Don't let AI training go to waste: The right way to upskill your workforce

Organizations are making significant investments in AI training opportunities without really knowing what their workforce needs — and it's stunting their employees progress.  

Sixty-two percent of employees still say they do not have the skills to "effectively and safely use" the tech, according to recent research from software company platform Salesforce. And it's not just employees; 70% of business leaders surveyed said they don't think their teams have the skills to safely use AI, even as the tools become more widely available — a disconnect that may be driven by a misunderstanding of what it truly means to train for AI. 

"AI is developing quickly and employers are struggling to keep pace," says Corey Hynes, founder and CEO at digital upskilling platform Skillable. "That's because the deep, specialist knowledge needed to create value and ensure alignment using AI cannot be learned through theory alone." 

Read more: 9 ways employees are using AI at work

Yet, theory is largely what leaders have been relying on over the course of the last few years when it comes to their AI training structures. While content and assessments are an important part of the learning journey, the kinds of AI projects being implemented in organizations today — such as checkout-free shopping enabled by computer vision, co-pilots and smart home assistants — require expertise beyond classroom or content-based learning.

The problem, according to Hynes, is that leaders don't know enough about what AI training actually entails to tailor their training programs to the needs of their workforce.

"There's a lot of nuance in AI training and it ultimately hinges on what you want to achieve, and with who," he says. "But the only way business leaders can feel confident that their people are truly ready to work with AI is to create hands-on learning environments that imitate, as closely as possible, the real-world tasks someone will have to do with AI." 

For example, some workers will only need basic skills, because they are working alongside AI, not deploying it or strategizing. Those in intermediate positions that are maintaining and integrating AI tools in existing systems — such as IT and database managers — will require more advanced AI skills and higher skill levels. Finally, those in charge of AI investment, deployment and governance will need high AI literacy, specific AI trust and ethics skills and the ability to assess risk and respond appropriately. 

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"If upskilling initiatives solely rely on content, theoretical assessments and other knowledge-based learning, you'll miss a crucial validation and application element that may hinder your AI transformation," Hynes says. "Without an experience that offers practice, assessment and validation, your learners won't feel confident performing a new skill in their role."  

Hynes urges employers to restructure their AI upskilling and reskilling efforts to include opportunities where employees can practice close-to-real-world scenarios that will help them not only retain information longer, but help them understand how to use advanced tech tools within their roles, specifically. This can be done by incorporating case studies and role playing in training sessions as well as investing in tech that can support simulations. 

"Pilots aren't allowed to fly passengers until they have completed specific hands-on training, and surgeons cannot pick up the scalpel without hands-on experience either," Hynes says. "AI is going to be so revolutionary that we cannot rely on content and courses alone to get our people ready."