AI Automation for Internal Tools: Where to Start

The hype vs the reality

LLMs are very good at specific, bounded tasks that previously required expensive human attention. The best starting points are the repetitive, high-volume, low-stakes tasks that currently absorb time without delivering much value.

Pattern 1: Internal knowledge retrieval (RAG)

Retrieval-Augmented Generation (RAG) connects an LLM to your internal docs.

Good fit for: onboarding assistants, internal policy Q&A, product knowledge bases

Pattern 2: Document processing pipelines

The LLM reads the document, extracts fields, and outputs structured JSON.

Good fit for: invoice extraction, contract clause identification, support ticket routing

Pattern 3: Support triage and response drafting

The LLM drafts, a human approves before sending.

Good fit for: first-response drafting, ticket classification, FAQ deflection

Pattern 4: Workflow agents

Agents combine LLM reasoning with tool use — calling APIs, reading databases.

Good fit for: multi-step ops workflows with clear success criteria

How to start

1. Identify one high-volume, repetitive task
2. Define a clear input/output format and success criteria
3. Build a prototype and measure accuracy
4. Add a human review step for edge cases
5. Measure time saved vs. errors introduced before scaling