Machines as colleagues

The digital world surrounds us. We take it for granted that computers, and their output, are a part of our everyday lives. Most of the time these machines are programmed to perform roles and functions dictated by traditional software design. Think of ever increasingly complex “if X then Y” type of work. However, over the past fifty years, computational capacity has changed. We are now in a new digital world, one in which computers are rapidly expanding in their capacity to adapt and develop new “intelligence.” The capacity for this has generally been termed “artificial intelligence.”

Artificial intelligence (AI) has been defined "as a system's ability to correctly interpret external data, to learn from such data, and to use those learnings to achieve specific goals and tasks through flexible adaptation.” (Kaplan & Haenlein, 2018).

I sat down to interview Dr. Michael Hemenway to discuss how AI might impact leadership in organizations (Groler Podcast episode four). Through our conversation, one big idea emerged: in the world of AI, we need to consider how this new generation of machines moves from being merely tools toward being colleagues.

Artificial intelligence represents a diverse new set of views. As readers are aware, gaining a diversity of thought and perspective has been an enormous issue for management and leadership for thousands of years. “Groupthink” triumphs when teams do not have enough variety to question the status quo. Traditionally, the solution has solely been in recruiting and retaining diverse human talent. While this is still the case, perhaps we need to also think of AI as being a valued colleague capable of a distinct perspective.

However, how do we do this?

Start by remembering that we do learn from machines. Contemporary AI and machine learning platforms are not merely advanced calculators, but rather provide new thought, perspective, and creativities.

Consider this learning unique to what we may gain from a fellow human. In considering our AI technologies as colleagues, we need to speak their language and pay careful attention to the human-machine interface. This goes beyond merely understanding programming languages to being reflective about the way we translate what machines are telling us. Hemenway articulated that it is easy to assume machines “think” the same way we do. However, in reality, their processes and methods are distinct from our own.

Build a team that has the technical, psychological and cultural capacity to engage this type of difference (25:07 of the interview). The days of having solely technically-oriented people handling technology are over. Having a wide range of capacities that can engage these interfaces is critical.

AI is not merely a tool for greater efficiency or more powerful analytic capacities. It is a new creative force in organizations. Like all change, leaders must adapt to this new reality and seriously consider how this changes our work.

[Image by Geralt via Pixabay]