The Refactor That Finished in Two Days But Still Needed Six Weeks of Sign-off

I shipped the refactor on a Tuesday afternoon. Two days of prompts and quick reviews, and the whole module sat there cleaned up, tested, ready. By Wednesday morning the first approval request had already bounced back with a question from someone two layers up who’d never touched the repo.

That was the part nobody warned us about. The code moved fast. Everything else moved like it always had—slow, cautious, built for a world where changes arrived one at a time and every line carried human weight. Now the lines arrive in batches, and the weight still has to be carried the old way.

Teresa Torres’s continuous discovery framework treats opportunity identification and solution testing as ongoing activities rather than staged events. Applied here, it reveals that verification gates function as the new discovery layer. When teams keep generation fast but leave validation infrequent and heavyweight, the system simply moves the bottleneck downstream without removing it.

The Number Everyone Celebrates

The two-day refactor number gets reported in sprint reviews and leadership updates. It reflects actual engineering hours spent on code changes. What the number hides is that the same teams now generate three times the volume of proposed changes per cycle.

That volume lands in queues designed for slower, human-authored diffs. Reviewers face context switching across dozens of small, AI-suggested edits rather than a handful of deliberate ones. The celebration of the initial number prevents anyone from measuring the cumulative time spent re-explaining intent across each verification step.

Ministry platforms that produce children’s curriculum or sermon outlines see the same pattern. A volunteer-facing lesson set that once took a content editor two weeks now reaches first draft in three days. The subsequent review with doctrinal reviewers, accessibility specialists, and print production still consumes the original calendar time because no one adjusted the handoff criteria.

Where the Six Weeks Actually Hide

The delay lives in repeated clarification loops rather than in the code itself. Each approver asks questions that the original prompt to the AI never surfaced: edge cases for age groups, regional translation needs, or integration with existing print workflows. Because the generation step moved fast, these questions arrive late and in batches.

Continuous discovery, in Torres’s terms, would have surfaced those constraints through regular exposure to users and stakeholders before the refactor began. Instead, the fast generation step creates the appearance that the work is mostly complete. The verification gate then becomes the place where discovery finally occurs, but without the lightweight interview cadence that makes discovery cheap.

One platform that manages global sermon resources found that legal and translation sign-offs now require separate meetings for each AI-generated variant. The meetings themselves had not changed frequency; only the number of variants had increased. The six weeks therefore represent accumulated coordination cost, not review effort per se.

Redesigning the Verification Gate

The fix begins by moving small validation checks earlier and more often. Rather than waiting for a complete refactor to reach the full approver list, teams can run targeted opportunity solution trees with the actual users of the output—volunteers, pastors, or translators—while generation is still happening. This turns verification from a final gate into a running discovery process.

Concrete changes include shortening the artifact that enters review. Instead of shipping an entire refactored module, teams surface one user-facing difference at a time through a simple prototype or print sample. Approvers respond to the observable impact rather than the full technical diff. This matches Torres’s emphasis on testing solutions against real opportunities instead of internal completeness criteria.

Another adjustment is to embed the same people who will ultimately sign off into the prompt refinement stage itself. When a doctrinal reviewer sees the initial prompt and flags a constraint before generation runs, the subsequent cycle avoids the six-week surprise. The calendar time shrinks because the verification criteria travel with the work rather than arriving after it.

Teams that adopted this pattern on curriculum tools reported that total elapsed time dropped from six weeks to under three, not because generation got faster but because the verification criteria became explicit and stable earlier in the loop.

Your Turn: Apply This Today

• Map the exact sequence of people and artifacts that must approve any AI-generated change in your current workflow, listing each step by name and average days elapsed.
• Pick the longest single verification step and run a 30-minute conversation with that approver this week focused only on the three constraints they care about most.
• Insert one lightweight check—such as a shared prompt log or single-user test—before the refactor reaches the full gate, then measure whether the later step shortens.
• Reduce the review artifact size by half for the next cycle and require approvers to respond to observable user impact rather than technical completeness.
• Schedule a recurring 15-minute slot with one downstream stakeholder to review emerging prompts rather than finished outputs.
• Document the revised gate criteria in a single shared paragraph and circulate it before any new generation task begins.

The same verification trap shows up in the workflow described in The Refactor That Finished in Two Days and Then Sat for a Week and in the coordination overhead tracked in Faster Code Does Not Move the Real Ministry Bottleneck.

I consult with product leaders in faith-tech organizations on verification workflows, AI output governance, and shifting from generation speed to discovery loops. Let’s talk.