--- title: "Blog: The Capacity Planning Gap: Why Most SaaS Teams Plan to 100% and Deliver 60%" url: https://daily.dev/posts/blog-the-capacity-planning-gap-why-most-saas-teams-plan-to-100-and-deliver-60--vunf1lh5r source_url: https://wawand.co/blog/posts/the-capacity-planning-gap type: article source: "Wawandco" published: 2026-08-25T14:40:41.115Z updated: 2026-08-25T14:41:07.688Z tags: ["leadership", "code-review"] reading_time: 7 upvotes: 1 comments: 0 language: en --- > ## Documentation Index > Fetch the complete documentation index at: https://daily.dev/llms.txt > Use this file to discover all available pages before exploring further. # Blog: The Capacity Planning Gap: Why Most SaaS Teams Plan to 100% and Deliver 60% **[Wawandco](https://daily.dev/sources/wawandco)** · 7 min read · 1 upvotes · 0 comments ## Summary Engineering teams typically plan to full theoretical headcount but only deliver about 60% of commitments because effective capacity, once meetings, reviews, mentorship, and incidents are subtracted, is really 50-70% of nominal hours. Citing DevPulse, LinearB, Runn, Halkwinds Research, and Faros.ai 2026 benchmark data, the piece argues capacity planning is a measurement problem, not a performance one, and that AI-assisted coding shifts the bottleneck to code review, with review time and incident rates rising sharply under high AI adoption. It proposes four disciplines to close the gap: applying a focus factor, honestly mapping hidden demand, identifying the actual bottleneck discipline, and holding a 15-20% capacity buffer. It closes by pitching embedded engineering teams as a faster alternative to hiring when capacity gaps are immediate. ## Full article daily.dev links to this article rather than hosting it. Read it at the original source: ## Questions this post answers ### Why do engineering teams that plan to full headcount consistently miss their sprint commitments? Because effective delivery capacity is only 50-70% of nominal working hours once meetings, code reviews, mentorship, incidents, and context switching are subtracted. A ten-engineer team with 400 nominal weekly hours typically has only 240-280 hours available for planned work, so committing to 400 hours of roadmap work guarantees the plan fails before the first sprint starts. _daily.dev surfaces engineering benchmarks like these for teams rethinking how they estimate sprint capacity._ ### How does AI-assisted coding affect code review workload and incident rates? High AI adoption correlates with a 210% increase in code-specific tasks with associated pull requests, but also a 156.6% increase in median time to first review, a 441.5% increase in median time in review, and a 242.7% increase in incidents per PR, according to telemetry from 22,000 developers and 4,000 teams. AI-assisted PRs also run 2.6x larger with only a 32.7% acceptance rate versus 84.4% for manual PRs. _teams weighing AI coding tool adoption can track review-bottleneck data like this on daily.dev._ ### What percentage buffer should a team reserve when planning sprint capacity? A 15-20% buffer of effective capacity should be reserved for estimation errors and emergencies, rather than planning to 100% of effective capacity. Teams that skip this buffer consistently miss commitments once unexpected work like incidents or scope creep arrives mid-sprint. _daily.dev helps engineering leads keep up with capacity-planning practices like buffer sizing._ ## Similar posts on daily.dev - [Blog: The Platform Engineering Multiplier: How SaaS Teams Reclaim Roadmap Capacity](https://daily.dev/posts/blog-the-platform-engineering-multiplier-how-saas-teams-reclaim-roadmap-capacity-bhjdp1kkb) · Wawandco · 1 upvotes · 0 comments --- Tags: [#leadership](https://daily.dev/tags/leadership), [#code-review](https://daily.dev/tags/code-review) [View this post on daily.dev](https://daily.dev/posts/blog-the-capacity-planning-gap-why-most-saas-teams-plan-to-100-and-deliver-60--vunf1lh5r)