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40% Faster Delivery: The Real Math Behind AI-Accelerated Development

Or: How we stopped writing boilerplate and started shipping features

Donnish Team
8 min read
2 Oct 2025

Let's address the elephant in the room. Everyone claims AI makes development faster. Most are guessing. We tracked every sprint for the last 12 months.

The Reality Check

GitHub's research shows 55% faster task completion with Copilot. Sounds amazing, right? Here's what they don't tell you: that's for individual coding tasks, not end-to-end delivery.

Real-world delivery involves:

  • Requirements gathering (AI helps: 20%)
  • Architecture decisions (AI helps: 10%)
  • Coding (AI helps: 55%)
  • Testing (AI helps: 40%)
  • Deployment (AI helps: 30%)
Actual sprint velocity increase: 38-42%

We claim 40% to stay honest.

Where AI Actually Accelerates

The Boring Stuff (Thankfully!)

Remember writing your 500th CRUD endpoint? Our developers don't. AI handles:

  • Boilerplate generation (90% automated)
  • Test scaffolding (80% automated)
  • Documentation (70% automated)
  • Code reviews for style (100% automated)

A Zoominfo study tracked 400 developers using Copilot. They accepted 20% of suggestions but saved 3.4 days per week. That's not because AI writes everything - it's because it handles the repetitive 30% that burns mental energy.

The Surprising Wins

Data transformation: What used to take 3 hours of regex wrestling now takes 15 minutes with Claude Code.

API integration: Instead of reading docs for 2 hours, we describe what we need and get working code in 10 minutes.

Debugging: "Why is this React component re-rendering 47 times?" AI spots the missing dependency in seconds.

The Part Nobody Talks About

AI doesn't work everywhere. Here's where we still go manual:

  • Business logic (only humans understand your weird edge cases)
  • Performance optimisation (AI suggests, humans decide)
  • Security architecture (never trust AI with your keys)
  • UX decisions (AI has no taste)

Real Client Example

Project: Healthcare Platform (Ongoing)

Traditional model: 8 developers (11 with cross-functional team) delivering X features/sprint

Our model: 4 developers + AI agents delivering 0.75X features/sprint

The wins:

  • 50% team size with 75% output = exceptional value ratio
  • AI-augmented developers: 1.5x individual productivity
  • Eliminated overhead: No bloated cross-functional coordination
  • Learning curve: Efficiency accelerating as our agentic workflows mature
  • Focus: Lean teams ship faster than committees

The Tools That Actually Matter

Forget the hype. Here's our enterprise-proven stack:

  • Cursor + Claude Agents: AI-enhanced development with privacy mode enforcement (used by OpenAI, Shopify, Nvidia)
  • Azure AI Services: Production AI capabilities with Australian data sovereignty
  • V0 + Figma: Design to code pipeline (36.77% market leader)
  • Playwright + Jest: Industry-standard testing automation

Combined efficiency gain: 40% faster delivery with enterprise security.

Bottom Line

We're not 10x engineers. We're 1.4x engineers with good tools. In a world where projects regularly run 200% over time, 40% faster is revolutionary enough.

Want to see the math for your specific project?

References

  • GitHub Copilot Impact Study 2024
  • Zoominfo Developer Productivity Report
  • BCG AI Impact Analysis

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