AI Automation Workflow
AI Automation Workflow
AI Automation Workflow

AI automation does not fail because models are weak.

It fails because workflows are poorly designed.

Designing an AI automation workflow is not about
chaining tools together. 
It is about deciding where intelligence belongs, where control is required, and where humans must stay involved. 

If everything is automated, nothing is understood.

Start With the Decision,
Not the Technology

The most common mistake in AI automation design is starting with tools.
The correct starting point is always:

What decision is being automated
and what happens if it goes wrong?

Before any workflow is drawn, define:

The

risk

The acceptable margin of error

Define the Automation Boundary

Identify the Actors in the Workflow

Design the Ingestion Flow

Assign Intelligence Deliberately

Design Orchestration Logic

Define Memory Strategy

Embed Guardrails Early

Design Human-in-the-Loop Touchpoints

Define Outputs and Feedback

AI Invoice Processing Workflow

A Simple Workflow Example

Simple. Deliberate. Governed.

Invoice
received

Data ingested and validated

AI agent extracts intent and values

Rules check compliance

Confidence assessed

If low -
human review

If safe -
system updates

Output
logged

Feedback improves
future runs

Common Workflow Design Mistakes

Automating too
much too soon

Ignoring

failure paths

Treating AI as
deterministic

Forgetting
observability

Designing
without governance

Complex workflows do not impress systems
They break them