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AI Coding AssistantsAI Business & IndustryZenn AI/MLPublished: Sep 30, 2026, 22:00 JST

AI-DLC 3 rounds at Kanri cut PBI days by 3割

AI-DLC 3 rounds at Kanri cut PBI days by 3割

3 Key Points

  1. What happened

    Kanri's benefits team ran three AI-DLC rounds; round one's three PBIs finished about 30% faster in days than comparable past PBIs, one taking 9 days versus roughly 20 before.

  2. Why it matters

    The body attributes the shorter completion times partly to AI-DLC and reports that spec gaps surfaced during sessions, though it notes complexity and sample sizes were not controlled.

  3. What to watch

    The team says the 3割 and other figures are reference values only, since it did not log actual work hours and will start by taking a baseline before adopting the method.

WHO IT HITSProduct managers and engineers running sprint-based development can see how AI-DLC was inserted into 2-week scrums at Kanri, while teams evaluating the method should note the 3割 and other figures are labeled reference values.

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Context & Analysis

Kanri's benefits team had already run three AI-DLC rounds before writing this account. The first round started from a reviewed mock, which the team says turned elaboration into a late exercise in putting the UI into words, and engineers reported feeling constrained by the fixed UI. The second round took on disabling a master that both serves users and hosts content, a change requiring checks across all screens and APIs; mid-session the team realized the master held two roles, and a proposal to split tables by role drew concern from a product-side decision-maker who had not attended. Engineers then compared three options, and the middle option with the smallest workload won product-side agreement, matching what the development team had originally assumed, so no rework occurred. The team also notes that if the estimates had changed the direction, roughly two days of work by three engineers could have been reworked.

The second round's retrospective led to five added steps, and the third round tested them on three account-issuance PBIs, finishing API and DB design in three days. The body reports both uneven areas and wins: decision records and Q&A were scattered and hard to find, but 19 decisions were kept; browser-readable deliverables reduced headaches from low-quality screen sharing; AI questions produced options only when prompted for pros and cons. The team also describes a case where it felt a discrepancy during domain modeling, assumed implementation could absorb it, and later found the issuing-side implementation conflicted with the domain model's handling, which it fixed on the domain-model side after confirming no spec impact.

The body itself frames the shorter completion days as a reference value: complexity and sample sizes were not aligned, the cause of the reduction was not isolated, and actual work hours were not recorded, so the team will start with a baseline. For teams considering AI-DLC, the value here may lie less in the 3割 figure than in the concrete process fixes—Tier-based impact judgment, separating elaboration from construction, and recording decisions—which the body says carried over into round three.

FAQ
How much faster did the AI-DLC rounds finish?
Kanri's three PBIs in round one finished about 30% fewer days than comparable past PBIs. One item of a scale that previously took about 20 days was done in 9 days.
What did the team change after the second round?
After round two, it rewrote the workflow as a skill and added five steps: Tier-based impact judgment, separating elaboration from construction, recording decisions, browser-readable deliverables, and shaping AI questions. Round three then handled three account-issuance PBIs, reaching API and DB design in 3 days.
What did the team say it will do next?
It plans to take a baseline before adopting AI-DLC, since it had not logged actual work hours. It also plans to break sessions into 2-hour slots and reduce the number of parallel items.

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