ROI of AI-Assisted Development
One of the biggest questions around AI adoption is what business value does it actually bring?
I'm currently introducing AI into the SDLC across my teams, so measuring its real impact is something I'm really interested in.
Back in February 2026, DORA published a dedicated report on this topic: ROI of AI-assisted Software Development report.
Key takeaways:
🔸 Measure your baseline first. Before evaluating the impact of AI, you need to understand where you're now. Without "before" metrics, it's impossible to measure "after".
🔸 Expect a J-curve. Most organizations experience a temporary productivity drop before seeing benefits. Main reasons for that:
- The learning curve. Time to master new tools and workflows.
- The verification tax. Time to verify AI-generated code and establish trustworthiness of agents output.
- Pipeline adaptation. Scaling other downstream processes like testing and change approval to handle increased number of code changes.
🔸 Invest in engineering practices. The quality of an internal platform, clear workflows, reliable test automation, and strong engineering culture become critical to produce predictable delivery. The rule garbage in -> garbage out is still actual.
🔸 Measure both positive and negative outcomes. Higher throughput is valuable only if it doesn't come with higher instability, more defects, or lower quality.
🔸 Measure the following business values:
- Cost efficiency
- Productivity
- Developer experience
- User experience
- Business growth
The report provides guidance on how to measure these areas and combine them into an ROI calculation. It also includes an online calculator where you can check your own numbers.
🔸 AI adoption takes time. According to the report, the average adoption journey takes around 8 months, in large enterprise rollout can take even 12–18 months.
Overall, the report is great: no hype, just a practical and rational approach.
It also strongly aligns with my own view of AI adoption. You can't simply buy AI licenses for developers and expect the investment to pay for itself.
Successful AI adoption requires improvements across the entire SDLC: better development processes, a stronger engineering culture, higher test coverage, and investments in guardrails and workflows that keep software delivery stable and predictable.
#ai #engineering #ai4sdlc
One of the biggest questions around AI adoption is what business value does it actually bring?
I'm currently introducing AI into the SDLC across my teams, so measuring its real impact is something I'm really interested in.
Back in February 2026, DORA published a dedicated report on this topic: ROI of AI-assisted Software Development report.
Key takeaways:
🔸 Measure your baseline first. Before evaluating the impact of AI, you need to understand where you're now. Without "before" metrics, it's impossible to measure "after".
🔸 Expect a J-curve. Most organizations experience a temporary productivity drop before seeing benefits. Main reasons for that:
- The learning curve. Time to master new tools and workflows.
- The verification tax. Time to verify AI-generated code and establish trustworthiness of agents output.
- Pipeline adaptation. Scaling other downstream processes like testing and change approval to handle increased number of code changes.
🔸 Invest in engineering practices. The quality of an internal platform, clear workflows, reliable test automation, and strong engineering culture become critical to produce predictable delivery. The rule garbage in -> garbage out is still actual.
🔸 Measure both positive and negative outcomes. Higher throughput is valuable only if it doesn't come with higher instability, more defects, or lower quality.
🔸 Measure the following business values:
- Cost efficiency
- Productivity
- Developer experience
- User experience
- Business growth
The report provides guidance on how to measure these areas and combine them into an ROI calculation. It also includes an online calculator where you can check your own numbers.
🔸 AI adoption takes time. According to the report, the average adoption journey takes around 8 months, in large enterprise rollout can take even 12–18 months.
Overall, the report is great: no hype, just a practical and rational approach.
It also strongly aligns with my own view of AI adoption. You can't simply buy AI licenses for developers and expect the investment to pay for itself.
Successful AI adoption requires improvements across the entire SDLC: better development processes, a stronger engineering culture, higher test coverage, and investments in guardrails and workflows that keep software delivery stable and predictable.
#ai #engineering #ai4sdlc
