Everyone wants to move fast. “Let’s build an MVP and test the market.” It’s the right instinct.
But AI projects have a reputation for dragging on. Months of data preparation. Endless model tuning. Scope creep disguised as “iteration.” I’ve seen teams spend a year on what should have been a 12-week sprint.
So what’s actually achievable in 90 days? Based on projects we’ve shipped, here’s an honest breakdown.
What 90 days can get you
A focused AI MVP with one core capability, built on available data, with a functional (not polished) interface.
That’s the honest scope. Let me unpack each piece.
One core capability means you’re not building a platform. You’re building a feature. One model solving one problem for one user type. Resist the temptation to add “and also it could do X.” That’s for version two.
Available data means you’re not waiting months for a data pipeline to be built. Either the data exists and is accessible, or you’re generating synthetic data, or you’re using a foundation model that doesn’t require custom training data. If you need to build data infrastructure from scratch, add 4–8 weeks before the clock starts.
Functional interface means users can interact with the AI capability, but you’re not shipping a polished consumer product. Think internal tool quality. Good enough to validate whether the AI actually solves the problem, not good enough for a Product Hunt launch.
The 90-day structure
Here’s roughly how the time breaks down on a well-run project:
Weeks 1–2: Scope and data assessment
This is where most projects either set themselves up for success or doom themselves to failure. You’re defining exactly what “success” looks like, auditing available data, and identifying the biggest technical risks.
The goal isn’t to start building. It’s to make sure you’re building the right thing.
Weeks 3–6: Core model development
Now you’re building. Training initial models, establishing baselines, iterating on architectures. This is heads-down technical work with frequent checkpoints to make sure you’re converging toward something useful.
If you’re using a foundation model (LLM, vision model, etc.), you’re doing prompt engineering, fine-tuning, and building the retrieval or context systems around it.
Weeks 7–10: Integration and interface
The model works in isolation. Now you’re connecting it to real systems, your database, your user auth, your existing product. You’re building the minimal interface that lets users actually interact with it.
This is where a lot of AI projects stall. The model was the “fun part.” Integration is the grind. Prioritize ruthlessly.
Weeks 11–12: Testing and refinement
Real users (even if it’s just internal stakeholders) are trying the thing. You’re fixing bugs, improving edge cases, and documenting what works and what doesn’t.
You’re also building the list of what version two needs to address. This is important, you’re not failing if the MVP has gaps. You’re failing if you don’t learn from them.
What blows up the timeline
I can almost predict which projects will miss their deadline based on early warning signs.
Unclear success metrics. If you can’t explain how you’ll know the MVP worked, you’ll keep adding scope.
Data isn’t ready. “We have data” is different from “we have clean, labeled, accessible data.” Budget time for the difference.
Too many stakeholders. Every additional decision-maker adds friction. For an MVP, you need a single person with authority to make tradeoffs.
Perfection instincts. An MVP that’s 80% right and ships beats a perfect product that’s still in development. This is hard for some teams.
The honest conversation
When someone asks me “can we build an AI MVP in 90 days?” my answer is: probably, if you’re willing to be disciplined about scope.
The discipline part is harder than the technical part. Saying no to features. Accepting imperfect interfaces. Shipping something that works but isn’t “done.”
That’s not cutting corners. That’s how you learn fast enough to build something great in version two.
