AI workflow automation combines language models with traditional automation tools (APIs, RPA, databases) to execute multi-step business processes end-to-end. Where RPA handles structured, rule-based steps, AI handles the unstructured parts: reading natural-language inputs, making judgment calls, generating text outputs, and routing based on content.
High-value targets: document processing pipelines (ingest → extract → classify → route), customer support triage (read ticket → categorize → look up KB → draft reply → escalate if needed), and data enrichment (fetch → summarize → write to CRM).
The implementation pattern that works in production: identify the 20% of cases that are edge cases, build automated handling for the 80% of common cases, and route the 20% to humans. Start with a high human-in-the-loop ratio and reduce it only as confidence builds.
Bring this to your business
Knowing the term is one thing. Shipping it is another.
We do two-week AI Sprints — one term, one workflow, into production by Day 10.