Humans Matter: Skills and Traits That AI Won't Provide
A lot of digital ink has been spilled speculating how generative AI (genAI) is coming for everyone’s jobs. Despite this, we haven’t seen the full takeover of generative AI just yet – a Tech.co study found that only 4% of businesses say that AI has had an extensive impact on replacing job roles, for example. A Wall Street Journal Article summarizes why this may be the case:?
?“Modern work is complex, and most jobs involve much more than the kind of things AI is good at—mainly summarizing text and generating output based on prompts. And whatever job AI does, it needs human oversight and vetting to get usable results.”?
While we may not have reached a major displacement stage, our research clearly shows that generative AI is definitely changing jobs. Sixty-six percent (66%) of data and IT leaders surveyed reported that their job responsibilities have already changed due to generative AI, and 67% reported that their teams’ jobs have also changed.
When we interviewed a data leader working in a financial services organization in the UK about how generative AI has changed workers’ roles, they had this to say:
“My team’s job responsibilities have undergone significant expansion and refinement in response to the introduction of genAI. They are now intricately involved in implementing, monitoring, and maintaining AI systems within our organization; this includes configuring AI algorithms, integrating them with existing systems and data sources, and conducting ongoing performance evaluations to optimize system efficiency and accuracy.”
Wondering how to separate the noise of AI and analytics from useful and trusted advice? Join a panel of experts as they break down the path from AI hype to first use case.
A human-forward framework for using generative AI
Since we’ve accepted that generative AI will change our jobs, now is the time to focus on how you can combine your uniquely human skills with generative AI for the best outcome.
Experts widely agree that having a human-in-the-loop (HITL) is a best practice when using AI, including generative AI. This means that human oversight and intervention is involved throughout the process of using genAI to ensure high-quality and accurate outputs. For example, a human may review data labeled by generative AI for accuracy, edit content before publication, or resolve complex issues that the generative AI model can’t handle alone. Combining generative AI with human expertise achieves better outcomes and helps to ensure that AI systems function reliably and ethically.
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When we surveyed data and IT leaders about generative AI last summer, 70% reported that they trusted genAI to “deliver initial, rapid results that I can review and modify to completion.” This is a perfect example of human-in-the-loop.?
When considering how generative AI will impact your job, position your skills and expertise for functioning effectively as that human-in-the-loop. This can mean that you emphasize your unique domain expertise, your analysis and interpretation skills, or your uniquely human qualities.
Our research on the Enterprise of the Future in late 2023 may be a good place to start when deciding what uniquely human attributes you should emphasize. We asked respondents what skills they expected humans to provide and which skills they expected AI to deliver when working together:
So, considering these results, you may want to showcase your creativity, emotion, critical thinking, morality, and intuition to set yourself up for success when working with generative AI.
Newsletter Round-Up
Review these resources to learn more about what the future of generative AI will look like in the business:
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2 个月Generative AI is influencing job roles but hasn't fully taken over yet. Only 4% of businesses report major job changes due to AI, as the complexity of work still needs human oversight. Sixty-six percent of data and IT leaders say their job responsibilities have changed, with new tasks like implementing and maintaining AI systems. A human-in-the-loop approach, where people ensure the accuracy of AI outputs, is suggested. Seventy percent of leaders trust AI to provide initial results they can refine. To work well with AI, emphasize skills like creativity, critical thinking, and intuition. These complement AI's strengths.