Generative AI is rewriting the rules on how enterprises define and cultivate talent. Employers must stay ahead of the curve—honing employee skills to be compatible with AI capabilities and instilling people with the knowledge to unlock its full potential.
Business leaders are aware of this talent gap; 82% of respondents in Microsoft’s 2023 Work Trend Index Annual Report acknowledged that employees will need new skills to prepare for the growth of AI.
And those skills aren’t strictly technical—or confined to technical workers. Everyone has the potential to use AI to augment their skills and abilities.
Existing roles are changing to make better use of AI, and new roles are quickly emerging. Prompt engineering, for instance, is a job title that didn’t exist until recently, says Andrew Mullins, Principal Data Scientist, Kin + Carta.
Regardless of the roles you’re filling, adaptability is critical in the face of changing tools and technology. Here’s how to build your AI dream team.
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Since accessible GenAI tools are relatively new, most organizations are starting from a fairly level playing field. Enterprises that encourage the workforce to experiment and make smart bets are likely to gain an advantage as early adopters. The key to integrating AI successfully is making sure those bets are calculated and intentional.
“You want to hire people who are willing to write the book—not just read it,” says Taylor Bradley, Head of HR Business Partners at Turing, an AI-powered “talent cloud” matching employers with remote developers. With GenAI, businesses can use existing data to create innovative solutions, but only if they hire people willing to test new prompts and practices.
“Thinking about automation with AI is not a natural thing that we all just develop…It really is a muscle and a skill set,” says Writer CEO May Habib. “And the more that leaders can identify people who are leaning into that and doing it, the more ideation happens, the more experimentation happens.”
So what does that look like in a candidate? Practically speaking, “you need people who like to document their work,” Mullins says. Recording what works and what doesn’t and sharing those findings with colleagues is a critical piece of the puzzle.