Enterprise teams are caught between two paradigms: traditional rule-based automation (RPA, workflow engines, scheduled scripts) and the new wave of AI agents. The answer isn't one or the other — it's knowing when to use each.
Traditional Automation: Strengths
Rule-based automation excels at predictable, high-volume tasks: data validation, scheduled reports, status updates, and simple if-then routing. It's fast, deterministic, and easy to audit.
AI Agents: Strengths
AI agents shine when tasks require judgment: interpreting unstructured data, handling exceptions, generating insights from patterns, and adapting to novel situations. They can read context, reason about edge cases, and take actions that weren't explicitly programmed.
The Hybrid Approach
The most effective enterprise systems combine both. Use traditional automation for the 80% of predictable work, and AI agents for the 20% that requires reasoning. For example:
- Traditional: Route standard purchase orders through approval flows based on amount thresholds
- AI Agent: Flag unusual purchase patterns, suggest better vendors, and draft exception justifications
- Traditional: Send automated SLA breach notifications
- AI Agent: Analyze why SLAs are being breached and recommend process improvements
How INFORMAT Combines Both
INFORMAT's workflow engine handles deterministic automations, while AI agents operate alongside them — monitoring data, answering questions, and executing tasks that require business context. Both share the same data layer, so there's no integration gap.
Getting Started
Start with traditional workflows for your core processes. Then add AI agents to handle the exceptions, insights, and complex decisions that rule-based systems can't handle well.