What happens when you apply date pressure to software engineers working on high value software projects? The engineers will focus on delivering Something™ by the Date™! This fatal flaw results in delivery of a Something™ full of chaos and features that nobody really wants or needs.
The mandate from above is clear, just get it done! Avoid everything that's in the way: all advice, all expertise, all discovery efforts that detract from hitting the Date™!
What these organizations don't realize is that all software change can be modeled as three components: Value, Filler and Chaos. Chaos destroys Value and Filler is just functionality that nobody wants. When date pressure is applied to software projects, the work needed to remove Chaos is subtly placed on the chopping block. Work like error handling, clear logging, chaos & load testing and other quality work is quietly deferred in favor of hitting the Date™.
Finding value is the result of enabling individual and group-level discovery attempts. It's not the result of everyone following one leader's gut.
What just happened is a new software product/feature was created that no customer wanted. This happens way too often. In fact, most hyper important software projects that must be done by date certain or else, have deep flaws that cause some variation of this phenomenon, flaws that include:
Not wanted - Company specified a solution to a problem that customers don't actually have
No Rarity - Company is pursuing an iKnockoff of existing products. The market already has two scaled competitors with working solutions, customers naturally spend budget on products that are already successful to avoid risk
Incorrect Packaging - Customers need a website, but the company created an iOS app instead
Incorrect Pricing - Customers need SaaS pricing, but the company created a shrink wrapped, on-premise solution with CapEx and maintenance agreements instead
There is increasing concern that most current published research findings are false. The probability that a research claim is true may depend on study power and bias, the number of other studies on the same question, and, importantly, the ratio of true to no relationships among the relationships probed in each scientific field. In this framework, a research finding is less likely to be true when the studies conducted in a field are smaller; when effect sizes are smaller; when there is a greater number and lesser preselection of tested relationships; where there is greater flexibility in designs, definitions, outcomes, and analytical modes; when there is greater financial and other interest and prejudice; and when more teams are involved in a scientific field in chase of statistical significance.