Tory Clarke, Co-Founder & Partner, Bridge Partners.
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Artificial intelligence (AI) has seemingly made inroads into every area of our professional lives, often in beneficial ways. Across industries and organizations, leaders are discussing ways to leverage this technology for greater efficiency. Rightfully so, given its powerful and transformative potential in many areas of business today. However, when it comes to intentional, inclusive executive search and hiring, I believe emotional intelligence carries more weight than artificial intelligence when choosing a leader. With all its promise and hype, AI is simply not ready for that task.
Let me start by clarifying that an intentional and inclusive search, by definition, excludes no one. Instead, it purposefully includes those otherwise qualified individuals who may not be considered for a leadership role due to bias or other limiting factors—for example, not coming from the "right" school. The result of an inclusive process is that the best talent is uncovered, period.
Twenty years ago, when I co-founded Bridge Partners to expand executive search beyond the typical candidate pool, corporate America did not have equitable hiring practices and strategies (clearly this work is still in progress). We expected our business would become obsolete within five years, anticipating that inclusive hiring would become the norm. Fast-forward to today and we are still here, and there is still an incredible amount of unconscious bias in C-level executive search.
In today's recruiting environment where companies desire greater diversity in their workforces, we see an increasing number of organizations contemplating the use of AI to make hiring decisions. Indeed, according to PwC's 2022 AI Business Survey, 46% of companies are already using AI technology to make decisions about workforce and DEI initiatives. Yet I strongly believe that AI in its current form is simply too immature to be used as a determining factor or as a substitute for human judgment—especially when choosing a leader. There are two key reasons for this.
Human Bias Gets Baked Into AI
We have seen preexisting human bias baked into algorithms before. For instance, biased algorithms in facial- and voice-recognition AI code led to platforms that were incapable of analyzing darker skin tones and virtual assistants that were unable to understand the many different accents and dialects that exist around the world.
Accordingly, it is logical to assume that any AI used in hiring practices will be similarly susceptible to the biases of its human trainers. While it may be tempting for organizations to seek out a shortcut by using AI to select leaders, there is a real danger of baking in recurring patterns and bias, which will perpetuate the cycle of the same names being proposed as candidates.
AI is not fully formed, and it must learn from trial, error and refinement before it can be leveraged beneficially in this area. Can we afford that trial-and-error period when selecting leaders?
Bias can seep in through both the human programmers and the underlying data sets that populate its programming. For example, a company may have a long, successful history of recruiting from Ivy Leagues schools. If hiring history data is used to program an AI, the new tool may assume that such credentials are the baseline for hiring (which we know has not historically been a path of inclusivity).
Simply put, AI cannot solve the problem that humans have yet to solve—evidenced by the fact that, 20 years later, there remains a need for a team like ours. We constantly check ourselves to ensure our approach remains inclusive, including two partners on every executive search to avoid bias in the interview process. Personality, leadership style, attitude and energy are all important factors to consider, and in a truly intentional and inclusive executive search, there are no quick, automated solutions.
AI's Lack Of EQ Disqualifies It From Current Use In Executive Search
Proponents of AI would argue that a diverse team of experts can train the AI platform to minimize or entirely remove all bias. But even if that was somehow achievable, AI's current capability to “learn EQ” is not sufficient to help in a leadership search at this point in time. When seeking a company's senior-most leaders, hiring managers need to rely on their emotional intelligence to get a read on the candidate's personality and leadership style and whether they will be additive to the culture of the rest of the team. Until AI can successfully demonstrate a level of EQ rivaling a seasoned human search professional, I am skeptical of its ultimate efficacy in the process.
Without this critical EQ element, the danger is that AI becomes a blunt instrument to simply increase "diversity" when hiring executives, without the combined end goal of an equitable and inclusive process that ultimately also hires great leaders for a specific organization. Meaning, AI could be used to simply target certain characteristics, making DEI efforts a box-checking exercise. Companies seek diversity in their leadership ranks in large part because they want the benefits that broad and varied perspectives and skill sets bring—more creativity and innovation, less group thinking. Clearly, box-checking does not help in achieving those goals.
Of course, the AI landscape evolves and improves daily. There are many experts and maestros who believe themselves irreplaceable (just ask Drake and The Weeknd) and the executive search industry is not immune—nor entirely objective on this topic. Maybe in the future, there will be a mature AI solution that can handle the complex job of seeking out and assessing the optimal leader for a specific nuanced culture and point in time—but not yet.
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