What Impact Will the AI Revolution Have on Executive Selection?

When it comes to identifying and selecting talent, leaders now have more tools at their disposal than ever before. Until recently, the main ways we assessed people were through the use of CVs, interviews, and if lucky, a psychometric survey. Putting aside the debate around the accuracy and utility of such tools, they all share something in common—they rely on human judgment. When it comes to identifying the right person for the role, human judgment can really get in the way:

1. We are prone to cognitive biases, heuristics, and assumptions. As described by the Dunning-Kruger effect, the more an individual declares themselves to be a good judge of talent, the less likely that is to be true.
2. Of those people that are good judges of talent, they can’t be scaled to match the demand or complexity of the modern economy.
3. We all struggle to make sense of complex sources of data and make accurate predictions about the future. In our case, one’s potential to thrive at work.

Fortunately, talent identification is undergoing a revolution. This is a result of the democratization and consumerization of Artificial Intelligence and related technologies, making it easier to train and implement predictive models and algorithms within HR and leadership practice. For example, there are numerous solutions on the market that use some form of AI to improve our talent decisions: using games to measure decision-making preferences and cognitive ability, social media and linguistic scrapers, and video interviews to mine prosody and body language. Such tools enable practitioners to accurately identify talent at scale, in a speedy fashion, and free of bias. So, while leaders of organizations are using these tools to identify and staff talent for junior or middle-management roles, how can such innovations be used to build a strong bench of senior and executive leadership?

Before I answer this question, I want to make a few things clear. First, I do not think leaders will ever, nor should, be selected solely on the basis of an AI tool. Second, I do not see a future where leadership consultants will be replaced by an algorithm. But as the adage goes, you can’t manage what you can’t measure. Not leveraging AI tools or novel sources of data leaves considerable value on the table—ultimately reducing our ability to help our clients and their organizations. With that said, the AI revolution—bringing technology, data, and prediction—will have a positive impact on leadership assessment in three ways:

Identify the talent signals that matter at scale.

Leadership assessment is rightfully “high-touch” with a candidate spending a considerable amount of time with a practitioner as they explore past experiences, expertise, and relative strengths and weaknesses. While such an approach is entirely appropriate, the talent identification trends that are happening in lower parts of the organization can give us a strong sense of how we can enhance the way we assess current and future leaders.

Most leadership assessments currently rely on in-depth interviews 360-assessments and long psychometric instruments. While these tools are scientifically validated and intuitive, it can be argued that they do not capture all the necessary talent signals, nor can they be truly scaled to meet the demand of the multinational firms and “VUCA” environments. In the not too distant future, we will be leveraging AI tools to further enhance our understanding due to their ability to process rich and voluminous data that we have traditionally been unable to collect or understand. Their automatic nature will also enable us to assess such talent at scale. We can already see examples of this as there are solutions that can deduce talent signals from the words we write, the things we say, and our digital footprint. Such tools will not replace human decision-making, rather, they will make the process of assessment and prediction significantly cheaper while ultimately producing insights that significantly improve human judgment.

Increase demographic and cognitive diversity.

Many organizations are proactively trying to increase minority representation and foster more inclusive work environments. Nonetheless, organizations still have a long way to go, as 70% of senior executives at Fortune 500 companies are white men. Similarly, there is a growing cause for an increase in cognitive or “deep” diversity amongst the leadership. Cognitive diversity describes variation in the ways people think, feel, and act, as well as how such differences positively interact within group contexts. Groups that have cognitive diversity are more likely to view problems differently, produce better decisions, and have complementary dispositions.

Here, AI tools can have a significant impact, as, unlike humans, they can be trained to be “blind” to demographic factors and instead focus on identifying the characteristics, qualities, and skills that are actually required to become a successful leader. Sure, AI assessments have been found to display forms of bias in talent selection, yet we should ask ourselves whether such tools are less biased than human raters (the answer is yes) and whether the issue will be resolved as the technologies mature (there is no reason to believe otherwise).

Support continuous development and self-awareness.

Without knowing where one’s leadership strengths and limitations are, any effort to create lasting behavior change is futile. The key to leadership development, therefore, starts with self-awareness, or as Socrates said, “know thyself”. Data-driven tools, that leverage technologies such as ChatBots, wearable devices, and AI-powered assistants such as those that currently live in our pockets (i.e. think Siri), have the potential to not only raise our self-awareness but also provide us with personalized and real-time feedback on how one can become a better leader. Think I’m crazy? Take a look at the quantified-self movement, and the huge adoption of fitness trackers and smartwatches to improve both our physical and psychological health. “Always-on” technologies that can mine our behaviors, and then provide us instant feedback and practical advice on how we can modify our behavior, will close the gap between leadership assessment and development as distinct practices. Ultimately improving the effectiveness of both.

To quote Ajay Agrawal, author of Prediction Machines, “Artificial Intelligence does not actually bring us intelligence but instead a critical component of intelligence—prediction.” As such, without good predictions, we cannot make good judgments. AI will not replace humans (nor consultants), it will only make us better.

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