Generative AI  

How do we measure the ROI of GenAI adoption in engineering?

AI ROI

More and more engineering leaders are adopting GenAI tools such as Copilot in their teams to boost productivity and efficiency, eventually leading to a better ROI. The good part is that these tools don't require much to learn and cost a few dollars per month per person. If spending $20 per month on a Copilot license saves me 50 hours a month, it is already a considerable ROI.

If you are an engineering leader and really want to measure ROI (Return on Investment), here are some facts for you. An engineering team can easily save 30% to 40% of the SDLC time from analysis to the delivery phase. Imagine a project that costs $500,000 and 6 months. GenAI and related tools can save a major chunk of that and launch a project within 3 months. The only catch is that the dev team must be really good at GenAI. 

The following discussion may help clear up more on this topic. 

Measuring the ROI requires a combination of quantitative and qualitative metrics that assess both productivity gains and cost savings. Here’s a breakdown:

✅ 1. Define Clear Objectives

Start by aligning GenAI adoption with specific engineering goals:

  • Improve developer productivity
  • Reduce time-to-market
  • Enhance code quality
  • Automate repetitive tasks (documentation, testing, etc.)

✅ 2. Quantitative ROI Metrics

a. Developer Productivity

  • ⏱️ Time saved per task (e.g., code generation, refactoring, writing tests)
  • 📈 Velocity increase (e.g., story points completed per sprint before vs. after GenAI)
  • 🔁 Reduction in context switching due to intelligent suggestions in IDEs

b. Cost Reduction

  • 💸 Fewer hours spent on manual tasks (e.g., writing boilerplate code, documentation)
  • 👥 Lower outsourcing or hiring costs due to higher productivity per engineer
  • 📉 Reduced bug-fix and rework costs via improved code reviews

c. Cycle Time Improvement

  • ⌛ Reduction in time from commit to deploy

d. Quality Metrics

  • 🐞 Reduction in post-release defects
  • 🧪 Increased test coverage through AI-generated tests

✅ 3. Qualitative ROI Indicators

a. Developer Experience

  • 😀 Increased developer satisfaction (via surveys or NPS)
  • 📚 Higher onboarding efficiency (especially for junior devs using GenAI as a coding assistant)

b. Innovation Enablement

  • ⚙️ Engineers spend more time on complex, value-adding tasks vs. routine code writing

c. Knowledge Sharing

  • 📖 Improved and more consistent documentation leads to faster team alignment

✅ 4. ROI Formula (Simplified)

ROI (%) = [(Value Generated – Cost of GenAI Tools/Integration) / Cost] × 100

Value generated can be estimated based on time saved × average hourly rate of developers, plus quality or support cost reductions.

✅ 5. Tools for Tracking

  • Jira/DevOps metrics (lead time, cycle time, throughput)
  • Git analytics tools (e.g., Code Climate, LinearB, Waydev)
  • Survey tools (for capturing team sentiment & experience)
  • Time tracking (for before/after comparisons)

✅ Example ROI Statement

By integrating GenAI into our development pipeline, we saw a 25% reduction in PR review time, a 30% increase in sprint velocity, and saved ~400 developer hours in 3 months—equating to ~$40,000 in productivity gains.

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