N-iX, a global technology partner specialising in pragmatic AI software engineering, has launched the N-iX Engineering Index, a new research publication series for engineering leaders. The inaugural report, Pragmatic AI Software Engineering: A field guide for engineering leaders, draws on N-iX engineering engagements in 2025 and 2026.
The central finding does not match the prevailing narrative. AI coding tools accelerate the build stage by 85 to 96 percent on piloted tasks, but software release cycles do not move. The constraints that limited release speed before AI tools entered the market are the same constraints limiting it now: the measurement gap that hides whether productivity gains are real, the security review strain that grows with code volume, and the slow human systems around requirements, acceptance, and decision-making. Writing code was the slowest stage of software delivery, but AI changed that. Everything else around engineering still runs at pre-AI speed, which is now the new bottleneck.
The AI coding research category is now crowded, and most of it is still asking whether the tools work. The Index is for organisations that already know the tools work and need to know what to do next. That is the gap the Index is built to address.
A featured case study from a leader in housing management technology, running 150-plus engineers across 300 repositories, shows what disciplined adoption produces at scale. Total annual incidents fell from 47,493 to 31,060, a 34 percent reduction. Team velocity rose 94 percent, and now releases daily.
That last outcome matters most for the CTO conversation in 2026.
"For many years, the development phase was the slowest part of every release. With AI tools, this part is no longer the bottleneck. What is left are the constraints that were always there, but the development bottleneck was hiding them. Engineering leaders now have to solve these constraints. This is what APEX is designed for," said Yaroslav Kisylychka, Head of N-iX GenAI Value Lab.
"We are publishing the operational picture for scaling AI adoption, with evidence gates that the board can audit. The Index will continue to publish what we learn from our engagements, including the questions that are still open," said Valentyn Kropov, Chief Technology Officer, N-iX.
"We started the APEX framework with thirty engineers across four teams. Within three months we were running it across a hundred. The hard problem in AI is not getting a few engineers productive. It is scaling that into a program the whole organisation can absorb. N-iX gave us the blueprint," said Greg Svitak, Chief Software Architect, WorkWave.
The full report is available for download.
About the research
Pragmatic AI Software Engineering: A field guide for engineering leaders draws on N-iX engineering engagements between Q1 2025 and Q2 2026. The research covers thousands of engineers using AI coding workflows across seven structured client programmes. Adoption rate is defined as the share of contracted developers showing at least daily AI activity on the contracted codebase. The report also references independent sources on AI risk practices.
Key data points
85 to 96 percent time savings on piloted tasks across N-iX engineering engagements. Featured case study (housing management technology, 150-plus engineers across 300 repositories): +94 percent team velocity, +34 points test coverage, -34 percent total annual incidents (from 47,493 down to 31,060), -87 percent incident investigation time.
