The Problem WASP Solves
Most AI initiatives die in the gap between an exciting idea and a credible decision. Months go into discovery decks, vendor demos, and roadmap debate, and at the end the organization still has no working evidence that the thing should be built at all. The cost of being wrong compounds quietly: engineering capacity committed to the wrong problem, stakeholders aligned on a solution nobody validated, and budget spent proving the obvious instead of testing the risky.
WASP, our Weekly AI Sprint Protocol, is our answer. It is the proprietary methodology behind our AI Product Strategy and 0 to 1 work, and it does one thing with discipline: it takes an AI initiative from a raw challenge to a validated, working MVP in five business days. Not a slide deck. Not a clickable mockup. A working prototype, tested with real users, with a clear go or no-go signal at the end of the week.
How It Works: Three Phases, One Week
Phase 1 — Challenge Scoping
We start by getting brutally specific about the challenge. Most teams arrive with a solution already in mind and a problem still blurry. We invert that. In Challenge Scoping we pressure-test the business case, define what success would actually look like in measurable terms, and identify the single riskiest assumption the whole initiative rests on. The output is a sharp, agreed problem statement and the one question the week must answer.
Phase 2 — Problem Space
Before building anything, we go deep on the people and the workflow the AI is meant to serve. We map the real process as it exists today, talk to the people who live inside it, and separate the parts of the problem where AI creates genuine leverage from the parts where it adds cost and risk. This is where we decide what is automatable and what is not, a distinction that, in our experience leading enterprise AI work, saves weeks of effort spent calibrating toward impossible targets.
Phase 3 — Solution Space
With the problem framed and the workflow understood, we build. Over the back half of the week our team designs, prototypes, and ships a working MVP, then puts it in front of real users and watches what actually happens. The week closes with evidence: how the prototype performed, where it held up, where it broke, and a clear recommendation on whether to invest further, reshape the approach, or stop.
What You Get at the End of the Week
- A working MVP, not a concept. Something real users can touch and react to.
- A validated, sharpened problem statement that survives contact with the people it serves.
- Evidence on the riskiest assumption, so the build-or-not decision rests on data, not opinion.
- A clear go, reshape, or stop recommendation, with the reasoning made explicit.
- A scoping foundation for what a full build would take if the signal is green.
Why It De-Risks AI Investment
AI projects fail expensively because organizations commit before they validate. WASP moves the moment of evidence to the front, where being wrong is cheap. One week of focused work replaces months of speculative roadmap, and the most dangerous assumption is tested before a single engineer is committed to a quarter of delivery. Leaders get to make the large, irreversible decisions, headcount, budget, public commitment, with a working artifact and real user signal in hand rather than a forecast.
The discipline is the point. By forcing a working prototype in five days, WASP exposes the hard problems immediately instead of letting them hide inside a long timeline. Initiatives that should not exist die cheaply and early. The ones that should exist enter the build phase already de-risked, already scoped, and already validated against the people they are meant to serve.