AI automation
AI automation: Automation in which AI models perform judgement steps (classifying, extracting, drafting, routing) inside a workflow that also uses deterministic rules and a human review path.
Why it matters
The distinction from older automation is that AI automation can handle unstructured inputs such as emails, PDFs and free-text forms, and steps that need judgement. Good AI automation still uses ordinary code for anything deterministic, adds confidence thresholds, and routes uncertain cases to a human queue rather than guessing.
Related terms
- Robotic process automation (RPA): Software robots that repeat fixed steps by operating user interfaces as a person would. Best for legacy systems without APIs; brittle when interfaces change.
- Workflow automation: Connecting applications through APIs with predefined triggers and actions, as in tools like Zapier, Make or n8n.
- Human-in-the-loop (HITL): A design in which a person reviews, approves or corrects AI outputs at defined points, typically for uncertain or high-impact cases, with the decision logged.
Where it comes up
- What is the difference between AI automation and RPA?
- When is an AI automation agency worth hiring?
- What is Sudo Sapient?
- What services does Sudo Sapient offer?
- AI Automation vs RPA vs Workflow Tools: Which Does Your Process Need?
- How to Measure AI ROI Without Fooling Yourself
- Agentic AI for Business: What It Is and What Actually Works
In practice
In a Sudo Sapient sprint, this sits inside AI Automation: one operational workflow taken from manual to automated in 6–8 weeks, with human review on uncertain cases. See the services page on sudosapient.com.