Many organizations implementing AI agents tend to focus too narrowly on a single decision-making model, falling into the trap of assuming a one-size-fits-all decision-making framework, one that follows a typical sequence in any circumstance: from input to research and analysis toward decision, then execution, eventual evaluation, and hopefully, lessons learned. However, it oversimplifies reality.

Currently, the performance of AI systems is predominantly—and in some cases, exclusively—developed and praised for their capacity to enable autonomous operations, often at the expense of fostering collaboration among humans, thereby not augmenting but diminishing human intelligence.

Artificial Intelligence allegiance lies in the code we have crafted. This is both its strength and its peril. AI as we know it is mimicking a form of intelligence but hollow—it lacks a moral core. Indeed, for decades, we’ve conceived and trained machines to calculate outcomes, not to uphold principles. Leadership in the next era demands that we fix this.