Nothing slows a team down faster than building the wrong thing—or building the right thing the wrong way. When requirements are vague, shifting, or interpreted differently across teams, the SDLC becomes a series of avoidable detours. Developers guess. QA chases moving targets. Stakeholders get frustrated. And the rework cycle continues.
This isn’t a documentation problem—it’s a clarity problem. Most teams don’t need thicker requirements; they need requirements that are testable, traceable, and aligned across business, product, and engineering. Without that shared understanding, the SDLC becomes an expensive game of telephone.
A Common Example
A product owner feels pressure to “go fast,” so instead of taking an extra day to validate user flows or confirm edge cases, they push requirements into the sprint with a promise to “figure out the details later.”
The team appreciates the urgency and dives in—only to discover mid-sprint that:
the workflow isn’t finalized
- the workflow isn't finalized
- the business rules conflict with an earlier feature, or
- a key dependency wasn't mentioned.
What was meant to accelerate delivery ends up slowing it dramatically. The team switches context, revisits decisions, rewrites code, and rebuilds tests. By the time the dust settles, the original “saved day” has ballooned into a week of rework across development, QA, and product.
It didn’t speed things up—it taxed every part of the system.
Symptoms
- Sprint goals constantly change mid-cycle
- Features meet the spec but not the intent
- Rework becomes a normal part of the workflow
- Feedback loops stretch from days to weeks
Why It Matters
Ambiguity compounds. A requirement that’s 10% unclear at the beginning can become 200% more expensive to fix once it reaches QA or production. Every unclear requirement forces the team to make assumptions—and assumptions are the slowest part of software development.
The Iter8 Approach
We treat requirement clarity as an iterative discipline, not a one-time event.
We look at:
- where ambiguity keeps reappearing in your cycles,
- where misalignment is introduced between handoffs, and
- how feedback from each iteration can tighten the next.
By mapping these patterns and creating intentional reflection points, teams begin to catch misunderstandings before work begins—not after code is written.
