It definitely can! Full disclosure, I'm a DevRel at FlowFuse, so I'm definitely biased here - but there's quite a lot of support for AI workflows, from lightweight automation to fairly complex pipelines.
For example, I'm currently building a flow that connects device monitoring data to a central reporting structure, compares the incoming values against internal spec docs, and uses OpenAI to summarise overall operational status and any drift/out-of-spec issues. The summaries (along with the raw JSON objects) are then sent over MQTT for multi-site compliance and operations. Once you map it out, it's surprisingly straightforward to build.
What makes Node-RED and FlowFuse stand out on this particular use case IMO is that AI flows get treated just like any other message payload. That means your AI output becomes a first-class data object in the system - you can remix, transform, mutate, or extract from it the same way you would any other JSON payload, making it pretty portable and easy to integrate.
Node-Red is probably a more hands on approach, than n8n, as someone who have 6-7 years of experience in node-red i find n8n really complicated for some reason.
A lot of windows and configurations. Started with node-red and quickly built great skills in backend development.
