Despite the past decade’s increased adoption of scientific methodologies by startups, most still fail to scale into large companies. The paralyzing plethora of advice, theory and models recommended to startups is poorly matched by practical advice on the applicability and implications of actually following the recommendations. In this action-based research I, an IT consultant for twelve years and founder/co-founder of several startups, try out and evaluate the applicability of methodologies for applying scientific management principles to innovation in early-stage startups. In the first part of my research, I use an naive explorative hands-on approach which results in insights into the limited applicability of popular methodologies such as Growth Hacking and The Lean Startup. These limitations are especially pronounced for early-stage startups who are yet to launch a minimum viable product (MVP), as well as those that have trouble to decide which hypotheses are the riskiest. Most actionable insights during this part stemmed from the engagement in various thought-experiments and reflections, and not from external customer feedback. To remedy this, and to thoroughly evaluate the applicability of a pre-launch market assessment method, I engage in market opportunity discovery following the recommendations set forth by Outcome-Driven Innovation (ODI). This hands-on in-depth approach yielded seemingly high-quality actionable insights with direct implications for the product and marketing strategy of the studied early-stage startup. In the discussion part, I reflect over the applicability of the evaluated methodologies and argue that the main difference between applicable and non-applicable methodologies is whether they are manufacturing-based or needs-based. Finally, I reflect over possible implications and suggest that a startup community wide change of mindset from manufacturing-based methodologies such as The Lean Startup to needs-based methodologies such as Outcome-Driven Innovation will minimize the startup innovation-process variability and increase startup efficiency dramatically on a global scale.