IT professionals should not fear replacing certain AI systems in every stage of development, but many companies are moving toward using artificial intelligence to augment and sometimes replace portions of coding work. A senior executive, influential in the IT field, spoke about this shift and its implications for developers as AI becomes more integrated into software creation.
One clear takeaway is that coding can consume a substantial portion of a developer’s time, often ranging from one-fifth to half of project hours. As AI evolves, it is likely to reclaim more of that time, allowing human engineers to focus on higher-level challenges. The idea is not to strip away human expertise but to reallocate it toward activities where human judgment adds the most value. In theory, an AI system trained on a large corpus of high-quality code could reach levels of sophistication comparable to a senior developer for certain tasks, provided the training data is robust and representative of best practices.
Proponents expect IT professionals to continue embracing AI-driven automation as a way to accelerate workflows and reduce repetitive, error-prone work. This progression may bring faster iteration cycles, enabling teams to experiment and refine architectures with greater agility while maintaining careful oversight from skilled engineers.
As an example of where automation stands today, the use of coding assistants such as GitHub Copilot demonstrates both the potential and the current limitations. These tools can draft substantial portions of code, but they still require human review, testing, and integration to ensure that the resulting software aligns with project goals and system architecture. In practical terms, Copilot or similar assistants may generate a portion of the codebase, while a human programmer validates, tunes, and assembles the components into a coherent whole. The gap between automatic generation and production-grade software remains a key barrier to full autonomy.
From a strategic perspective, programmers will remain essential for dealing with more intricate tasks. They will design original architectures, define the hierarchy of system components, map out how modules interact, and interpret the meaning of each software element within a broader objective. This deeper layer of understanding — grasping how individual pieces contribute to the overall function — empowers teams to build robust, scalable solutions that stand up to real-world demands. Human oversight and creative problem-solving continue to be critical at the highest levels of software engineering.
For those entering the IT workforce, practical guidance from experienced professionals suggests a proactive approach during interviews and career planning. A well-considered set of seven questions can help job seekers demonstrate readiness and alignment with an organization’s needs. These questions can probe project scope, team dynamics, the balance between automation and human contribution, expectations for learning and adaptation, and the organization’s approach to code quality, testing, and lifecycle management. The intent is to reveal how a candidate would contribute to a team that leverages AI while maintaining rigorous standards and a clear sense of ownership over software outcomes.