Many organizations, including the business school where we teach, are rapidly adopting artificial intelligence. However, in the rush to build this capability, a quieter risk is emerging: the erosion of human judgment and accountability.
The Temptation to Delegate
Because AI can generate answers faster than we can fully assess them, we may be tempted to rely on its recommendations without remaining fully engaged in the judgment that consequential decisions require. The danger of AI is not simply that it may become highly capable; it is that we may quietly cede human judgment and authority to machines because doing so is efficient or convenient.
Real-World Consequences
Decisions that shape people's lives are increasingly influenced or made by AI systems. Job applicants are screened by algorithms. Mortgage approvals are determined by automated risk models. Hospitals use AI tools to prioritize care and allocate scarce resources. The promise is compelling: greater efficiency, faster processing, and more consistent outcomes. But beneath that promise lies a critical question: When a decision has real consequences, who is accountable?
If a job application is rejected, a loan denied, or a patient deprioritized, where does responsibility sit? Is it with the organization that deployed the system? The individual who relied on it? Or does it dissolve into the technology itself?
Instrumental vs. Agentic AI
Instrumental decisions, such as a thermostat turning on a furnace at a set temperature, have long been automated within predetermined human preferences. But agentic AI systems, like self-driving cars, are designed to make judgment calls over consequential, even life-and-death matters. When rules are clear and the situation fits those rules, a machine can apply them. However, decisions involving trade-offs, moral ambiguity, or critical thinking require deeper judgment.
Leadership begins when the decision situation is not defined by simple rules, and someone must exercise judgment and stand behind the result. A person or institution can delegate instrumental choices to a machine and remain accountable, because they define the objective, set the decision rules, and choose to deploy the system. But when the decision rules run out, responsibility remains with the authorized person or institution. It cannot be shifted to a machine that cannot understand broader implications or stand behind the result.
Why Delegate?
Why do we see consequential decision-making delegated to machines? Often, it is the result of optimizing efficiency or other legitimate objectives. Because modern AI systems execute instrumental decisions much faster and often more reliably than humans in certain contexts, they are given increasing authority. However, trust erodes when judgment over decisions that affect society is delegated to an entity that cannot participate in defined accountability.
Leaders should know when to rely on AI, when to question it, and when to override it. They must be able to justify the decisions they make and stand behind the consequences. As organizations embrace AI, preparing leaders to take responsibility is not optional; it is essential.
