Development Effort Is Cut in Half Using AI
During the second quarter of 2026, Smart Coding Lab, a Brazilian consultancy specializing in application development, conducted an exploratory field study to investigate the practical benefits that artificial intelligence can bring to the enterprise application development lifecycle.
The approach consisted of selecting four actual client requirements and developing the corresponding solutions in parallel, with the same functional scope, across two “production lines”: the first used the conventional process, without AI assistance; the second carried out development with the support of AI.
Where permitted, the use of AI covered the entire development lifecycle: from project initiation, through development itself, to the release of the solution into production. AI was also employed in post-production stages, such as stabilization, support, and monitoring.
Consistently, the study demonstrated a reduction of approximately 40% in the total effort required to deliver projects developed with the support of artificial intelligence. This result was driven primarily by the gains achieved during the development phase, which includes activities greatly enhanced by the use of AI, such as coding and testing, where the average reduction in effort reached 50%, rising to an impressive 75% in one of the projects evaluated.
The study also identified five main factors behind the significant reductions in effort during the development phase: accelerated code generation; automation of repetitive tasks; greater standardization and quality of the code produced; automated generation and maintenance of test plans; and reduced technical effort required to deliver the solutions.
The reduction in total effort also translated into shorter delivery timelines, resulting in an average decrease of approximately 18% in project duration. It is worth noting, however, that a reduction in effort does not translate linearly into shorter timelines. This is because the agile methodology adopted in the development process is structured around two- to three-week sprints. Since efficiency gains are distributed across various activities throughout these sprints, they are not always sufficient to eliminate entire weeks from the schedule.
Even when productivity gains did not fully translate into shorter timelines, they continued to generate value through increased delivery capacity, reduced risks, greater predictability and control, and better use of the time of both teams and clients. This improved use of time resulted primarily from less time spent on operational tasks and greater focus on value-adding activities. This transformation also redefined the roles of the professionals involved, increasing the importance of experience, expertise, and judgment in project execution.
Developers began devoting more attention to architecture, assisted implementation, and code quality, and less to manual coding and solving recurring technical problems. Tech Leads, in turn, reduced the time spent on manual code reviews and shifted their focus to AI orchestration, standards definition, and strategic decision-making. The QA team began prioritizing test strategy and test plan development, reducing its involvement in execution and manual validation. Finally, Engagement Managers reduced the effort spent preparing User Stories and communicating status and delivery reports, redirecting their attention toward aligning expectations and generating value for clients.
AI-assisted projects revealed a new and significant bottleneck: client availability. As the different project phases — particularly development — began to be completed more quickly and with less effort, the need for client involvement increased, especially in defining new requirements, conducting functional tests, and providing follow-up after production deployment.
Although the exploratory nature of the study and the limited number of projects do not allow the observed percentages to be generalized to all development contexts, the results provide practical evidence of the gains that can be achieved through the structured adoption of AI. The findings also align with the trend identified by Gartner: in a survey of 598 organizations, 63% reported that they were testing, deploying, or already using AI coding assistants. The consulting firm also predicts that, by 2028, 75% of enterprise software engineers will use these tools, compared with less than 10% at the beginning of 2023.
Smart Coding Lab’s experience helps explain this trend: when incorporated throughout the entire development lifecycle, with appropriate processes, governance, and human involvement, AI ceases to be merely a code-generation tool and becomes an important multiplier of teams’ delivery capacity.