Leading Machines
The dreaded status report is dead, and I, for one, am not mourning its passing. As we move into 2026, the "wasteful cycle" of repeating the news up the food chain is being replaced by automated intelligence, finally allowing managers to stop acting as message relays and start acting as leaders. If you are or want to be a servant leader, these are the best of times. Most of our teams' obstacles can be traced to broken systems and lossy data that hinder good decision-making, and these problems are more solvable now than ever. As AI moves from experimental pilots to full-scale implementation, we can finally build tools to automate mundane tasks and let the base data of work progress speak for itself.
This reduction of routine administrative "noise" is intended to liberate us, reclaiming hours each week for coaching and strategy. However, this does not necessarily mean a lighter workload. The nature of management is becoming more sophisticated, requiring a "human-in-the-loop" approach where leaders must supervise AI performance and validate its output. We are finding ourselves in a position of "dual management," figuring out how to balance the demands of human teams alongside these new automated agents on the fly. While the technical hurdles are largely cleared, the primary challenge now lies in navigating the human and organizational shifts required to become a truly data-driven workplace.
There is, however, a cost to this efficiency. The machine has become the elephant in the room, mediating nearly every interaction. I’ve found that the essential human connection between a manager and their team can easily become clouded by the presence and relentless output of the machine. It takes a significant, conscious effort to trudge through that digital layer to keep personal relationships vital. As we redesign workflows, we must be intentional about identifying which human skills, like context-setting and empathy, become more valuable as the AI handles the operational heavy lifting.
This reduction of routine administrative "noise" is intended to liberate us, reclaiming hours each week for coaching and strategy. However, this does not necessarily mean a lighter workload. The nature of management is becoming more sophisticated, requiring a "human-in-the-loop" approach where leaders must supervise AI performance and validate its output. We are finding ourselves in a position of "dual management," figuring out how to balance the demands of human teams alongside these new automated agents on the fly. While the technical hurdles are largely cleared, the primary challenge now lies in navigating the human and organizational shifts required to become a truly data-driven workplace.
There is, however, a cost to this efficiency. The machine has become the elephant in the room, mediating nearly every interaction. I’ve found that the essential human connection between a manager and their team can easily become clouded by the presence and relentless output of the machine. It takes a significant, conscious effort to trudge through that digital layer to keep personal relationships vital. As we redesign workflows, we must be intentional about identifying which human skills, like context-setting and empathy, become more valuable as the AI handles the operational heavy lifting.