Testing Assumptions: Why Business Plans Are Dangerous at First

A startup team was excited about launching a fitness accessory. They spent weeks crafting a business plan, building elegant spreadsheets and detailed projections that looked impressive in board meetings. The plan was colorful and sleek, the numbers just right. But after pouring thousands of dollars into production and a glossy launch event with free cupcakes (which everyone liked), something odd happened. Hardly anyone actually bought a second product. The team was left with shelves full of inventory—and a report that explained why the plan had made perfect sense, but reality didn’t cooperate.

A few months later, learning from this painful lesson, the team shifted gears. This time, they listed their boldest bets: Would runners spend extra for a smart chip? Did gym owners want to sell accessories at all? Instead of hypothesizing, they built a barebones web page with a simple sign-up form and drove a few dozen people to click 'Buy Now.' Only 6 out of 100 did. Then, after subtly tweaking the headline and price, the number doubled overnight. Armed with this evidence, they called up three gym owners to ask for a small commitment on the spot. One agreed to test, two passed, but nobody gave polite, vague answers.

Wasted plans turned into quick, actionable experiments that revealed customer realities, not just optimistic guesses. Over time, the team realized that business plans were great for established routines but disastrous for bold new ideas riddled with uncertainty. The real work was not predicting, but testing, learning, and adapting—each iteration grounded in evidence, not hope.

This approach echoed lean startup and behavioral economics principles: when uncertainty is high, action and feedback loops beat paper forecasts every time.

Before you write another long business plan, pause and ask yourself what assumptions need to be true for your idea to work. Pick the riskiest one—maybe it's 'people will pay for this feature'—and design a lean experiment around it. Try a prototype, a fake sale, even just measuring online clicks. Look for clear, measurable data and allow yourself to react to what you find, even if it disrupts your initial vision. The faster you learn, the less you risk—a single experiment this week is worth more than a month’s worth of plans.