{
  "id": 1443881,
  "title": "Adoption Is a Latency Problem: The Zero-Backlog Policy",
  "url": "https://urgent.news/2026/08/17/adoption-is-a-latency-problem-the-zero-backlog-policy",
  "topic": "ai",
  "section": "AI",
  "published": "2026-08-17T07:15:32.000Z",
  "source": {
    "name": "Dev.to",
    "slug": "dev-to",
    "url": "https://dev.to/helkyn_coello/adoption-is-a-latency-problem-the-zero-backlog-policy-1pi0"
  },
  "original_language": "en",
  "account": "In an AI project, a common issue is the lack of feedback, which occurs when a team creates a feedback channel but never receives any suggestions. Deloitte's survey shows that most workers have access to AI tools, but less than 60% actually use them in their daily work. The gap between having a tool and using it is the key to adoption, and the project the author observed taught them how to close that gap.\n\nThe project involved generating training videos for a training team, starting with a narrow scope of simple functions and one team. Each stage of the build took two to three weeks, and every stage had to result in something the team could use. After the first increment was shipped, a dedicated channel was opened for feedback. The AI team was instructed to respond to feedback the same day, and a short meeting was held daily to discuss the feedback, with the lead of the using team present.\n\nThe zero-backlog policy was implemented, meaning that no suggestions were parked. If a suggestion was not shipped within a day or two, the user was informed the same day why it wouldn't be implemented. This policy closed the adoption gap by showing users that their suggestions were implemented quickly, while those sitting in a quarterly backlog proved otherwise. The project owners became the users, who corrected each other's usage and reported problems as if the tool was theirs.\n\nThe presentation of the project at company-wide meetings was a deliberate choice to have the users present, rather than the AI team. The metric tracked from day one was finished videos per week, a metric the business already cared about. When the metric improved, there was no need to convince anyone of its importance. Once the tool stabilized and adoption was no longer in question, the cadence relaxed, releasing every other day and then weekly.\n\nTraceability and audit were built in from the first increment, and governance was simply how the tool worked from day one. This approach ensured that adoption was not slowed down by later added compliance requirements. Finally, having traceability built in from the start prevented any perception of it being a later added friction point. The source material highlights that latency failures can also contribute to adoption failures, and big-bang projects that take six months before users can touch the tool rarely create the opportunity to build adoption.",
  "summary": "Notes from an AI video project, and the release cadence that turned one team into the project's owners The most common artifact of an enterprise AI rollout is a dead feedback channel. It gets created in week one with genuine enthusiasm, collects a burst of suggestions in the first month, and goes quiet by the third. Not because people ran out of opinions, but because nothing they said ever came…",
  "key_points": [],
  "editors_take": null,
  "illustration": null,
  "coverage": {
    "outlets": 1,
    "also_reported_by": []
  },
  "ai_generated": true,
  "disclaimer": "Summaries, key points and the editor’s take are written by software from other outlets’ reporting and may contain errors — always check the linked original."
}