For new functionality, the Why still exists in people’s heads and conversations. AI can help surface it before it disappears. During refinement or design discussions, it could ask why the functionality is needed, whose problem it solves, which outcome it should influence, what assumptions shape it, and under what circumstances it would no longer be necessary.
In that setting, AI should not invent the Why from a user story after the discussion has ended. It should help people articulate, challenge and preserve the Why while the decision is being made.
Existing functionality presents a different problem. The people who made the decision may have left, and the original rationale may never have been recorded. AI can inspect the surviving evidence: code, BDD scenarios, tests, backlog history, commit messages, architecture decisions, support incidents and business documents. From these fragments, it may be able to reconstruct a likely explanation.
But a likely explanation is not necessarily the original explanation. AI may recover what the system does and reconstruct how it does it. It can also propose why that behavior might exist. Yet where the original Why left no trace, AI cannot restore history with certainty. It can only form a hypothesis.
That means an AI-generated Why should carry an explicit status:
- Recorded — directly supported by an authoritative source.
- Corroborated — supported by several independent pieces of evidence.
- Inferred — plausible, but not directly evidenced.
- Unknown — no defensible explanation can be reconstructed.
This may be where AI becomes most valuable: not by filling every blank with persuasive prose, but by distinguishing recovered understanding from inferred understanding and genuine gaps.
For existing systems, AI could act as an investigator. It could identify functionality whose rationale is preserved, functionality whose rationale is merely implied, contradictory explanations, and areas where interviews are still needed before the remaining human knowledge disappears.
For new systems, it could act as a facilitator. It could ensure that the Why is captured alongside the How and the What rather than being left behind in a meeting.
The two uses are complementary:
Prospectively, AI can help prevent understanding debt. Retrospectively, it can help repay it.
But it cannot manufacture certainty.
Perhaps the most important role for AI is therefore not answering every Why. It is helping us see which Whys are known, which are inferred, and which have already been lost.
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