According to a recent report by Gartner, generative AI (GenAI) is set to transform the software engineering landscape, necessitating that up to 80% of the engineering workforce upskill by 2027. This shift will create new roles and redefine existing ones as organizations increasingly rely on AI to enhance their software development processes.
Philip Walsh, senior principal analyst at Gartner, commented on the evolving role of engineers: “Bold claims on the ability of AI have led to speculation that AI could reduce demand for human engineers or even supplant them entirely. While AI will transform the future role of software engineers, human expertise and creativity will always be essential to delivering complex, innovative software.”
Gartner outlines three phases of AI's impact on software engineering:
- Short Term: In the immediate future, AI will augment existing developer work patterns, resulting in modest productivity gains. These benefits will be most pronounced for senior developers working in organizations with established engineering practices.
- Medium Term: As AI agents emerge, they will begin to take over more tasks, marking a transition to AI-native software engineering. In this phase, the majority of code will be AI-generated, requiring developers to adopt an "AI-first" mindset. Walsh noted that this shift will make skills in natural-language prompt engineering and retrieval-augmented generation (RAG) essential for engineers.
- Long Term: Looking ahead, advances in AI will create a strong demand for skilled professionals known as AI engineers. These individuals will need a unique blend of software engineering, data science, and AI/machine learning expertise. Walsh emphasized, “Building AI-empowered software will demand a new breed of software professional, the AI engineer.”
A Gartner survey conducted in late 2023 revealed that 56% of software engineering leaders consider AI/machine learning engineers the most in-demand roles for 2024, highlighting a significant skills gap in applying AI/ML to applications.
To effectively develop AI capabilities, organizations must invest in AI developer platforms, which will facilitate the integration of AI into enterprise solutions at scale. Walsh stated, “This investment will require organizations to upskill data engineering and platform engineering teams to adopt tools and processes that drive continuous integration and development for AI artifacts.” This proactive approach will be crucial for organizations aiming to thrive in the evolving AI landscape.
