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
How would you change this structure so that you could put a masonry brick on top of it without crushing the figurine, bearing in mind that each block added costs 10 cents? If you are like most participants in a study reported by Adams et al. in Nature, you would add pillars to better support the roof. But a simpler (and cheaper) solution would be to remove the existing pillar, and let the roof simply rest on the base.
A series of problem-solving experiments reveal that people are more likely to consider solutions that add features than solutions that remove them, even when removing features is more efficient.