Old ways of leadership won’t work in the age of AI. Change these 5 things
The first time an AI system made a recommendation I disagreed with, I felt something I didn’t expect: ego. It was a resource allocation call on a complex project. The model had crunched variables no person could hold in their head at once, and its recommendation was better than mine. Something in me didn’t want to let a model make that call. So, I made it myself. I told this story to Michael…
An AI system once made a recommendation that the reporter disagreed with, leaving them feeling surprised. This experience led the reporter to discuss the nature of leadership with Michael Jabbour, AI innovation officer at Microsoft. Jabbour emphasized that new AI tools change who gets to decide, rather than what gets decided. The reporter has also spoken with experts like Avantika Sharma, global head of healthcare at Brillio, an enterprise AI company.
The common theme they've heard is that leadership is shifting from directing people to designing systems that facilitate human-machine collaboration.
The reporter has implemented several changes in their own leadership approach:
1. They've stopped managing tasks and started creating decision systems. AI can process information and flag risks faster than humans, so the focus should be on controlling how those insights are used. The reporter now defines guardrails, such as compliance, data governance, transparency, and operational reliability, while leaving design decisions open to intelligent systems.
2. They've rethought the org chart. Traditional hierarchies based on positional authority no longer work when AI systems generate insights that coordinate work across functions. Instead, Sharma advocates for clear paths for escalation and intervention, with accountability remaining with human leaders.
3. They've separated speed from responsibility. AI can process information and identify patterns much faster than humans, but accountability for judgment and validation still belongs to a person. The reporter now clearly designates roles for watching outputs and owning any actions taken based on AI recommendations.
4. They ask what the system is optimizing for. AI learns from the data it is fed and adapts over time, which raises questions about what assumptions and values the system is encoding. The reporter considers the potential trade-offs between efficiency and human nuance, nuance which is often discarded due to its difficulty in measurement. They evaluate AI tools by examining the incentives they promote and whether they push for speed at the cost of depth, quality, or relationships.
5. Finally, they define what has to stay human. Some decisions, like those affecting a person's career or medical care, cannot be automated. Leadership means showing up in person and providing human guidance in these critical situations. The reporter's new rule is to automate analysis, not accountability.
Written by urgent.news from Fast Company's reporting — not their text. Machine-written — may contain errors; check the original before relying on it.
