Overview
AI workflow automation puts model calls inside an orchestrated process. Traditional automation handles the deterministic steps — triggers, branching, retries — while the AI handles classification, extraction, drafting and the judgement calls in between.
How it works
A trigger fires, data moves through a sequence of steps, and AI steps classify, extract or generate at the points where fixed rules cannot cope. Conditional branches, retries and human approval gates keep the process reliable.
Use cases
Inbound lead routing
Classify and enrich inbound enquiries, then route them to the right owner.
SalesInvoice processing
Read, validate and post supplier invoices with exceptions sent to a human.
FinanceContent pipelines
Draft, review and schedule content across channels on a fixed cadence.
MarketingTicket triage
Categorise, prioritise and assign support tickets as they arrive.
SupportBenefits
- Removes repetitive handoffs between systems.
- Handles unstructured input that rules engines choke on.
- Runs continuously without staffing.
- Leaves an auditable record of every run.
Limitations
- A wrong AI decision propagates silently unless you monitor for it.
- API and schema changes break flows without warning.
- End-to-end debugging across many steps is genuinely harder.
- Cost scales with volume in ways that surprise people.
What to look for when choosing a tool
- Connectors for the systems you actually use
- Human approval gates on consequential steps
- Retry, error-handling and alerting behaviour
- Run history and audit logging
- Pricing model at your real monthly volume