Lowest to Highest in Four Months
This weekend, a small business owner told me a story about the power of AI. Paraphrasing: “We had a guy who was a mid-to-low performer, but he was teaching himself AI on the side. Four months later, he’s one of our highest performers.”
The difference: armed with AI, he built tools that helped the company use its data more effectively and replace tools they were paying for elsewhere. And in doing so, he went from someone who might be expendable to someone the company couldn’t afford to lose.
It’s worth noting that none of that work was squarely within his job description, and the tools he built crossed normal organizational boundaries.
My first reaction to the story was, “That’s amazing.” But as I thought more about how that employee might have fared in other organizations, I realized that the story also called into question the systems we use to determine who’s a “low performer” and “high performer.” For example, if someone can quickly go from one status to the other by working outside of their job description, it might be the job description that’s the low performer, rather than the person. If someone can improve their performance by using new tools, perhaps we should evaluate how effectively the manager secures resources or teaches others to use those tools. And if it only takes four months to change performance, perhaps we need to scrutinize our language to ensure we’re saying “they are performing poorly right now,” rather than “they are a low performer.”
As we kept talking, one of our reflections was just how much the new technology might affect the existing systems society uses to develop human resources. For example, we have an entire education system that assigns worth and rewards based on small differences in the power of someone’s brain. The difference between an A and a B in a high school class, or a few points on the SAT, can have significant, long-lasting effects on how people are perceived in their careers. We willingly pay inflated tuition costs for college degrees, especially those with a brand name, because there’s a positive ROI from signaling our brainpower is slightly more than others’. And in the workforce, we run performance reviews that try to separate someone whose individual competencies are a 7 out of 10 from someone who’s an 8.
All those approaches are called into question in a world where someone can achieve a performance turnaround in four months by bolstering their individual capabilities with technology and creating tools for group impact.
This isn’t an argument for changing how we select, evaluate, and reward people in the workplace overnight. Rather, it’s to say that we should be open to the notion that new technology will reveal the need for fundamental shifts to existing structures and ways of working. That is, today’s experiments with AI might not be limited to just the work we use it for.
That notion will no doubt be uncomfortable, in part because people don’t like change, and in part because there are existing power equities in the status quo. But somewhere, there’s another employee just like the one I heard about this weekend—quietly creating new ways to be valuable, while the people and systems meant to judge that value are still using old standards. The real question is whether their leaders will be open enough to take another look.