Shaping the Future of Software Together: Insights from the SITA AI Working Group
The Serbian IT Association (SITA) AI Working Group recently gathered at the Endava office for an open, collaborative exchange focused on Artificial Intelligence in the Software Delivery Lifecycle (SDLC). Led by Borivoje Kovač, Regional AI Evangelist from Endava and coordinator of the working group, the session brought peer companies together to move past marketing hype and discuss how AI is actually touching day-to-day engineering practices, team dynamics, and human roles.
Because the session was a closed, trusted forum meant for honest benchmarking, specific internal details remain private. However, the high-level insights guided by Borivoje during the micro-panel and round table discussions highlight critical shifts relevant to our entire community.
Engineering Reality: An Assistant, Not a Replacement
A central theme from the panel was separating vendor promises from engineering reality. Despite intense hype around fully autonomous AI agents, the room agreed they are not ready to replace human judgment. Instead, AI works best as a disciplined tool under strict human control, where developers act as the essential architects who define intent, set system structure, and carefully review generated code.
Furthermore, AI output quality scales directly with the precision of human guidance. Vague instructions introduce unnecessary risk, making crisp specifications and accountable human code reviews completely non-negotiable to prevent downstream bugs.
Shifting Abstractions and Team Dynamics
The round table discussions offered a grounded perspective on the changing workforce. Rather than eliminating jobs, AI is viewed as part of software history’s long trend toward higher levels of abstraction. The modern engineer’s focus is shifting upward, from manual syntax to system design, orchestration, and business logic validation.
Looking ahead, the group anticipated a shift toward standardized, team-wide AI workflows and smaller, highly adaptive team structures. However, this brings new challenges, such as managing knowledge concentration risks and protecting mentorship paths for junior developers.
Defining the Right Yardstick
The session closed with a clear call to action. The tech sector currently lacks a mature framework to measure AI’s real value, and simplistic metrics like lines of code or raw speed were completely rejected. Moving forward, SITA aims to define a practical evaluation scorecard that accurately balances delivery speed against code quality, long-term costs, and human learning.
SITA extends a huge thank you to Endava for hosting us, and to all participating members for their openness, trust, and commitment to shaping a forward-thinking tech community in Serbia.
