Leading well with artificial intelligence

AI is no longer a futuristic concept. It is already an integral part of our lives and has been so for many years. From financial services, to manufacturing, to medical diagnosis, AI is helping businesses get things done. In our personal lives, AI resides in our mobile devices, in social media feeds, in household appliances … the list is endless. Consciously or not, AI and humans are already close partners.

All successful partnerships require a ceding of control and trust between collaborators. Our relationship with technology is no different; working with AI means surrendering control to machines. By extension, that also means trusting those who provide the AI tools.

In my previous article, I explored the interplay between AI and leadership, stressing that leaders have ultimate agency in deciding what to hand over to technology. In this article, I discuss some suggestions for how this agency might be exercised effectively. But first, a few points on some features of AI and leadership that, for me at least, makes this discussion interesting:

• AI simplifies things: AI simplifies complex tasks by automating processes, analysing datasets quickly, and offering insights that enhance decision-making. It streamlines workflows and saves time. Tasks like customer service, financial forecasting, and content creation are made easier. But is it always better though?  

• Simplification has its downsides: Over-reliance on AI and automation erodes critical thinking and creativity skills. Generative AI, in particular, lures us by making hard thinking seem straightforward. However, as the writer Yuval Noah Harari points out in his latest book Nexus, people may like things simple, but the truth is usually complex.

• AI does not feel: AI lacks emotions, empathy, and intuition, which are integral to human decision-making and leadership. While AI can analyse data and predict outcomes accurately, it does less well with moral dilemmas or the subtleties of human relationships. This absence of feeling means AI might optimise for efficiency at the expense of fairness, creating solutions that lack “the human touch.”

• Feelings are difficult things to manage: Many leaders struggle with handling emotions, both their own and those of others. This is perfectly understandable as dealing with feelings can be awkward. The allure of AI lies partly in its emotionless precision. Yet, this can be a pitfall. Suppressing or ignoring emotions can lead to disengaged teams, unresolved conflicts, and decisions that compromise ethics.

Taken together, the four points above combine into what I cheekily call an “Emotionless Simplicity Loop.” People like things simple -> AI promises simplicity -> People suck at feelings -> AI has no feelings. To some leaders, this loop may appear a Godsend, for it allows work to be done without the drama of tempers. And it can be, if applied to the right decisions in the right ways. We must, however, not let the Emotionless Simplicity Loop lead us into a vicious circle of automation that harms businesses and teams.

What then are some strategies that leaders could adopt to avoid pitfalls in automation? Paradoxically, when it comes to working well with technological advances, I think the answer lies in going back to some basics. I offer four points for discussion.

Leading Self

• Deepen your domain expertise: As business environments grow more complex, the adoption of AI will add more intricacy. To leverage technology effectively, leaders must know their businesses. Here, mastering every technical detail would be overkill. Knowing what to interrogate is key. Example: in a retail setting, a leader might ask, “How does this AI tool recommend inventory levels? What data is it using? How is that data collected and processed?”

• Learn through hands-on AI projects: Leaders should identify specific challenges within their organisations and personally lead teams to solve them using AI. Whether it is streamlining customer service or improving supply chain efficiency, dive into the details. Question the assumptions behind the AI model to better understand it. Example: a leader in manufacturing could run AI projects on predictive maintenance for plant equipment, leveraging on her native technical expertise to learn what AI can or cannot do in her operations.

Leading Teams

• Encourage teams to visualise AI as a teammate and to help train it: Leaders should position AI as a collaborative partner in their teams instead of a passive tool. One way is to suggest that teammates imagine AI as a team member – let’s call it “AI Alex.” Invite everyone to picture AI Alex sitting at the meeting room table with everyone. Facilitate a discussion on how tasks should be divided between human teammates and AI Alex, taking into account the latter’s strengths (data analysis) and limitations (lack of intuition). Encourage team members to properly onboard and train AI Alex, just as they would any new human teammate. This helps foster a collaborative mindset and ensures human teammates retain their creative and strategic roles while delegating the appropriate tasks to AI Alex.

• Utilise team expertise: The readiness to adopt AI varies across industries and businesses. Within a company, engineers and IT professionals might find it easier to embrace AI compared to, say, the HR department, which relies more on the human touch. Leaders should acknowledge their teams’ inherent skills and biases. Use this understanding to identify AI champions who can spearhead the transformation process. Here's an example from real life: The CEO of a global airline quickly recognised the enthusiasm of his engineering team when experimenting with new technology. He leveraged their natural skills and inclinations by allowing them to lead AI adoption, from identifying problems to developing solutions and implementing AI.

The suggestions above are derived from my personal experience in leading digital transformation projects. Out of curiosity, I also fed ChatGPT the first part of this article and asked it for four suggestions on leading self and teams. ChatGPT’s response is attached below. It reads predictably well, albeit too theoretical for my liking. But I guess that is what makes the theoretical ChatGPT a useful partner for the practical me?

ChatGPT’s Theory

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As a leadership consultant and coach, I help organisations and individuals use good leadership practices to succeed. Email me to discuss how we can work together.