Training presents challenges
A recent study of 1,000 global companies by Accenture found that AI is already creating three new categories of jobs: trainers, explainers and sustainers. Trainers are the people who teach AI systems how to act -- whether it's language, human behavior or the intricacies of human interaction. Explainers are the liaison between technology and business leaders, providing more insight and clarity into machine learning for the non-tech workers. Sustainers are the workers required to maintain AI systems and troubleshoot any potential issues.
"Some jobs were highly technical and required advanced degrees, but other roles demanded innately human things such as empathy and interaction. Downstream jobs, such as those in sales, marketing, or service will change to take advantage of the insights from AI, but many of the core skills will remain," says Estes.
It might sound like any job related to AI will require years of technical knowledge, but that isn't the case. We've already seen a shift in tech hiring -- companies often need highly specific skill sets that are hard to find in potential candidates. As a result, more businesses are hiring employees with the right soft skills, and then training them in technical skills.
"This opens a new window of opportunity for a diverse and booming workforce, as many organizations don't necessarily require a college degree from their technical employees. If you onboard a person with a willingness to learn and an understanding of basic technology skills, you can train them on a multitude of systems and applications," says Papatsaras.
Papatsaras also expects to see an overall shift in the education system, where students will be trained from a young age on robotics and AI. It's already happening outside of the education system - games like Minecraft can help teach children the fundamentals of coding to kick start STEM education.
A measured approach to AI
The real takeaway is that any approach to AI will need to consider the human aspect of every business. AI has great potential to increase efficiency and accuracy and it's already been proven in certain industries.
For example, Estes points to the use of AI In banking to identify "rogue traders" and money laundering schemes. It's also improved healthcare by "increasing the speed and accuracy" of cancer diagnosistics. AI can also help reduce the cost and length of human trafficking investigations, a situation where time is precious.
In these examples, argues Estes, AI hasn't replaced jobs, but has positively impacted efficiency. Yet, he still cautions against complacency with AI.
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