(un)Qualified: the human element in AI-assisted talent development
This past fall I attended a high profile human resources conference. Between keynotes featuring the field’s leading voices (#BersinFangirl here), happy hour networking, and sizzling pitch competitions, I explored the endless trade-show floor (a Costco-sized bazaar of every HR technology imaginable).
The event was designed to impress and inspire. Mostly, it did. But somewhere amid the glamorous tech, boasting “generative,” “powerful,” and “ethical” AI that promised to unleash workforce potential and deliver exponential outcomes, I began to wonder: at what cost?
And, biased as I am about learning’s role in transforming individual potential and performance, I’ve been wondering about it ever since.
AI’s capacity to improve hiring decisions and talent development is obvious. For the most part, sign me up. It’s a powerful advancement. But, as with any gain, we run the risk of loss. In this case, and specifically when it comes to how we identify and develop a human’s talent: What is at risk when a person’s unique potential and vocational self-determination doesn’t match the taxonomy of their education or experience data? If “what I want to be when I grow up” meets the limitations of an algorithm? And is found wanting.
Consider any number of historical and cultural greats or the most well-known Biblical or religious figures, how many would have cleared an AI prompt directed to look for keyword competencies? Very few would have “qualified” for roles they went on to define and dominate; the world being better for their outsized ambitions!
Let me be clear, the benefits of AI to eliminate historic kinds of bias from hiring and talent development programs is a good thing. Commenting on this in a Forbes’ article, Nancy Xu (founder and CEO of an AI-based recruiting company) explained:
“I'm driven by the fundamental belief that talent is universal, but opportunities aren't. I started to think, how could I use AI to democratize access to jobs and get people hired because of their talent and not just pedigree?”
Her words underscore AI’s promise to revolutionize opportunity and access. So, too the beneficial use of AI to tighten the hiring timeline (2x faster) and drive operational efficiency is a plus.
Beyond Background: Prioritizing Potential
Organizations more quickly identifying people with the right qualities, experiences, background, education, skills and appropriate licenses and credentials is useful. But what about the human leaping from one field into an entirely new one, whose learning could impact and even revolutionize an organization because they bring an entirely unique set of experiences, education, and skills to the table?
Organizations miss out when they fail to prioritize potential in their hiring and development approach. What questions will AI help them answer? Which possibilities remain open for exploration even if the initial output from AI doesn’t identify a “match”? How will the organization value opportunity and access not just in usual ways (demographics, education, etc.), but in innovative ways? What is the plan to optimize outliers?
Opportunity for Outliers
The vast majority of the time, AI’s benefit to talent development is likely to be more additive than not. Ensuring its use doesn’t limit authentic inspiration or overlook the power of human learning is the trick, particularly when it comes to its influence on recruitment, retention, and career development.
Organizations create opportunity for outliers in three ways:
Be a learning organization: don’t just ask the right questions, ask the unexpected questions. Be (deeply) curious about applicants and veterans alike, and embed specific ways to understand not just their knowledge, skills and personality types, but what motivates them, what drives them to commit, and how they learn and grow.
Get serious about capacity not just competencies: organizations are getting much better at defining the competencies necessary to perform roles, both hard and soft skills. But there is a deeper dimension.
If competencies are like software or an app, capacity is the bigger operating system. There’s been a lot of conversation about competency and capacity in leadership literature, but it’s a true dynamic for every person. When you hire and recruit, and especially when you think about how to develop a human within your workforce, skills and competencies matter, but what is their capacity? What possibilities exist for this person and their learning and development? Their use of existing skills? Where do they exceed the algorithm (and everyone does in some way) of the role or position? Capacity is a powerful resource that every single person can use to dramatically impact organizations and missions. Unleash it.
Establish metrics to measure opportunity and access: organizations that prioritize potential and allow room for outliers measure that intention. How many outliers have made it through the cut? Are a variety of backgrounds bubbling to the top? What better questions could be asked? How many career shifts has the company allowed its people to make? How did it go? Measuring what matters means setting some goal posts.
Ultimately, artificial intelligence will benefit our organizations if we keep a priority on authentic learning. Like every other intelligence, how we deploy it and match it to the right questions and problems is a competitive choice. And when it comes to people, allow AI to take its lead from real-life inspiration, first.
