I recently attended a training by David Caruso, one of the preeminent researchers of Emotional Intelligence and co-author of the highly regarded MSCEIT (Mayer-Salovey-Caruso Emotional Intelligence Test) assessment instrument. In the training, he discussed the development of the MSCEIT® 2 instrument and why the ability model of emotional intelligence is important to leadership development.
Some people consider emotional intelligence to be a personality trait, which would make it difficult to change. But if you look at the abilities required to be emotionally intelligent, they can be learned—and most of us could use some growth in managing our emotions and connecting with others. I often help leaders develop skills in emotional intelligence. It is essential for leaders to be able to navigate emotionally charged situations and figure people’s feelings into business decisions. Leaders who make the effort to build their emotional intelligence can round out their leadership capability.
This brings me to leaders of AI companies.
In addition to leading people, it is particularly important for AI leaders to be emotionally competent so they can understand how the AI products they are creating impact the people who use them. I would go beyond saying emotional competence is an essential skill for AI leaders to say it is a critical component of an AI organization’s responsibility to its shareholders, customers, employees, and the world at large. As AI applications proliferate, the world is depending on AI leaders to create products that are good for humans, not harmful.
Whether we like it or not, we have emotional reactions to AI. We anthropomorphize it, we are persuaded by it, we fear it, we become addicted to it. LLMs (large language models, like ChatGPT) have emotional components built into their algorithms both to appeal to users and to optimize their usage of the products. AI-driven tools by their very design use “optimization metrics” to train the algorithms and ensure they are providing the desired outcomes. The developers or purchasers of the tools choose which metrics to use for their optimization.
The most highly publicized optimization metrics are the quantity of time using the product and the quantity or quality of engagement with the product. For example, these optimization metrics are used in social media apps for the AI algorithms to learn how to draw users in and engage with the apps so that advertisers can earn money from displaying ads and learning more about how to nudge users to buy their products across other apps.
Companies are also employing AI-driven tools to optimize employee performance. Similar to social media apps, workplace tools measure the quantity of time employees spend working and the quantity or quality of work produced. Just as managers need to learn how to motivate employees, hold them accountable, improve their performance, and facilitate collaboration in legal and ethical ways, AI-driven workplace tools need to be held to the same standards. This extends beyond the workplace to tools of all sorts.
If leaders of AI companies don’t understand the human components of work and life, they won’t be able to lead their products to do it.
Emotions are part of the human condition. It makes us wonderful creatures, as well as confusing, frustrating, and sometimes even difficult. Perceiving feelings and understanding them helps us connect with others and manage reactions to situations. Understanding emotions helps us plan and make decisions. It also helps us build better tools for humans. As David Caruso said in his training, emotional intelligence is comprised of abilities that we can learn and develop.
In my experience working with technical leaders, I find that they are often better at intellectually understanding emotions (and dismissing them!) than perceiving and managing them. The leaders who invest in developing their skills improve their leadership effectiveness and bring a whole new level of empathy to their approach. The new level of empathy helps them understand and connect with people far beyond their teams, which is exactly what organizational leaders need.
Emotionally intelligent leaders of AI companies are well positioned to lead the development of emotionally intelligent artificial intelligence and the teams to produce it.
