Navigating the AI Project Delivery Landscape: A Practical Guide

I've been getting tons of questions lately about hosting AI workflows and handing them over to clients. Like, where should they be hosted? Who handles the security? What about API keys? And how the heck do you test everything before delivery?

There's not much out there that walks you through this stuff step by step. So I figured I'd share what I've learned from delivering countless AI workflow projects.

First up - who should host the workflow? This is kinda huge because it impacts everything downstream. From my experience, it's usually best to have clients host their own workflows. I primarily work with N8N, which has specific licensing requirements. You can only use it internally unless you've got that commercial license.

Basically, you've got three options:

1) Client hosts N8N themselves - this is the cleanest approach. They own their N8N Cloud instance, and you just build within it. Think Zapier model.

2) You host N8N for your own agency stuff - perfectly fine for your internal automations, just don't expose it to clients.

3) Hosting N8N as a SaaS product - only if you've got that commercial license! Don't try to skirt this one.

Security is something people overlook in AI project management until it's too late. N8N actually has solid encryption for credentials, only decrypting during execution. But you still gotta be careful with webhook security - always use HTTPS, signing secrets, and validation tokens.

One thing I've learned the hard way? API key management. Seriously. Have clients generate their own API keys for OpenAI or whatever services you're using. I used to handle billing myself and it was a nightmare - unpredictable costs, delayed invoices, ugh. Now I just record quick loom videos showing clients how to set up their own accounts. Way cleaner workflow handover that way.

Testing is another critical part of AI workflow delivery. You need real data for this - actual emails, CRM records, whatever you're working with. And don't just test the happy path! Throw some weird edge cases at it and see if it breaks. For AI components specifically, check for relevance, tone, safety... I usually log everything in a Google sheet so clients can see exactly what was tested.

The handover process varies. Sometimes you're building directly in the client's environment, which makes things easier. But always have separate test and production versions! And back that stuff up, preferably somewhere like GitHub.

Lastly, get everything in writing. What's included in the scope? Who owns what? What happens if a client wants to bail? Data quality issues can sink an otherwise solid AI project, so make sure everyone understands what "done" looks like.

In my free school community, I've put together additional resources on project forecasting and data quality for AI workflows. Because honestly, the most successful AI project delivery comes down to solid planning, transparency, and clear expectations.

Got questions about your specific AI workflow delivery challenge? Drop them below!