Two approaches to automation
Rule-based automation and AI automation are often presented as alternatives. In practice, they are points on a spectrum. Rule-based automation handles what can be explicitly defined. AI automation handles what requires interpretation. Most effective automation uses both.
Understanding which approach fits which process helps businesses avoid two common mistakes: using AI for processes that could be handled more simply with rules, and trying to use rules for processes that require the interpretive capability that only AI provides.
What rule-based automation does
Rule-based automation follows explicit instructions. If this condition is met, take that action. If this data matches this pattern, route it there. The rules are defined in advance and the automation applies them consistently.
Rule-based automation is appropriate when:
- The process is well understood and all paths can be defined
- The inputs are structured and predictable
- The decisions are binary rather than interpretive
- Variation is limited and can be explicitly handled
Examples include: routing invoices based on amount, sending reminders based on dates, categorising transactions based on account codes.
What AI automation adds
AI automation handles what rules cannot: interpretation, context and variation. Instead of following explicit instructions, an AI assistant understands what something means and decides what to do based on context.
AI automation is needed when:
- The inputs are unstructured — emails, documents, conversations
- The decisions require interpretation rather than simple matching
- Variation is significant and cannot be reduced to explicit rules
- Context matters — the same input might require different handling based on the situation
Examples include: understanding what a customer enquiry is about, extracting relevant information from a contract, determining the appropriate response based on customer history.
How they work together
The most effective automation combines both approaches:
- Rule-based automation handles the structured, predictable parts of a process — the routing, the data validation, the status updates.
- AI automation handles the interpretive parts — understanding inputs, making context-dependent decisions, handling the variation that rules cannot capture.
A workflow might use AI to understand an incoming email, determine it is a contract amendment and extract the relevant changes. Rules then handle the routing based on contract value, the approval path based on the type of change and the system updates based on the approval outcome.
Choosing the right approach
The choice between rule-based and AI automation — or, more commonly, the right combination of both — depends on the process:
- If the process is structured, the inputs are predictable and the decisions are binary, rule-based automation is likely sufficient and will be simpler to build and maintain.
- If the process involves unstructured inputs, interpretive decisions or significant variation, AI automation is necessary to handle what rules cannot.
- Most real business processes sit somewhere in between and benefit from both.
For a related comparison that focuses on the technology vendors rather than the approach, see AI automation vs RPA.
For a foundational overview of where these approaches fit into business operations, see what is workflow automation.
Moonshot Monkeys builds automation that combines rule-based and AI approaches appropriate to each process. If you are unsure which approach fits your needs, we can help assess your processes and recommend the right combination.