There’s a growing pressure to turn every process into an AI agent. Probably because the technology is new and hot—but that is not the right approach for everything. Using AI should be strategic. It should not be treated as a silver bullet that can be thrown at every organizational problem.
We already have different solutions for different problems:
- Humans — for high-stakes decisions
- Rule-based systems — when the logic is known and explicit
- Machine learning — for predictions
- GenAI — for unstructured inputs that need interpretation
Each comes with its own trade-offs in accuracy, cost, complexity, and risk.
Take employee expense approvals as an example. If the rules are clear and stable (e.g., receipts under a fixed limit with standard categories), a rule-based system is usually enough. If the receipts are messy photos or free-text descriptions, GenAI can help extract and categorize the data. But turning the whole process into an autonomous AI agent that “decides” what to approve or reject often adds cost, risk, and complexity for little extra value.
The real decision is about matching the right tool to the task—not treating an AI agent as the ultimate goal of every system. Some tasks that are currently done by humans can (and should) move to a rule-based system, machine learning, or GenAI when those tools fit better. But there is no obligation to follow this progression from human → rules → ML → GenAI → agent. In many cases the best next step is simply to leave the task with a human—or to stop at rules. Jumping all the way to an agentic solution just because it is the latest trend is rarely the right answer.
In the end, one of the most valuable AI skills is knowing when not to use AI.