{
  "id": 8486792,
  "title": "Your Team Got Faster With AI. Why Is Your First Move a Layoff?",
  "url": "https://urgent.news/2026/09/19/your-team-got-faster-with-ai-why-is-your-first-move-a-layoff",
  "topic": "ai",
  "section": "AI",
  "published": "2026-09-19T15:30:10.000Z",
  "source": {
    "name": "Dev.to",
    "slug": "dev-to",
    "url": "https://dev.to/coryntas/your-team-got-faster-with-ai-why-is-your-first-move-a-layoff-5676"
  },
  "original_language": "en",
  "account": "After implementing AI workflows, a team experienced faster results by producing useful first drafts, catching routine mistakes, and reducing documentation time. However, questions arose when a budget meeting questioned the necessity of the entire team, as customers were waiting to go live due to a stuck deal. An experienced engineer kept being pulled into recurring onboarding problems that should have been fixed earlier.\n\nThe decision to lay off employees was made easier by the salary savings, but it overlooks the value of the remaining team's skills and potential contributions. A CFO can estimate savings, while a head of implementation must argue for retaining team members and assigning them different tasks to increase customer onboarding success. The payroll reduction is measurable, but the impact of losing those people remains a forecast.\n\nThe article suggests that companies should focus on the actual improvements achieved by AI first. For instance, if AI writes an integration in an afternoon, that's beneficial, but it doesn't guarantee customer agreement on what the integration should do. Customers may have different definitions of \"active customers.\" AI assistance in customer support, according to a 2023 NBER working paper, resulted in a 14% increase in issues resolved per hour, but this gain was seen more in less experienced workers with minimal impact on highly skilled workers.\n\nP&G's experiment showed AI matched human teams in innovation tasks while helping produce proposals combining technical and commercial perspectives, proving that AI still holds value with human judgment. However, the article raises concerns about how this might affect employees who built the workflow and may experience job loss due to increased efficiency.\n\nThe author recommends a different approach, starting with a narrower commitment: during a trial period, use AI-generated time to clear specific customer work, involve the team in choosing projects, and evaluate the results before deciding on further actions. This approach considers the well-being of employees who adapt to new workflows and encourages them to share their knowledge, improving the shared process and promoting effective collaboration with AI.",
  "summary": "Suppose your implementation team finally gets an AI workflow working. It produces useful first drafts of integrations, catches routine mistakes, and cuts down the time spent writing documentation. Engineers still review the output, but the improvement holds up after review. At the next budget meeting, someone asks whether you still need the whole team. You also have customers waiting to go live.…",
  "key_points": [
    "Budget meeting questioned team necessity due to stuck deals, leading to layoff decision.",
    "Retaining team members could boost onboarding success, but impact of layoffs remains forecasted."
  ],
  "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."
}