The Impact of AI on Skill Development and Career Progression in Software Engineering

This study examines the impact of AI adoption on skill requirements and career trajectories within the software engineering field. Through a mixed-methods survey of41 professionals, the study combines quantitative analysis with qualitative findings to capture both measurable patterns and in-depth insights from professionals from the industry. The quantitative findings show that many agree that AI improves productivity and requires constant skill development. However, findings also point out that there is a notable lack of structured support from organizations for AI training and governance. The qualitative analysis discovered six main themes: (1) Acceleration of Daily Work due to the automation of routine tasks; (2) Shifting Relevance of Technical Skills, with less focus on memorizing syntax and manual tasks; (3) Rise of Meta-Skillslike system design, critical evaluation of AI outputs and collaboration; (4) Gaps in Organizational Support; (5) Tensions Around Career Progression for junior roles; and (6) Polarity of Perceived Risks and Opportunities between optimism for growth and concerns about deskilling and job security. Analyzed within the conceptual framework of the Dynamic Capabilities Framework, Triple-Loop Model and the Skill-Biased Technological Change, the findings imply that individuals adapt rather quickly to AI,while organizations often lag. An additional study finding is that AI tends to reward flexible professionals with strong meta-skills, which may further widen existing skillgaps. The study recommends that professionals focus on improving their meta-skills.They need to keep their technical knowledge current and stay up-to-date on AI. While organizations ought to create AI adoption frameworks, invest in organized skill development, and follow ethical AI practices. It concludes that lasting success will require both individuals and organizations to adapt together to benefit from AI while reducing its risks.