Before building a complex feature, validate its value by manually creating the desired output for customers. The Buildots team used Excel to generate performance insights from their data. Only after seeing customers act on these manual reports did they productize the feature.
Related Insights
Before committing resources to a proof-of-concept (POC), build a preliminary ROI case. If the potential return isn't substantial enough for the customer to reallocate budget or personnel, the deal is unlikely to close. This step prevents wasting both your and your customer's time on unwinnable evaluations.
Instead of inventing new features, Prepared identified its most lucrative expansion opportunity by seeing users' painful workarounds. They noticed 911 dispatchers manually copy-pasting foreign language texts into Google Translate—a clear signal of a high-value problem they could solve directly.
For AI products, the quality of the model's response is paramount. Before building a full feature (MVP), first validate that you can achieve a 'Minimum Viable Output' (MVO). If the core AI output isn't reliable and desirable, don't waste time productizing the feature around it.
Validate business ideas by creating a fake prototype or wireframe and selling it to customers first. This confirms demand and secures revenue before you invest time and money into development, which the speaker identifies as the hardest part of validation.
Features follow an S-curve of value. Early effort yields little, then a steep rise, then diminishing returns. Use this model to determine if a feature needs more investment to become valuable or if you've already extracted its maximum worth and should stop investing.
In early stages, the key to an effective product roadmap is ruthlessly prioritizing based on the severity of customer pain. A feature is only worth building if it solves an acute, costly problem. If customers aren't in enough pain to spend money and time, the idea is irrelevant for near-term revenue generation.
Product teams often fear showing prototypes because strong customer demand creates pressure. This mindset is flawed. Having customers eager to buy an unbuilt feature is a high-quality signal that validates your roadmap and is the best problem a product manager can have.
To avoid over-engineering, validate an AI chatbot using a simple spreadsheet as its knowledge base. This MVP approach quickly tests user adoption and commercial value. The subsequent pain of manually updating the sheet is the best justification for investing engineering resources into a proper data pipeline.
People are unreliable at predicting their future behavior. Instead of asking if they *would* use a new feature, ask for a specific instance in the last month where it *would have been* useful. If they can't recall one, it's a major red flag for adoption.
When handed a specific solution to build, don't just execute. Reverse-engineer the intended customer behavior and outcome. This creates an opportunity to define better success metrics, pressure-test the underlying problem, and potentially propose more effective solutions in the future.
