AI Sprints: When Your Prototype IS Your Product

AI collapsed that gap.

In a recent six-day AI Sprint, we delivered four working prototypes. But here's what made it different: those prototypes weren't demos. They were foundational code. The validation work became the production work. Day five's experiments informed day six's roadmap, which led directly to development.

This is the shift AI enables: sprints where the line from idea to shipping is direct, not circular.

Day 1: Mapping the Real Problem

Every sprint starts with alignment. Not alignment on solutions—alignment on the problem worth solving.

We bring together the product team, technical leads, and people who've shipped AI features before. No PowerPoints. Just honest conversation: What are we actually trying to do? What do users need that they're not getting? Where are competitors weak?

We map the problem space. Not every problem. The specific problems AI might actually solve.

Then we talk constraints. What data do we have access to? What's our timeline? What would success actually look like?

By end of day, everyone understands: we're not exploring AI for AI's sake. We're solving specific user problems where AI happens to be the best tool.

Day 2: Generate, Then Decide

Forty ideas in one day.

We run lightning demos—looking at what other companies have built with AI and relevant APIs. Not to copy, but to understand what's possible.

Then Crazy Eights: everyone sketches eight quick concepts in eight minutes. The rule: don't self-edit. The weirdest ideas often contain the seed of something brilliant.

Then solution sketching: taking the most promising directions and developing them into actual user flows. What would this feel like to use? What problem does it solve? What data would it need?

By afternoon, we have concepts. By end of day, we've voted and decided: the ideas worth testing.

Day 3: Storyboard What We'll Build

This is where sprints typically stay in design-land. Storyboards. User journeys. Clickable prototypes that look real but do nothing.

We storyboard differently. Every step includes the question: what would we need to make this work?

Which APIs? Which AI models? What data transformations? What edge cases?

We're not just designing interfaces. We're mapping technical requirements for experiments we'll run tomorrow.

Day 4: The AI Advantage — Experiment With Real Capabilities

Here's the first place AI changes everything.

In a traditional design sprint, day four is "prototype day." You build convincing fakes—static screens that look interactive but aren't.

We run real experiments instead.

API tests: Can we actually extract the insights this feature needs? We test real queries, real data, real responses.

We document what works, what doesn't, and what surprises us. We're not guessing about feasibility. We're proving it.

This only works because the tools are accessible now. A decade ago, you'd need specialized developers and weeks of setup. Today, you can test APIs, AI models, and data pipelines in hours.

Day 5: Build Prototypes That Become Products

Day five is where the real shift happens.

We use production-ready tools—the Vercel AI SDK, OpenAI, and the actual technology stack we'd recommend for shipping. We build working AI features.

Not demos. Working code.

Users can interact with them. AI responds in real time. Data populates correctly. Error states handle gracefully.

Here's why this matters: these prototypes aren't throwaway work. They're proof-of-concept code built with production patterns. The validation layer and the development layer merge.

In a traditional sprint, you'd validate the concept with a fake, then start over building the real thing. Weeks of rework. New bugs. Learning the problem again from scratch.

In an AI sprint, the prototype code informs—and often becomes—the production code. You're not rebuilding. You're refining.

Day 6: From Validation to Roadmap

We present validated concepts and working prototypes to stakeholders.

But the real deliverable isn't the prototypes. It's certainty.

Certainty that these features are technically feasible—because we've built them.

Certainty that they solve real user problems—because we mapped those problems on day one and validated solutions with real code.

Certainty about implementation timeline—because we've already written foundational code and know exactly what the build will entail.

You can make informed bets. Not "let's see if this works." But "we know this works, here's how long it'll take to ship it."

Why AI Makes This Possible

Three factors enable AI sprints that traditional sprints can't match:

Accessible tooling. You don't need months of infrastructure setup. OpenAI's APIs, Vercel's SDK, pre-trained models—the barrier to experimentation dropped to near zero. What used to require a specialized team now requires a developer and an afternoon.

Prototypes that ship. Because you're building with production tools from day one, your validation code becomes your shipping code. The learning doesn't reset. The work compounds.

Speed reveals truth. When you can build multiple working prototypes in a day, you learn faster. You test more ideas. You find the holes in your logic before they become expensive mistakes. Speed isn't about rushing—it's about learning before committing.

What People Are Saying

"Working with Dave & his FORGE tools is amazing. He has an uncanny ability to cut through the AI hype and target the exact answers you crave, in record time. Dave isn't just a great guy to work with, he's a secret weapon on how to use AI to improve your business."
— Dave Witting, Partner, DEPT® Agency

Let's Talk

If you're trying to figure out where AI fits in your product, you don't need more planning sessions. You need working prototypes that prove feasibility and inform your roadmap.

AI Sprints give you both. One week. Real experiments. Functional features. A direct line from validation to production.

Reach out if you're ready to move fast and build something real.