Most AI MVPs stall for the same reason: the scope was written as a technology list instead of a job someone needs done.
Start with one workflow, not one model
Teams plan an assistant, a dashboard, an integration layer and a fine-tuned model, then spend the first month wiring infrastructure nobody has validated. By the time something is testable, the budget is half gone and the product still has no users.
A first version should answer one question. Does this system save a specific person a measurable amount of time on a task they repeat every week? Name the task, write down how long it takes today, then build only what changes that number.
Data access decides your timeline
The model is rarely the hard part. Getting clean, permissioned access to the documents, tickets or records behind the feature usually is.
Before we estimate anything we ask where the data lives, who owns it, how often it changes and what is not allowed to leave the customer environment. Two projects with identical feature lists can differ by a month on those answers alone.
Ship the unglamorous parts early
Request logging, a small evaluation set and a way for users to flag a wrong answer belong in the first release. Without them you cannot tell whether version two is better than version one, and the team ends up arguing about impressions.
Fifty labelled examples are enough to start. Run them on every change, look at what regressed, and keep the failures in the set. This is the cheapest quality process available to an early product.
What to leave out
Role hierarchies, multi-tenant billing, a mobile client and custom model training can almost always wait. So can the second use case, however obvious it looks while you are writing the plan.
Every extra surface adds prompts to maintain, edge cases to test and opinions to reconcile. Keep the surface small until one workflow is genuinely working for real people.
What a realistic first release looks like
Four to six weeks. One workflow, running on production data, used by a group of people who agreed to give feedback. A screen that shows how often the output was accepted without edits, and a short list of the failures you already know how to fix.
