enough data experts. Campuses, such as Stanford University and Boston University, are offering degree programs, and new training programs are cropping up everywhere, but even with these programs, we just can’t keep up with the burgeoning demand.
This is concerning on many levels – from an individual company’s market outlook on a micro level to our nation’s ability to compete on the international stage. I believe we must act decisively, proactively and swiftly to address this problem, and I’ve identified four areas where we can have positive impact:
- Growing the government role. The government needs to do more to bridge the AI expertise gap. For example, it can allocate more money to R&D to develop advanced AI tools to enable data scientists to build algorithms faster, better, more accurately. It also should provide more grants and other economic incentives to encourage people to learn data science through courses at training programs, colleges or in advanced degrees.
- Educate early and often. Schools should be teaching technology classes to everyone in order to prepare the next generation of students for the mid-21st century workplace. Cultivating an interest in and curiosity about tech should begin at the earliest levels of education, even in kindergarten. And that’s just the beginning. Throughout primary and secondary school, classes in technology should be required and taught alongside science, math, literature, history and language to provide the must-have knowledge in today’s world. Everyone should learn how to code before they graduate.
Colleges should build upon this knowledge with more advanced courses, and more universities and graduate programs should be offered, providing critical, in-depth expertise, particularly in data engineering and data science. - Retraining is a key part of the solution. Companies should seize opportunities to retrain their workforce from roles that are shrinking to ones that will continue to be in demand, such as data science. For example, workers trained in traditional coding and legacy systems would be ideal candidates to learn data science. Similarly, the government should promote retraining programs for skilled, educated workers from other fields.
- Strategic partnerships are the cure. Companies looking to fast-track key AI initiatives are turning to solution providers, nearshorers and other strategic business partners. These firms provide highly educated, trained data scientists and engineers, and the advanced GPU processors and infrastructure to manage huge amounts of data. Leveraging these types of partners not only helps companies address a talent shortage, but it can be a more cost-effective long-term solution, since it can be costly to build AI expertise in-house.
In addition to these measures, we must fire on all cylinders to make a difference, and that requires collaboration. Just like the government-private sector industry initiatives in STEM, we can bring multiple stakeholders together to form consortiums to address the AI expertise and talent shortage and build a brighter future for AI innovation. Surely, if we put our heads together and collaborate, we can achieve a groundswell of interest in AI careers, but it begins with setting out sites on the goal and then making it happen.
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