Adding AI to Workflows

AI as a Workflow Component

AI doesn't replace workflows — it augments them. The best workflow automations use AI for the parts that require judgment or language understanding, and deterministic logic for everything else.

Pattern: structured input → AI processing → structured output → downstream action

The key is keeping AI's input and output structured. Don't give AI free-form inputs or accept free-form outputs — it makes the rest of the workflow unpredictable.

Common AI Workflow Patterns

Classification — route incoming items based on AI-determined category.

Inbound email → AI: "Classify as sales/support/spam" → Route to appropriate queue

Enrichment — add AI-generated context to existing data.

New lead → AI: "Generate company summary from website" → Append to CRM record

Generation — produce content based on structured inputs.

Event triggers → AI: "Generate status update from metrics" → Post to Slack

Extraction — pull structured data from unstructured content.

Contract PDF → AI: "Extract dates, parties, and obligations" → Store in database

The n8n AI Node

n8n includes built-in AI nodes: OpenAI Chat, Anthropic, and a generic LLM node. Configure with your API key and model selection.

Use the Code node for complex prompt construction — it's easier than n8n's expression editor for multi-line prompts.

Error Handling for AI Steps

AI calls fail (timeouts, rate limits, model errors). Always:

1. Add retry logic (3 attempts, exponential backoff)
2. Set reasonable timeouts (30s for most tasks)
3. Define a fallback path for when AI is unavailable
4. Validate AI outputs before passing downstream