# AI and workflow automation for operations that cannot stay manual

I design and implement automation for companies whose delivery is limited by manual steps — not by a lack of dashboards. The default path is still a workflow: trigger, validate, execute, observe, recover. Models enter when the input is unstructured or the decision is ambiguous. They do not enter because 'automation' and 'AI' are now the same slide. I have built automation backends, content-ops pipelines, and AI-assisted product flows. The result you should care about is a process that survives bad input, retries, and the night shift — with a human path when it does not.

## Who this is for
- Operations and product leaders whose SLAs are eaten by copy-paste between systems.
- Companies drowning in unstructured documents, tickets, or content that still needs a human for the last 10%.
- Teams that bought a no-code automation tool and hit the wall on failure handling.

## When a process should not get a model
Most 'AI automation' requests are workflow requests. That is not a lesser project. It is usually the correct one.
### If the input is already structured, write code
A status change, a paid invoice, a filled form — these are jobs for queues and APIs. A model here is latency and a new error class.

### If the output is a side effect, require a contract
Creating a ticket, moving money, or publishing content is not a chat completion. Validate, then execute. Never execute from prose.

### If the exception rate is high, design the exception path first
Automation that only handles 70% and dumps the rest in a shared inbox is how you create a worse job. Queue, SLA, and owner.

### If you cannot name the hour you are buying back, wait
Automation for narrative is a product feature. Automation for operations needs a number, even a rough one.


Canonical: https://ihar-ivaniuk.com/en/services/ai-automation